It looks like when Google Gets into Brands; It does it in a big way – trying to identify all the Brands it can

This patent relates to determining measures of brand penetration over geographic regions. The patent is about a brand penetration determination system and methods to generate indices based on many brand detections from many geo-located images and corresponding locations within various partitioned sub-regions.

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Image content analysis engines were developing and deployed to detect many objects and entities. Data obtained from these engines can get processed for later retrieval and analysis, spanning many applications and carrying a heavy computational load. As such, more technology gets needed to provide valuable data associated with analyzed images and related content while minimizing costs of storing such data, including, for example, the amount of computer memory required to keep such data.

The Advantages of The Brand Penetration Patent

This brand Penetration patent:

1. Determines brand penetration across a geographic area. It includes splitting, by computers, a geographic area into two or more sub-regions
2. Decides from images captured at sites within each sub-region, many detections of a brand within each respective sub-region
3. Generates a brand penetration index for each sub-region by the computers. The brand penetration index is based on the number of detections in the respective sub-region
4. Stores the brand penetration index for each sub-region in memory in association with an indicator of the respective sub-region
5. Includes a geographic sub-region determination system configured to partition a geographic area into two or more sub-regions
6. It contains an image content analysis engine configured to determine, from images captured at sites within each sub-region, many detections of a brand within each respective sub-region

The Brand Penetration Index

The brand penetration index is based on the number of detections in the respective sub-region weighted by a population factor based on a population within the sub-region or a category factor based on a category of goods associated with the brand.

The geographic sub-region determination system can tell, in splitting the geographic area into two or more sub-regions, the number of sub-regions and boundaries of each sub-region to ensure that the population within each sub-region is above a threshold.

The computer also includes tangible, computer-readable media configured to store the brand penetration index for each sub-region associated with an indicator of the respective sub-region.

The operations also include:

• Splitting a geographic area into two or more sub-regions
• Determining from images captured at sites within each sub-region, many detections of a brand within each respective sub-region
• Generating a brand penetration index for each sub-region – based on the number of detections of the brand in the respective sub-region
• Storing the brand penetration index for each sub-region in memory that is associated with an indicator of the respective sub-region
• Deciding on an electronic content item associated with the brand based at least in part on the brand penetration index for a given sub-region of the two or more sub-regions. That electronic content item gets configured for delivery to and displays on an electric device associated with the given sub-region

The Brand Penetration patent is at:

Brand penetration determination system using image semantic content
Inventors: Yan Mayster, Brian Edmond Brewington, and Rick Inoue
Assignee: Google LLC (Mountain View, CA)
US Patent: 11,107,099
Granted: August 31, 2021
ed: July 12, 2019

Abstract

Example embodiments of the disclosed technology install a brand penetration determination system using image semantic content. A geographic sub-region determination system gets configured to partition a geographic area into two or more sub-regions.

An image content analysis engine gets configured to determine, from images captured at sites within each sub-region, many detections of a brand within each respective sub-region.

A brand penetration index generation system becomes configured to generate a brand penetration index for each sub-region based on the number of detections of the brand in the respective sub-region weighted by factors (e.g., population factor, category factor, etc.), which becomes stored in memory with an indicator of each respective sub-region.

In splitting the geographic area into two or more sub-regions, the number and boundaries of sub-regions get determined to ensure that the population within each sub-region is above a threshold.

Google May Use An Image Content Analysis Model to Detect a Brand

How might Google decide whether there is a brand associated with a geographic region?

1. It can use an image content analysis model to determine brand detections from many geo-located images
2. Brand penetration indices and related measures can then become generated based on brand detections and corresponding partitioned locations.
3. A brand penetration determination system can get configured to determine a distribution of two or more discretized sub-regions within a geographic region.
4. An associated measure of brand penetration can get determined. Sub-region numbers and boundaries can become committed to ensure that a population within each sub-region is above a threshold.

Why Population is above Thresholds for Sub-Regions

By ensuring that the population of each sub-region is above that threshold, a computational burden imposed in storing statistically relevant brand indices for each sub-region gets reduced. Besides, by ensuring that only brand indices for sub-regions having a statistically significant number of inhabitants get stored, the disclosed systems and method can provide valuable data associated with an imagery corpus while simultaneously minimizing the costs of holding that data in memory.

This technology can allow a user to make an election when systems, programs, or features described herein may enable the collection of user information. That is specific information about a user’s current location, social network, social actions or activities, profession, a user’s preferences, or other user-specific features. It also covers images from computers associated with a user and controls data indicating whether a user gets sent the content or communications from a server.

Personally Identifiable Information

Besides, specific data may become treated in ways before it becomes stored or used to remove personally identifiable information. For example, a user’s identity may get treated so that no personally identifiable information can get determined for the user, or a user’s geographic location may become generalized where location information gets obtained (such as a city, ZIP code, or state level) so that a particular area of a user cannot get determined.

Thus, the user may control what information gets collected, how that information is used, and what information is provided. In other examples, images and content determined from such photos under the disclosed techniques can become treated to ensure that personally identifiable information such as images of people, street names and numbers of residences, and other personal information gets removed.

Determining A Measure Of Brand Penetration Across A Geographic Region

A computer comprising processors can help install aspects of the technology, including a brand penetration determination system. In general, the brand penetration determination system can get configured to determine a measure of brand penetration across a geographic region. The brand penetration determination system can include:

• • A geographic sub-region determination system
  • An image content analysis engine
  • A brand penetration index generation system

How A Geographic Sub-Region Determination System Works

A geographic sub-region determination system can split a geographic area into sub-regions.

Sub-regions can correspond, for example, to discretized cells within the geographic region that can have predetermined or dynamically determined sizes and boundaries based on desired measures of brand penetration.

The brand penetration determination system can become configured to divide the geographic area into two or more sub-regions, each sub-region boundaries according to particular cell size and a particular geographic partitioning.

For example, partitioning a geographic area into sub-regions could correspond to implementing a grid imposed over the geographic area.

Each cell in the grid can become characterized by a given shape, and these can include a square, rectangle, circle, pentagon, hexagon, or another polygon.

The Dimensions can characterize each grid cell. They can look at a width, length, height, and diameter dimension. They can also have a predetermined or dynamically determined distance. That can become a value measured in meters, kilometers, miles, or another suitable variable. Or it can become a grid of cells corresponding to respective sub-regions that can become uniform in size, while the cells can vary in size in other embodiments.

Configuring the Geographic Sub-Region Determination System

The geographic sub-region determination system can get configured to determine, in splitting the geographic area into two or more sub-regions, the number of sub-regions and the boundaries of each sub-region to ensure that the population within each sub-region is above a threshold.

When sub-region boundaries are uniform in size (e.g., as in the form of a uniform grid of cells) or when they become predetermined in size (e.g., corresponding to predetermined geographic partitions such as those corresponding to zip codes, neighborhood boundaries, town/district boundaries, state boundaries, country boundaries, etc.), the number of sub-regions can get reduced by excluding sub-regions whose population does not exceed the threshold.

The population here can correspond to many people within a given geographic area, such as census data or other predefined databases associated with a geographic area.

In this example, when ensuring that the population within each sub-region is above a threshold, the geographic sub-region determination system can determine the number and boundaries of each sub-region.

Each sub-region has a population defined by a threshold corresponding to a number (x) of people in each sub-region.

Why Have A Geographic Sub-Region Determination System?

Referring to the geographic sub-region determination system, the population described can correspond to a determined number of goods associated with people in a given geographic area, such as many homes, vehicles, businesses, electronic devices, or particular categories or subsets such goods.

When ensuring that the population within each sub-region is above a threshold, the geographic sub-region determination system can determine the number and boundaries of each sub-region such that each sub-region has a people defined by a threshold corresponding to a number (y) of goods (e.g., homes, vehicles, businesses, electronic devices, etc.) in each sub-region.

The population as described here can correspond to a determined number of images obtained for sites within a particular geographic area and many detections of goods, entities, or the like within such images. In this example, when ensuring that the population within each sub-region is above a threshold, the geographic sub-region determination system can determine the number and boundaries of each sub-region such that each sub-region has a people defined by a threshold corresponding to a number (z) of images and detected objects within the photos in each sub-region.

By partitioning a geographic area into sub-regions, the size of sub-regions is dynamically determined based on population density (or corresponding density of brand detections), the likelihood of including cells corresponding to sub-regions having little to no population brand detections gets reduced.

This gets accomplished at least in part by avoiding a scenario in which many sub-regions have associated brand indices but where each sub-region has such a small number of inhabitants that the data from those sub-regions are not statistically relevant.

Similarly, the size of cells within sub-regions with a more significant number of brand detections can become determined to help ensure an appropriate size to maintain distinctions within a distribution level of brand detections.

This can help ensure that meaningful brand penetration measures can get determined for a geographic area. As such, cell size can vary based on different regions. For example, cell size can get smaller in urban locations and progressively larger when transitioning from urban areas into suburban areas and rural areas.

An image content analysis engine can become configured to determine from images captured at sites within each sub-regions and many detections of a brand within each respective sub-region. The images captured at sites can include a substantially extensive collection of photos. The collection of images can have gotten captured at locations within each sub-region by a camera operating at street level.

For example, the camera operating at street level can become mounted on a vehicle and configured to collect images while the car is traversing street locations within a geographic region.

The collection of images can include, for example, many different photographic images and sequence(s) of images from a video. Such photos get geotagged with geographic identifiers descriptive of a geographic location associated with the camera when it captured each image.

The image content analysis engine can store, include or access machine-learned image content analysis models. For example, the image content analysis models can otherwise include various machine-learned models such as neural networks (e.g., feed-forward, recurrent, and convolutional, neural, etc.) or other multi-layer non-linear models regression-based models, or the like.

The Machine-Learned Image Content Analysis Model(s) Can Detect Text and Logos Associated With A Brand

The machine-learned image content analysis model(s) can:

• • • Detect text and logos associated with a brand within each image of a collection of images related to the geographic area
    • Install a text transcription identification and a logo matching title. For example, text transcription and symbols can get compared to text and logo options identified in a predetermined dataset of text transcription and logo options (e.g., names of text and symbols associated with a particular type of label (e.g., terms of vehicle makes))
    • The brand can get associated with a particular category of goods (e.g., vehicles, business entity types, vendor payment types, apparel, shoes, etc.). More particularly, in some examples, the image content analysis engine can get configured to determine each brand detection from the images captured at the one site from an entity storefront appearing within the images.

Brand Detection In Entity Storefront Examples

This section reminded me of storefront Images from Streetview Cameras.

The patent tells us that Brand detection in entity storefront examples can include but are not limited to a brand name associated with the entity itself. It then provides examples such as:

• • • Types of fast food stores
    • Convenience stores
    • Gas stations
    • Grocery stores
    • Pharmacies
    • Other types of business entities.

It can also look for vendor payment types associated with the entity (e.g., detecting names and logos indicating that the entity will accept payment from respective credit card companies or the like).

Other Sources of Brand Detection

Vehicles – the image content analysis engine can determine the brand from the images captured at the sites from a car in the pictures.
BillBoards – the image content analysis engine can determine the brand from the images captured at sites from billboard(s) in the pictures.

Appreciate that the subject systems and methods applied to many other examples of images and brands while remaining within the spirit and scope of the disclosed technology.

Referring to example aspects of an image content analysis engine, the machine-learned image content analysis model can get configured to receive each image in the collection of images as input to the machine-learned image content analysis model. The machine-learned image content analysis model can get configured to generate output data associated with detections of brands appearing within the images in response to the collection of pictures. For example, the machine-learned content analysis model can get configured to generate a bounding box associated with each detected brand. Output data can also include labels associated with each detected brand. Each title provides a semantic tag associated with some aspect of the brand. These can be the brand name and goods associated with a brand.

A vehicle within an image, labels associated with the vehicle could include a vehicle model label (e.g., Camry), a car make the label (e.g., Toyota), a vehicle class label (e.g., sedan), a vehicle color label (e.g., white), or other identifiers.

The output data can also include a confidence score descriptive of a probability that the detected brand is correctly detected within that bounding box. Such output data can become aggregated over the collection of images to determine a count descriptive of the number of detections of the brand within the geographic region or within specific sub-regions.

Detections of Brands in Different Sub-Regions

Advertisements appear in many places, and products often have logos identifying them. This patent tells us that it is actively looking for those.

A brand penetration index generation system can become configured to generate a brand penetration index for each sub-region. The brand penetration index gets based on the number of detections of the brand in the respective sub-region. A count descriptive of the number of detections of the brand can be weighted by factors including but not limited to: a population factor based on a population within the sub-region; a category factor based on a category of goods associated with the brand; a source factor based on several source locations for a brand within a sub-region, such as dealerships or stores, etc.

Brand Capacity and Brand Saturation

Brand capacity is the total number of all brands detected in an area or an unlimited number of possible detections based on population within an area).

Brand saturation is an amount or index of detections of similar brands in the area).

This brand penetration index can become determined as representing brand prominence in a particular category. This means, for example, the number of detections of the vehicle make/model in a category such as sedans, luxury cars, all cars.

Refining Detections in the Brand Penetration Index

The brand penetration index generation system can become configured to refine detections by de-duplicating multiple detections associated with a distinct geographic location.

That refining process can help increase accuracy and usefulness within the disclosed techniques while making the disclosed systems and methods more immune to potential disparities and differences associated with a large imagery corpus for determining brand detections.

For example, refining can help reduce potential bias in some portions of a geographic area being more prominent than others.

Potential Disparities of Brand Detections

Potential disparities of brand detections can arise because of:

• • • Differences in the total number of images available at different locations. Such as images affected by the speed of the operator, vehicle or human, that took the photos
    • Inconsistencies in the times, circumstances, and weather patterns existing when images become taken
    • The visibility viewshed of each brand object

The patent tells us that these problems can become consistently solved by:

• • • Refining brand detection data using knowledge of the operator/vehicle routes
    • When the images got taken
    • Geolocation and pose information from each image
    • The detection box within an image for each detection

Then, it becomes possible to de-duplicate the obtained detections. Each detection box can become associated with a well-defined real-world location, and the number of distinct locations associated with detections within a sub-region can get counted.

Problems with Some Brand Occurrences

Another possible concern about some brand occurrences, especially for vehicles, is that they may not necessarily get associated with the people living in a given geographic area.

This is not expected to become a significant source of error as most travel is local and should dominate the corpus of obtained imagery detections.

But, in some scenarios, it may become desirable to include all detections, whether based on people/homes/vehicles/etc. that are local to the area or simply traveling through it.

Storing The Brand Penetration Index And Tracking Changes Over Time

According to another aspect of the present disclosure, the computer can become configured to store the brand penetration index generated for each sub-region in a regional brand penetration database. The regional brand penetration database can correspond, for example, to a memory including tangible, non-transitory, computer-readable media, or other suitable computer program product(s). The brand penetration index for each sub-region can get stored in the regional brand penetration database associated with an indicator of the respective sub-region. The brand penetration indices for a plurality of sub-regions can become ordered within the brand penetration database. The sub-regions or corresponding indices get stored in a manner indicating the most dominated to least dominated sub-regions (or vice versa) to measures of brand penetration.

The brand penetration index generation system can track how index values stored within the regional brand penetration system database change over time. For example, the brand penetration index generation system can get configured to determine a shift factor indicative of dynamic shifts in each sub-regions brand penetration index over time periods.

The system can get configured to generate flags, notifications, and automated system adjustments when a determined shift factor exceeds predetermined threshold levels. These shift factors and associated notifications can become used to help identify successful brand penetration or areas in which more targeted brand penetration gets desired.

A Targeted Advertisement System Coupled With the Regional Brand Penetration Database

The computer can also include a targeted advertisement system coupled with the regional brand penetration database and an electronic content database.

This targeted advertisement system can become configured to determine an electronic content item from the electronic content database.

This electronic content item can get associated with the brand based at least in part on the brand penetration index for a given sub-region of the two or more sub-regions.

The electronic content item can become configured for delivery to and display on an electric device associated with the given sub-region. For example, an electronic device associated with a sub-region could correspond to an electronic device operating with an IP address or other identifier associated with a physical address located within the sub-region.

In other examples, an electronic device associated with a sub-region could correspond to an electronic device owned by a user living in the sub-region or currently operating in the sub-region. This may become the case with mobile computers or the like.

Serving Brand Content To Users In A Physical Manner

The computer can also include reversing geocoding features that can help determine physical addresses for serving brand content to users in a physical manner instead of an electronic manner.

The brand penetration determination system and targeted advertisement system can more particularly include a reverse geocoding system configured to map cells corresponding to sub-regions within a geographic area to physical addresses within those cells/sub-regions.

The reverse geocoding system can leverage databases and systems that map various geographic coordinates (e.g., latitude and longitude values) associated with cells/sub-regions to physical addresses within or otherwise associated with those areas.

These physical addresses can be stored in memory (e.g., in the regional brand penetration database) and other respective cells/sub-regions indicators and corresponding brand penetration indices.

Measures of brand penetration determined for particular cells/sub-regions can then get used to determine content items. Such as targeted advertisement mailings. These can work for selected delivery to physical addresses within those cells/sub-regions.

By utilizing the disclosed brand penetration index in dynamically determining or adjusting electronic content delivered to users, advertisers can more accurately target products to the most suitable audience.

More particularly, electronic content can become strategically determined for delivery to users within geographic areas having a below-average penetration of a particular brand and above-average penetration of competing brands.

The further analytics implemented by a targeted advertisement system can determine penetration measures of variables, including various threshold levels of brand penetration.

Brand Penetration Tracking

The systems and methods described here may provide many technical effects and benefits. For instance, a computer can include a brand penetration determination system that generates meaningful object detection data associated with a large corpus of collected imagery. More particularly, the detection of brands associated with goods, services, and the like within images can become correlated to statistically relevant measures of brand indices representative of brand capacity, prominence, brand saturation, etc., in a computationally workable manner.

Besides, these measures of brand penetration can become advantageously tracked and aggregated over space. Such as various geographic regions. It can also be tracked over time. These can be various windows–times of day, days of the week, months of the year, etc. They can determine alternative data measures.

A further technical benefit of the disclosed technology concerns integrating a geographic sub-region determination system, which can get configured to determine sub-regions numbers and boundaries within a geographic area to ensure that a population within each sub-region is above a threshold. By ensuring that the population of each sub-region is above that threshold, a computational burden gets imposed in storing statistically relevant brand indices for each sub-region gets reduced.

In addition, by ensuring that only brand indices for sub-regions having a statistically meaningful number of inhabitants get stored. The disclosed systems and methods can provide useful data associated with an imagery corpus while simultaneously minimizing the costs of storing that data in memory as the disclosed technology can achieve such, specific improvements in computing technology.

Integration Within A Targeted Advertisement System

A further technical effect and benefit of the disclosed technology can get realized when the disclosed technology becomes integrated within the application of a targeted advertisement system. Advertisers are commonly faced with determining how to accurately target product(s) and services to the most suitable audience.

This problem can involve many variables, where various demographics, cultural differences, infrastructure, and other considerations come into play. Such technology is used by providing systems and methods for generating computationally efficient and meaningful measures of geographic partitioning and corresponding brand penetration. In combination with other demographic indicators, this can be possible to dynamically develop advertising strategies for targeted delivery of electronic content to consumers.

With reference now to the Figures, example embodiments of the present disclosure will become discussed in further detail.

Brand Penetration Conclusion

The patent provides more details about this tracking of Brands in different geographic areas. It potentially can mean a lot of counting a tracking in the physical world. A log of this can be done through image analysis using programs such as streetview. It’s interesting knowing that Google might have a good idea of where all of the brands might be in the future and know things such as brand capacity and brand saturation in different geographic areas. Will Google know brands this well at some point in the future?

In 2020, I wrote about how Google might track product lines more closely on the Web in the post: Google Product Search and Learning about New Product Lines. There was a point in the past where Google did not seem to pay much attention to brands. With that earlier patent on product lines and this one on brand penetration, that has the potential to change in a big way really fast.

Google is showing us that they are using technologies such as image recognition and streetview cameras to learn more about the world around us. This is also seen in a recent post:

An Image Content Analysis And A Geo-Semantic Index For Recommendations

Detecting Brand Penetration Over Geographic Locations is an original blog post first published on Go Fish Digital.

What is Voice SEO?

Voice SEO is voice-based search engine optimization designed to answer questions in natural human speech, instead of manually-typed queries. Voice search is typically powered by virtual assistants. There are four main voice assistants currently on the market:

1. Google Assistant from Google
2. Alexa from Amazon
3. Siri from Apple
4. Cortana from Microsoft

Apple’s Siri previously used Microsoft’s Bing for voice searches but it recently switched to being powered by Google. However, the popular voice assistant Alexa from Amazon still uses Bing to answer users’ questions.

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How Many Searches Are From Voice?

According to Statista, 31% of US households own smart speakers with virtual assistants. Furthermore, a recent study from Adobe estimates that 48% of users use voice for “general web searches.” This makes sense considering that virtual assistants are now natively integrated with nearly all personal devices, such as smartphones, computers, and tablets.

Currently, about 44% of users use voice search technology every day. These numbers are expected to grow as natural language processing (NLP) technology continues to improve and users become more comfortable interacting with artificial intelligence (AI).

Source: Adobe Voice Technology Study, July 2019 (n=1,000)

Strategies for Optimizing Content for Voice Search

1. Write the Way You Speak

That’s right. Think about answering your question from a user experience (UX) perspective and try to write your content as you would if you were speaking to the user face-to-face. This helps make your content flow as though you were having a meaningful dialogue or conversation with your users in response to their questions.

Furthermore, readability makes your content easier to understand, especially when read out loud by a virtual assistant. A good rule of thumb for readability is to write at a high school or middle school reading level. These parameters translate to the lowest estimated level of standard education needed to understand your writing; they don’t necessarily mean that this demographic is your target audience. A handy tool to help make sure you’re on the right track is the Hemingway App, which can help make your content more accessible to your readers.

2. Answer Questions Succinctly

Short and to the point is the key here. Try answering questions in about 40-50 words maximum.

Formatting your content to answer questions quickly and efficiently also optimizes it for Featured Snippets. Virtual assistants frequently read answers from rich search results like Featured Snippets and Knowledge Panels, since they tend to answer questions briefly.

Optimizing content for informational search intent by using “definitive” phrasing and keywords (for example, using defining “to be” statements, like “Siri is a virtual assistant from Apple Inc.” instead of “‘Virtual Assistants’ can also refer to remotely employed administrative aides”) can help position your content to more directly answer users’ questions. Also, make sure your content is well-structured with semantic HTML and Schema mark-up (JSON-LD) to help search engines understand your page better. This can help reinforce the core content of the target page by providing search engines with supporting contextual signals.

3. Target Long-Tail Queries

We can get a good idea of how users might phrase their voice searches by looking at longer-tailed queries. These typically include inquisitive phrases like “what”, “why”, “which”, and “how” for introducing questions. AnswerThePublic is a great tool for brainstorming questions to answer in your content.

AlsoAsked.com is another free tool you can use to “reverse-engineer” user search intent by visually graphing Google’s “People Also Asked” questions into a hierarchy of related searches from other users. This helps us make sure that we are answering real questions that users are actually searching for.

4. Optimize for Local SEO

Voice search is frequently geographically targeted. For example, users commonly use voice search on their smart devices to ask for local directions or recommendations. Try using phrases like “near” or “in” when optimizing your content for local search.

Source: Adobe Voice Technology Study, July 2019 (n=1,000)

5. Improve Page Speed

Page load times are officially a ranking factor for Google’s mobile-first index. This means that it’s important for pages to load quickly in order for voice search engines to parse the information on-page more efficiently, so they can answer users’ questions faster.

For example, defer pesky render-blocking resources by inlining critical JS and CSS, and then minifying and loading the rest after the core HTML content is rendered in the DOM. Also, try compressing, lazy-loading, and serving images in next-gen formats (like WebP) with descriptive alt texts that are easy for search engines to understand and read to users.

Key Takeaways

Voice search is the future and continues to grow in popularity. This means that digital marketers and content creators need to adapt to the changing search landscape in order to stay ahead of the curve. By following these five simple steps, you’ll already be well on your way to staying top-of-mind and on the tip of the tongue (pun intended) of your target audience.

How to Optimize for Voice Search is an original blog post first published on Go Fish Digital.

Human Friendly Driving Directions with Personalized Landmarks

Back in 2005, I wrote a post about Human Friendly Driving Directions From Google?

A new patent that has come out this spring presents a different approach based upon an awareness of places that you have traveled to in the past, which it could use as personalized landmarks.

Giving Directions and Problems with Directions

Usually, when someone searches using Google Maps, they specify a starting point and a destination, and the mapping program will display directions immediately and/or as the user travels from the starting point and the destination.

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The program indicates such things such as distance, street names, and building numbers, to generate navigation directions based on the route.

Some issues with Driving Directions can include problems such as:

• Drivers may not accurately judge distances
• Seeing street signs and building numbers can be difficult
• There are areas where street and road signage is poor

This patent suggests that to improve driving directions, navigation directions can be augmented with references to points of interest (also referred to herein as “landmarks”) along the route, such as buildings that standout visually or billboards.

And these landmarks can be presented to orient a user within a digital map.

But, the number of landmarks that are well-known to the general public is limited, and software applications may not always be able to present a landmark when orienting or navigating the user. That may mean that Google may instead decide to use personalized landmarks.

Personalized Landmarks

The solution that this patent provides is to include in navigation directions or when orienting a user, personalized map data showing places they know when the user permits the system to use such location data.

That location history can include information about landmarks previously visited by the user, such as:

• The location of the POI or landmark
• The date and time in which the user visited the landmark
• The amount of time spent at the landmark
• A label for the landmark provided by the user such as “Home,” “Work,” “John’s House,” “Favorite Restaurant,”
• Etc.

A personalized landmark may be selected from the user’s location history that is:

• Near the selected location
• Based on the frequency and/or recency with which the user visited the landmark

Google may keep track of how often you visit places, and how much time you spend at them, as seen in this chart from the patent drawings:

Then the mapping application will present a selected landmark on a map display, to give a user a frame of reference for the chosen location, and a final destination in comparison to it.

In addition to seeing a personalized landmark in a set of directions, someone using this navigation system may see it in directions from a starting location such as where they are at present.

And they might see in their directions one that could be something like, “Turn right at the intersection after passing Bob’s House”

Boosting Rankings of POIs Used as Personalized Landmarks?

The place that Google might choose as a landmark for you may be boosted in the rankings you see based on how often and how recently you have visited it.

Google may also include in the display map, information such as “This is a place you visit frequently” or show information about the last time you visited, as an annotation, like in this patent drawing:

In addition to seeing places annotated in a map display, you may also see annotations in SERPs for those maps results

Personalized Landmarks Shown as Intermediate Destinations

Google may show a familiar location as an intermediate destination on your way to a final destination, like this place listed with an annotation as a place you visit frequently (“You come here often.”) on a map:

That location may not be in a direct route to a final destination but would be a landmark which the user is very familiar with or may have traveled to many times in the past.

The patent says that doing this can significantly reduce the number of navigation instructions in might need to provide (and may only provide sparse navigation directions until a user gets to that intermediate destination.) They may give more detailed directions after you pass your personal landmark, and noted in this drawing from the patent:

This personalized landmarks patent can be found at:

Displaying Personalized Landmarks in a Mapping Application
Inventors: Haroon Baig, Ankit Gupta
Applicants: GOOGLE LLC
Publication Number WO/2020/050842
Publication Date March 12, 2020
International Filing Date June 9, 2018

Abstract

To provide personalized data for display on a map, a server device obtains location data for a user and identifies locations that are familiar to the user based on the frequency and recency in which the user visits the locations. The server device then provides the familiar locations in search results/suggestions and annotates the familiar locations with a description of a relationship between the familiar location and the user. The server device also includes the familiar locations as landmarks for performing maneuvers in a set of navigation instructions. Furthermore, the server device provides a familiar location as a frame of reference on a map display when a user selects another location near the familiar location. Moreover, the server device includes a familiar location as an intermediate destination when the user request navigation directions to a final destination.

Human-Friendly Driving Directions Based Upon Personalized Landmarks is an original blog post first published on Go Fish Digital.

Google made an announcement recently about changes to local search. Those are detailed in: Google Changes How Local Search Results Are Generated. It tells us that they are “applying neural matching to local search results.”

A new patent has also come out from Google about changes to Google’s navigation system. It looks like it will provide better results by adding intermediate destinations to journeys and better searching on those trips.

Together, these changes to local search and Google’s navigation system could have an impact on local businesses. We have seen technology make changes to yellow pages, paper roadmaps, and transportation services. Changing local search and navigation can have a tremendous impact. I’ve detailed a lot of the changes that the new navigation patent hints at. There is no timeline on when these things might go into effect.

The patent that Google was granted is about navigation systems and better search results while searchers operate a navigation system, such as Google Maps. This post ends with three detailed examples that show off how much different the navigation system described within this patent might be.

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An inventor from this patent has a background that feels appropriate. The LinkedIn profile for Mark Hanson, and the types of jobs he worked on while at Google make him feel like an ideal fit for this patent:

• Senior Software Engineer
• Company Name: Google
• Dates Employed: Dec 2011 – Present
• Employment Duration: 8 yrs 2 mos
• Location: Sydney, Australia
• Currently Tech Lead / Engineering Manager on Google Maps for Android Automotive

• Previously:
• Google Maps APIs (Tech Lead / Manager)
• Google Maps for the Mobile Web
• Google Maps Coordinatev
• Google Maps Business Database

This patent feels like it was created by someone concerned about making navigation from within a car feel like a good user experience.

It starts by telling us this about navigation systems:

Navigation systems are known for identifying and displaying a desired geographic location, as illustrated on a map, as well as computing a route from a current location to the desired location. These systems are commonly found on automotive vehicles as well as encompassed within hand-held devices.

The Navigation System Problem this patent tries to solve

The focus here is on better search results that are more efficient, and easier to refine.

These need improvement according to the patent, which tells us about it like this:

It is often the case that navigation systems provide information relating to points of interest (POI), such as shopping, food, and business-related locations. However, voluminous search query results are typically generated, which is inefficient and often ineffective for providing information relating to POIs.

It would be advantageous for a navigation system to efficiently provide enhanced search query results for more effective searching. It would be further advantageous for a navigation system to provide sub-searching capabilities for refining search results and therefore providing more effective searching.

Better Car Navigation

What is being invented is a navigation system that includes a graphical user interface capable of receiving input, and displaying content, with a database that contains travel-related data, and includes a processor that can execute searches to respond to search queries.

The input that this navigation system receives is a destination location. With that information, it can perform a search based on that location. It can identify with the travel-related data in the database, search results based on the query, and categories associated with the results.

In addition to a location, this system can receive a keyword, and associate the keyword with one of the search categories, and provide a refined list of results according to the search categories. Those results can be displayed on the graphical user interface.

A search with this navigation system can perform can use several keywords, without first receiving final location destination information. In response to that query, using the categories, the system can display intermediate destinations that can be selected from.

This patent can be found at:

Navigation system and methods for generating enhanced search results
Inventors: Francis Bourque, Sanjay Gupta, and Mark Hansen
Assignee: GOOGLE TECHNOLOGY HOLDINGS LLC
US Patent: 10,527,442
Granted: January 7, 2020
Filed: May 22, 2015

Abstract

A navigation system and various methods of using the system are described herein. Search query results are refined by the system and are prioritized based at least in part upon sub-search categories selected during the searching process. Sub-searches can be represented by graphical icons displayed on the user interface.

The Geographic Database Behind the Navigation System

The database is one with information about geographical roadways and routes.

The patent tells us that there are “a variety of commercially available databases containing map and atlas related information” which are suitable to use with this process.

That database would also contain information that:

• • Is relevant to the user
  • Has previously performed search queries

The patent also tells us that it can use other information like sociological data such as:

• • • Neighborhood crime rates
    • Ethnicity demographics
    • Average household incomes

And industrial data such as:

• • • Type of businesses
    • Shopping related data
    • Potentially hazardous industrial locations

It may also contain sociological and industrial profiles for neighborhoods or geographic regions.

The search engine can perform reverse address searches to identify businesses and other points of interest (POI) within proximity of the current location.

It can also identify a current location by the POI visited.

If a searcher adds information about a point of interest location, that information in the database is updated

The database can also include information relating to previous travel and other behavior selections and circumstances encountered by one or more users in the past.

These can include:

• • • Geographic travel related historical data
    • Contextual travel related historical data

The contextual travel related data includes:

• • • Frequency of destination visits
    • Search query time
    • Time-of-day associated with previous search queries
    • Current directional travel
    • Weather conditions
    • Traffic conditions
    • Current time of day
    • Frequency of current route

The historical database can include information about:

• • • Previous search queries
    • Prior travel routes
    • Prior locations visited
    • Type of prior location visited
    • Deviation distance from route for previously chosen locations

Other kinds of information can include things such as:

• • • Operating hours of a previously visited business is stored in the database
    • Proximnity to locations may play a role in how they may be ranked

The patent tells us about the ability to plan for intermediate destinations on a trip to a final destination. It references being about to identify parking locations near other destinations, which would be helpful in a car navigation system. It also refers to an ability to search for restaurants within a ten-mile radius.

Better Searches in this Navigation System

I was looking forward to seeing what this patent meant by discussing more refined search queries. Here is an example of what the search described in the patent tells us it might look at:

The final destination route can be analyzed based upon contextual information, such as the approximate distance or estimated length of time and type of driving (By example, Interstate, County Highway, or City Roadway). Query results are prioritized based at least in part upon the routing context and sub-search categories selected. For example, a longer route on the Interstate can prioritize fast-food restaurants located proximal to the Interstate higher than a five-star restaurant located distal to the Interstate. If a lodging sub-search category was selected, then the results would be further prioritized based upon lodging proximity and predetermined lodging specifics.

Including Intermediary Destinations in Navigation

An intermediary destination is one that is on the way to a final destination. It can be added to a final destination, such as a gas station, a restaurant, or some other place that might be stopped at on the way to a place. The route in the navigatin system might be updated to include intermediate destinations. Additional information may also be looked at such as:

• • • • • Traffic information
        • Road construction
        • Preferred routes
        • Alternative roadway information
        • Alternative methods of transportation, such as walking or bicycle travel

Having Intermediary Destinations Being Found and Offered Along a Journey

A search to a final destination may be requested, and that may result in a display of intermediate destinations along the journey to that final destination

A list of Places of Interest may be provided based in part upon:

• • • • • Keywords selected
        • Proximity metrics to a final destination
        • Structure associated with the databases

This patent offers three examples of how search queries can influence navigation routes (I am going to quote them directly.) These illustrate how much is being added by this patent to the experience that you may have today using Google Maps.

Search Query Example 1

At approximately 6:00 A.M. on a Wednesday during the winter a user enters a vehicle in downtown Chicago, Ill. and enters a search query including the keyword “Fast food” and selects an option for proximity to a final destination, which is identified as “work”.

The search engine identifies the search query and compares the query to historical query information contained within one or more databases. After the comparison is complete the search engine generates a prioritized list of fast-food destinations within proximity to the user’s preferred route to work.

“Dunkin Donuts” is at the top of the list, followed by several other fast-food restaurants, as well as additional Dunkin Donuts locations.

The user presses the sub-searching icon that represents available parking near the query results. A new list is generated that refines the list based upon available parking.

The search engine also identified the user’s contextual information, including location upon query initiation, time of day, day of the week, and season.

After comparing the database with the user’s query and contextual information, it was identified that the user had traveled to Dunkin Donuts 12 previous times during the workweek, at approximately 6:00 A.M., during the winter while on his way to work after initiating the same or similar query. The user had previously traveled to “Starbucks” only 3 times, and therefore this location had been assigned a lower priority value.

In the immediate case, a Dunkin Donut location without available parking was placed towards the bottom of the list, even though it was closer to the desired route. The user selects a Dunkin Donut location that is closest to the desired route and that has convenient available parking in close proximity.

Search Query Example 2

A search is initiated containing the keywords “Restaurants in Chicago.”

After initiating the query a database is accessed and a comparison of the keywords is performed concerning information contained within a database having historical travel data. A list of restaurants in Chicago is provided ranked by the frequency of visits to a particular restaurant, the day of week and time of day associated with the current query, and prior visits to the results.

Traffic Data can be collected based upon the time of day and day of the week to minimize the travel time to a location, and the list can be prioritized based upon the estimated travel time. A combination of historical, contextual, and traffic information relating to a particular search query can provide a list of enhanced search results.

The search results represent a list of potential intermediary destinations. The user selects an arts and entertainment icon to further refine the list.

In the present case, performing arts and entertainment venues near the restaurants in the search list are provided. A user is then able to select a performing arts venue that is conveniently located close to the Chicago restaurant they desire. It is conceived that an initial and final destination is the same, while the intermediary destination is a target destination within a round-trip travel sequence.

Search Query Example 3

A search query is initiated for a brand of navigation systems, such as “Motorola.”

The search is performed and a generated list of possible businesses offering Motorola.RTM. navigation systems for sale are provided. The businesses can include electronic stores, department stores, travel-specific businesses, and alternative locations that carry Motorola.RTM. products.

Businesses are prioritized based upon the frequency of visit, location proximity, and other contextual and historical information.

Searching for a preferred brand, such as Motorola.RTM. navigation systems will return businesses that are associated with selling and servicing the preferred product brand.

A user can further refine the search results based upon the need to obtain fuel for their vehicle. A fuel source icon is selected, which focuses the results list based upon fuel sources that are near the user’s current location and that provide the most efficient route to the search results on the list.

A New Navigation System Hinted at Google Maps is an original blog post first published on Go Fish Digital.

Local directories can provide very detailed information about businesses too, such as the address, operating hours, history, related businesses, and more.

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But not every business at every location has detailed information about it online, or could be biased, or outdated, or missing information that you would like to know more about.

Mobile Location History and Quality Visit Scores

A newly granted Google patent tells us about how the search engine might respond to “a query associated with a physical location using a quality visit measure. That quality visit measure may be based in part on “the number and/or frequency of repeat visits by one or more individuals to that physical location.”

We know that Google is tracking mobile location history for people using Google Maps to navigate to places, and who have location tracking enabled on their smartphones.

There have been a number of other patents from Google that use information from such actual visits to places to impact search recommendations and results:

• Google to Use Distance from Mobile Location History for Ranking in Local Search
• Google Knows How Busy a Place is by Looking at Location History
• Future Directions for Google Maps?
• Ranking Local Businesses Based Upon Quality Measures including Travel Time
• Google May Check to See if People Go to Geographic Locations Google May Recommend
• Google May Diminish Reviews of Places You Stop Visiting

I have seen Google refer to quality scores for sponsored search results and Google patents that refer to quality scores for organic search results, but I haven’t seen Google refer to quality visit scores until now about Google’s local search results.

The summary for this new patent tells us more about these quality visit scores:

In this manner, data related to visits by one or more individuals to a physical location may be used as an indication of the popularity of that location, with the incidences of repeat visits used to effectively incorporate the “quality” of visits into the popularity indication.

So Google is tracking quality visits to physical locations of stores, to capture the popularity of those locations, based upon repeat visits to them. They are claiming that the benefits of them using the process described in this patent result in:

For example, in some implementations the determination of such data and/or a quality visit measure may improve the accuracy of the information that is identified as relevant to the query and/or provide information with an appropriate prominence to increase the likelihood that the information will be consumed in response to the query. Doing so may also decrease the likelihood that further searches will be needed to identify information, thereby preventing consumption of network and computer resources in response to such subsequent, and otherwise unnecessary, queries.

This Quality Visit Scores Patent can be found at:

Quality visit measure for controlling computer response to query associated with physical location
Inventors: Krzysztof Duleba
Assignee: Google LLC
US Patent: 10,366,422
Granted: July 30, 2019
Filed: September 9, 2015

Abstract

The response of a computer to a query associated with a physical location may be controlled using a quality visit measure that is based at least in part on the number and/or frequency of repeat visits by one or more individuals to that physical location.

Quality Visit Scores Takeaways

A quality visit might be seen as a ranking signal because actual visits to a place can show that people felt that a place was worth returning to.

The length of a visit would be appropriate for the type of physical location visited. For example, a visit to a full-service restaurant for a period of fewer than 5 minutes, or not enough time to order and consume a meal, might not be sufficient to be seen as a quality visit. Five minutes for a dry cleaner or a takeout restaurant would be sufficient to indicate an actual visit to a place.

Other information than just a visit might be considered, such as:

• Check-ins on a social media service
• Geotagged pictures or videos
• Navigation requests
• Etc.

Quality Visit Scores could be used to “rank a physical location higher in a list of search results.”

So, for someone searching for a restaurant in a certain area, a restaurant with a relatively high-quality visit measure may be promoted over other restaurants in the same area due to the frequency of revisits by other individuals.

The patent includes the possibility of a process that may involve structured queries, such as “a user could search only for restaurants with many ‘regulars’ or repeat customers determined to have visited N or more times.”

Quality Visit Scores Information could be potentially provided to the proprietor of a physical location, such as “36% customers have visited 5+ times, 24% of customers have visited 2-4 times.”

There are higher-quality visits, such as someone returning to a place, and bringing a friend, and even that friend returning for a visit, too.

Also “recommended visit measures” where someone receives a recommendation from a friend for a place that they visit on social media, with a certain timeframe.

Location information could be from a navigation app, but could also be based upon GPS, or a geotag in a photo or video, or a wifi connection or cell tower triangulation., or a phone call from a location or an email from a location, or check-in on a social application. These are indications of the physical presence of a person at a physical location associated with a business.

These quality visit scores could have confidence measures attached to them that indicate the likelihood that a person was at the physical location of a business.

In 2011 I wrote about a patent from Apple which used location information from Mobile devices to help rank places in an apple local search. The post I wrote about that is was Crowdsourcing Behind New Apple Local Search Patent.

Basing rankings of businesses in local search on quality visit scores of customers of a business could be part of the future of local search at Google. Google has likely used location information to provide traffic information for Google Maps information since at least 2006, which I wrote about in Ending Gridlock with Google Driving Assistance (Zipdash Re-Emerges). They’ve been working on location history information for a while now. Will they use it to influence rankings?

Added: August 2, 2019, I was pointed to a beta feature at GA360 on Twitter today:

Presently in GA360 there is a beta feature to enable store visit tracking and relate it with channel data. Google can easily use this as a signal for ranking.

— Christy Kunjumon (@christykunjumon) August 2, 2019

Mentioned August of last year: Introducing new local marketing innovations for advertisers, where we are told:

Store visits in Google Ads and Google Analytics are estimates based on data from users that have turned on Location History. Only aggregated and anonymized data is reported to advertisers, and they aren’t able to see any store visits from individual website visits, ad clicks, viewable impressions, or people. Google uses industry best practices to ensure the privacy of individual users.

Added August 3, 2019. I was also asked about a new local Favorite feature, which will highlight the top 5% of businesses in categories at a location, which could tie into this approach as well:

I feel like this may tie into the “Local Favorite” designation coming in GMB, which will highlight the top 5% of businesses in a category. Curious if you think there might be a connection there @bill_slawski

— Etela Ivkovic (@Etela) August 3, 2019

The Google Blog post is from June 30, 2019, titled Helping businesses capture their identity with Google My Business. It tells us:

Finally, we want to recognize those businesses that consistently deliver a great experience for people. We’ll be highlighting the top five percent of businesses in a particular category with the “Local Favorite” designation. To help people easily find and engage with these businesses, we’re also creating digital and physical badges of honors. Stay tuned for more details on these recognition categories coming later this summer.

Since this is towards the end of the summer, we should be hearing more about local favorites at Google soon.

Quality Visit Scores to Businesses May Influence Rankings in Google Local Search is an original blog post first published on Go Fish Digital.

A newly granted Google patent starts by telling us that some searchers have problems creating helpful search queries, and looking through search results generated by queries.

In response to those problems, this patent works to suggest questions to initiate a search. It can use contextual questions based on information about a searcher such as their location or their interests. A searcher can select a question that is provided and the search engine will show answers in response to that question. The selection of answers may be used to identify additional questions related to the selected answers. This can seem like a seemingly infinite series of questions and answers if you continue to look at the answers for questions provided. The questions that are being asked allow for people to make selections, and even submit their questions.

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We’ve been seeing related questions at Google which are inserted into search results, and I’ve written about those. These contextual questions seem to be a little different – and we will explore how. This patent was just granted, and I haven’t seen any questions quite like the ones described in this patent yet, but we could – they aren’t that different from the related questions that we see in many Google SERPs.

The patent provides some examples of such questions and answers based upon location

…the application can initially display an interface including relevant questions to the user based on the location of the user. For example, the user is located in downtown New York City, and the interface can display popular questions for New York City, such as “What are the best sites for New York City” and “What are the best shows to see in New York City.” The user can select the question “What are the best sites for New York City,” and in turn, the interface is updated to display relevant answers to the question, such as “Empire State Building” and “Times Square.” The user can subsequently select “Empire State Building” and the interface is further updated to include additional questions based on the answer “Empire State Building” such as “How tall is the Empire State Building” and “How old is the Empire State Building.” The user can select the question “How old is the Empire State Building” and the interface is further updated to include an answer “84 years.” Upon selection of the answer “84 years” by the user, it is determined that there are no further questions associated with the answer “84 years.” However, the interface can be updated for the input of an additional question provided by the user for association with the answer “84 years.”

The last time I wrote about questions asked and answered by Google, was in the post: Related Questions now use a Question Graph and are Joined by ‘People Also Search For’ Refinements

The point of this patent is that people don’t always quite know the right things to ask for when they might want information about a topic – so Google may show questions that may match what they might want to know more about.

The patent, like many, shows off the benefits of the process behind asking these related questions:

• Receiving data identifying one or more contexts
• Selecting an initial set of one or more questions based at least on one or more of the contexts
• Providing a respective representation of one or more of the questions of the initial set, for output
• Receiving data indicating a selection of a particular representation associated with a particular question of the initial set of questions
• Selecting a set of one or more answers associated with the particular question
• Providing a respective representation of one or more of the answers of the set that are associated with the particular question, for output
• Receiving data indicating a selection of a particular representation associated with a particular answer of the set that is associated with the particular question
• Determining that an additional set of one or more other questions is associated with the particular answer
• And in response to determining that the additional set of one or more other questions is associated with the particular answer, providing a respective representation of one or more of the other questions of the additional set, for output

A New Direction for Contextual Questions?

This patent isn’t referring to these questions as related questions, and the team of inventors that worked on the earlier versions of patents involving related questions has no overlap with this one – there are none of the same inventors listed in those patents and this one. It’s as if two different teams both took attempts at addressing asking and answering questions in search results. So What differences are there between those patents and this one?

1. The patent granted this week on Contextual Questions was the latest filed patent on questions appearing in SERPs.
2. This patent uses contextual questions, asking about things related to a searcher’s location or a searcher’s interests.

The earlier versions of patents involving questions (and there were two of them) focus upon the topics in those questions, and how they might be related to each other, but did not look at contextual information (such as location and interests), like this new patent on contextual questions – that seems to be the difference between them. I started looking through the new patent for information about Contexts, and found this in the summary section:

The one or more contexts include a location-based context of a mobile computing device providing the data identifying the one or more contexts. One or more contexts include an interest-based context of a user associated with a mobile computing device providing the data identifying the one or more contexts. The one or more of the questions of the initial set are ranked based on a popularity of each of the questions. The one or more of the answers of the set are ranked based on a popularity of each of the answers. Providing the respective representation of one or more of the answers of the set that are associated with the particular question, for output, further includes: providing, for output, a control for submission of a new answer, receiving the new answer entered through the control, and storing data associating the new answer with the particular question.

This screenshot image of a map and contextual questions from the patent show us how different it might be from the related questions that we know about:

This new Contextual Question patent can be found at:

Question and answer interface based on contextual information
Inventors: Weizhao Wang, Monica Priya Garde, Justin Min, Jiarui Li, Eyal Segalis, Daniel Walevski, Yaniv Leviathan, and Matthew Streit Coursen
US Patent: 10,289,729
Granted: May 14, 2019
Filed: March 17, 2016

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving data identifying contexts; selecting an initial set of questions based at least on the contexts; providing a respective representation of the questions of the initial set, for output; receiving data indicating a selection of a particular representation associated with a particular question of the initial set of questions; selecting a set of answers associated with the particular question; providing a respective representation of the answers of the set that are associated with the particular question, for output; receiving data indicating a selection of a particular representation associated with a particular answer of the set that are associated with the particular question; determining that an additional set of other questions is associated with the particular answer; and in response to the determining, providing a respective representation of the other questions of the additional set, for output.

The detailed description of the patent starts by telling us about a typical presentation page for questions, as seen in this post above. It tells us that the presentation page includes a map region and a question region. The map region may be associated with the location of a mobile device where a question may be asked. The questions may contain information associated with the current location as well.

Where do Contextual Questions Come From?

They are likely associated with “a location-based context of the user.”

They may be previously provided queries from the current location of the user by other users.

Questions can also be associated with “an interest-based context of the user.”

Those interests can be determined by “explicit interests indicated by the user” or from sources such as social networking profiles and might include such things as eating preferences, price preferences, and so on.

Examples of Contextual Questions

In addition to answering questions such as “Are there any good restaurants around here?”. Followup questions may show menu items from specific restaurants that may be selected, and ask for opinions on specific dishes from those restaurants.

This seems to be a way to help a searcher become familiar with information about the area they may find themselves in. It looks like it could be something that I would find useful if I was visiting someplace that I hadn’t been to before, and wanted to find out about nearby hotels, restaurants, stores, and clubs.

The patent allows searchers to vote on questions and answers such as “What are the best Fast Food places nearby?” So these contextual queries enable Google to crowdsource information about locations from searchers. Being shown as a potential choice for a question such as “What is the best place to get food around here?” could lead to more business for restaurants listed, and nothing in the patent suggests how to be selected as a potential answer to a question like that. But it’s possible that as Google learns about an area they may use information from sources such as submissions to Google My Business, from listings in local directories and data aggregators and enterprise websites, and from questions to local guides who answer questions about places for Google.

Different from Google Local Guide Questions

These aren’t today’s related questions, but this kind of contextual question seems to be the kind of thing I could see Google offering. As a Google Local Guide, Google asks a lot of questions about places that I may have visited in the past. These contextual questions would be a good addition to those. The local guide questions are a little different, such as whether there are ATMs at certain locations, and if handicap parking spaces are available, or parking overall. Or if certain items are available at stores or restaurants. Or if children are welcome at certain locations.

A New Type of Google Contextual Questions is an original blog post first published on Go Fish Digital.