A9.com, Inc.

Shruti Sheorey

Sunil Ramesh

Colin Jon Taylor

William Brendel

Douglas Ryan Gray

Xiaofan Lin

David Creighton Mott

Edward Hsiao

Mark Scott Waldo

Mark Jay Nitzberg

Nityananda Jayadevaprakash

Adam Neustein

Sean Niu

Zur Nehushtan

Pinkee Rasik Patel Gupta

Christopher Joseph Hawley

Daniel Luke Schiffman

Techniques for providing recommended keywords in response to an image-based query are disclosed herein. In particular, various embodiments utilize an image matching service to identify recommended search keywords associated with image data received from a user. The search keywords can be used to perform a keyword search to identify content associated with an image input that may be relevant. For example, an image search query can be received from a user. The image search query may result in multiple different types of content that are associated with the image. The system may present keywords associated with matching images to allow a user to further refine their search and/or find other related products that may not match with the particular image. This enables users to quickly refine a search using keywords that may be difficult to identify otherwise and to find the most relevant content for the user.

Methods to present search keywords for image-based queries

Because not all companies register or obtain barcodes (or other encodings or identifiers) from a single, consistent source, and because different companies can be associated with the same barcode at different times, there can be multiple products associated with a specific barcode. If a user requests information associated with a specific barcode, information associated with the user or request can be used to attempt to determine the associated product if more than one matching product is uncovered. In some embodiments, a location of a user device at approximately a time of the request can be used to attempt to determine the appropriate product, such as if the user is in a store that only offers one of the potential products. Other information can be used as well, such as past locations, purchase history, viewing patterns, and the like.

Barcode disambiguation

Various embodiments relating to predictive analytics from visual data are described. For example, a system associated with a matching service can obtain visual data that includes content related to a marketing campaign associated with a particular product or service. The system can determine image information that contains one or more objects relating to a logo for the particular brand or product. The system can process the content item using one or more machine vision algorithms. The system can determine usage data associated with the particular product or service based at least in part on the one or more feature descriptors. The system can aggregate the usage data with other usage data relating to different users across different time periods to compile a database of insight information with respect to the particular product or service. The system can determine one or more usage patterns relating to the particular product or service.

Predictive analytics from visual data

Approaches provide for analyzing image data to determine and/or recognize text in the image data. The recognized text can be used to generate a search query that can be automatically submitted to a search engine without having to type the search query to identify a product (or related products) associated with the image. For example, a camera of a computing device can be used to capture a live camera view (or single images) an item. An application executing on the computing device (or at least in communication with the computing device) can analyze the image data of the live camera view to determine a set of keywords (e.g., identified text) based on visual features extracted from the image data. The keywords can be used to query an index of product titles, common search queries, among other indexed data to return a ranked list of search suggestions based on a relevance function. The relevance function can consider the ordering of the keywords to rank search suggestions more highly that contain the keywords having the same word order. Further, the relevance function can consider the confidence of the visual recognition of each keyword, the confidence of each search suggestion, customer impact, as well as other factors to determine the ranking of the search suggestions. The search suggestions can be further refined to ensure search results that the user will be more likely to view and/or purchase.

Visual search suggestions

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Mountain View, CA, United States of America

Shruti Sheorey