# US 12,762,181 B2 · Content-first collection generation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,181 B2 - Document ID: US12762181B2 - Status: Granted - Kind: B2 - Application number: 19/043,408 - Filed: 2025-01-31 - Published: 2026-08-06 - Granted: 2026-10-13 - Earliest claimed: 2025-01-31 - Assignees: Pinterest, Inc. - Applicants: Pinterest, Inc. - Inventors: Faye Zhang; Qi Wan - Web page: https://patentgrep.com/patent/US12762181B2 ## CPC - G06F 16/538: Presentation of query results - G06F 16/535: Filtering based on additional data, e.g. user or group profiles - G06F 16/583: using metadata automatically derived from the content - G06F 16/5866: {using information manually generated, e.g. tags, keywords, comments, manually generated location and time information} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, systems, and apparatus, including computer programs encoded on computer storage media, for analyzing content. One of the methods includes obtaining a collection of content items, each content item including an image and associated metadata; generating a collection of attributes for each content item by analyzing the image and the associated metadata; assigning one or more attributes to each content item; generating a plurality of queries from the collection of attributes; and populating a plurality of collections, each collection having a topic corresponding to a query, wherein populating the collections comprises evaluating a relevance of each query-content item pair based on an overlap between attributes associated with the query and attributes assigned to the content item. ## Claims ### Claim 1 A method comprising: obtaining a collection of content items, one or more of the content items including an image and associated metadata; generating a collection of attributes for one or more content items of the collection of content items by analyzing the image and the associated metadata, the generating including extracting attributes from one or more content items using a vision language model trained to generate structured attributes from content items and associated metadata; assigning one or more attributes to one or more content items; generating, using a machine learning model, a plurality of queries as a model output from an input comprising the collection of attributes; and populating a plurality of collections of content items, one or more of the collections having a topic corresponding to one of the generated plurality of queries, wherein populating the collections comprises evaluating a relevance of query-content item pairs based on an overlap between attributes associated with the query and attributes assigned to the content item. ### Claim 2 (depends on 1) The method of claim 1, wherein content matching is used to assign the one or more attributes to the one or more content items, the content matching comprising: learning one or more functions that map content items and attributes to a shared embedding space; and for the one or more content items, identifying relevant attributes for assignment to the content items by computing a similarity between the functions. ### Claim 3 (depends on 2) The method of claim 2, further comprising assigning the one or more attributes using a content matching model, wherein using the content matching model comprises: for the one or more content items: determining, by an image encoder, an image embedding for image content of the content item; determining, by a first text encoder, a first text embedding for textual content of the content item; and generating a combined embedding for the content item by combining the image embedding and the first text embedding; for the collection of attributes, determining, by a second text encoder, a second text embedding for each attribute; computing a similarity measure relating respective content item and attribute pairs; and assigning a particular attribute to a particular content item if the similarity measure satisfies a threshold value. ### Claim 4 (depends on 3) The method of claim 3, wherein training the content matching model comprises: adding a weight value to one or more attributes based on a frequency of occurrence of the attribute in training data. ### Claim 5 (depends on 1) The method of claim 1, wherein generating a query comprises: combining a specified number of attributes from the collection of attributes; and transforming, by a machine learning model, the combination of attributes into one or more queries. ### Claim 6 (depends on 1) The method of claim 1, wherein generating the collection of attributes comprises: providing one or more content items of the collection of content items to the vision-language model that is trained to extract attributes across a specified set of domain specific categories. ### Claim 7 (depends on 6) The method of claim 6, further comprising: filtering the extracted set of attributes before performing content matching, the filtering comprising one or more of frequency filtering or semantic deduplications. ### Claim 8 A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining a collection of content items, one or more of the content items including an image and associated metadata; generating a collection of attributes for one or more content items of the collection of content items by analyzing the image and the associated metadata, the generating including extracting attributes from one or more content items using a vision language model trained to generate structured attributes from content items and associated metadata; assigning one or more attributes to one or more content item; generating, using a machine learning model, a plurality of queries as a model output from an input comprising the collection of attributes; and populating a plurality of collections of content items, one or more of the collections having a topic corresponding to one of the generated plurality of queries, wherein populating the collections comprises evaluating a relevance of each query-content item pairs based on an overlap between attributes associated with the query and attributes assigned to the content item. ### Claim 9 (depends on 8) The system of claim 8, wherein content matching is used to assign the one or more attributes to the one or more content items, the content matching comprising: learning one or more functions that map content items and attributes to a shared embedding space; and for the one or more content items, identifying relevant attributes for assignment to the content items by computing a similarity between the functions. ### Claim 10 (depends on 9) The system of claim 9, further comprising assigning the one or more attributes using a content matching model, wherein using the content matching model comprises: for the one or more content items: determining, by an image encoder, an image embedding for image content of the content item; determining, by a first text encoder, a first text embedding for textual content of the content item; and generating a combined embedding for the content item by combining the image embedding and the first text embedding; for the collection of attributes, determining, by a second text encoder, a second text embedding for each attribute; computing a similarity measure relating respective content item and attribute pairs; and assigning a particular attribute to a particular content item if the similarity measure satisfies a threshold value. ### Claim 11 (depends on 10) The system of claim 10, wherein training the content matching model comprises: adding a weight value to one or more attributes based on a frequency of occurrence of the attribute in training data. ### Claim 12 (depends on 8) The system of claim 8, wherein generating a query comprises: combining a specified number of attributes from the collection of attributes; and transforming, by a machine learning model, the combination of attributes into one or more queries. ### Claim 13 (depends on 8) The system of claim 8, wherein generating the collection of attributes comprises: providing one or more content items of the collection of content items to the vision-language model that is trained to extract attributes across a specified set of domain specific categories. ### Claim 14 (depends on 13) The system of claim 13, further comprising: filtering the extracted set of attributes before performing content matching, the filtering comprising one or more of frequency filtering or semantic deduplications. ### Claim 15 One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: obtaining a collection of content items, one or more of the content items including an image and associated metadata; generating a collection of attributes for one or more content items of the collection of content items by analyzing the image and the associated metadata, the generating including extracting attributes from one or more content items using a vision language model trained to generate structured attributes from content items and associated metadata; assigning one or more attributes to one or more content items; generating, using a machine learning model a plurality of queries as a model output from an input comprising the collection of attributes; and populating a plurality of collections of content items, one or more of the collections having a topic corresponding to one of the generated plurality of queries, wherein populating the collections comprises evaluating a relevance of query-content item pairs based on an overlap between attributes associated with the query and attributes assigned to the content item. ### Claim 16 (depends on 15) The one or more non-transitory computer storage media of claim 15, wherein content matching is used to assign the one or more attributes to the one or more content items, the content matching comprising: learning one or more functions that map content items and attributes to a shared embedding space; and for the one or more content items, identifying relevant attributes for assignment to the content items by computing a similarity between the functions. ### Claim 17 (depends on 16) The one or more non-transitory computer storage media of claim 16, further comprising assigning the one or more attributes using a content matching model, wherein using the content matching model comprises: for the one or more content items: determining, by an image encoder, an image embedding for image content of the content item; determining, by a first text encoder, a first text embedding for textual content of the content item; and generating a combined embedding for the content item by combining the image embedding and the first text embedding; for the collection of attributes, determining, by a second text encoder, a second text embedding for each attribute; computing a similarity measure relating respective content item and attribute pairs; and assigning a particular attribute to a particular content item if the similarity measure satisfies a threshold value. ### Claim 18 (depends on 17) The one or more non-transitory computer storage media of claim 17, wherein training the content matching model comprises: adding a weight value to one or more attributes based on a frequency of occurrence of the attribute in training data. ### Claim 19 (depends on 15) The one or more non-transitory computer storage media of claim 15, wherein generating a query comprises: combining a specified number of attributes from the collection of attributes; and transforming, by a machine learning model, the combination of attributes into one or more queries. ### Claim 20 (depends on 15) The one or more non-transitory computer storage media of claim 15, wherein generating the collection of attributes comprises: providing one or more content items of the collection of content items to the vision-language model that is trained to extract attributes across a specified set of domain specific categories.