# US 2021/0241341 A1 · Query intent mapping > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241341 A1 - Document ID: US20210241341A1 - Status: Published application - Kind: A1 - Application number: 16/777,486 - Filed: 2020-01-30 - Published: 2021-08-05 - Earliest claimed: 2020-01-30 - Assignees: Walmart Apollo, LLC - Applicants: Walmart Apollo, LLC - Inventors: Rahul D. SHARNAGAT; Hrushikesh MOHAPATRA; Sreenivasa Prasad SISTA - Web page: https://patentgrep.com/patent/US20210241341A1 ## CPC - G06Q 30/0627: {by specifying product or service characteristics, e.g. product dimensions} - G06F 16/9536: Search customisation based on social or collaborative filtering - G06Q 30/0603: {Catalogue creation or management} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract An approach is disclosed for mapping an intent of a query. The approach receives query data for one or more queries. The approach determines product type data of the one or more queries. The approach determines attribute data for the determined product type data. The attribute data includes sequence attribute data and sequence value data. The approach retrieves catalog data for one or more items that include product type data that matches the determined product type data. The catalog data includes catalog attribute data and catalog value data. The approach determines engagement data for the one or more received queries. The engagement data includes a number of times customers interacted with a retrieved item for a respective query. The approach determines cumulative engagement data based on the retrieved catalog data and the determined engagement data. The approach generates a probability and an evaluation score based on the cumulative engagement data. ## Claims ### Claim 1 A system comprising: a memory having instructions stored thereon, and a processor configured to read the instructions to: receive query data for one or more queries; determine product type data of the one or more queries; determine attribute data for the determined product type data, the attribute data comprising sequence attribute data and sequence value data; retrieve catalog data for one or more items that include product type data that matches the determined product type data, the catalog data comprising catalog attribute data and catalog value data; determine engagement data for the one or more received queries, the engagement data comprising a number of times customers interacted with a retrieved item for a respective query; determine cumulative engagement data based on the retrieved catalog data and the determined engagement data; and generate a probability and an evaluation score based on the cumulative engagement data. ### Claim 2 (depends on 1) The system of claim 1, wherein the processor is further configured to determine the product type data by tagging a product type for the one or more queries. ### Claim 3 (depends on 1) The system of claim 1, wherein the processor is further configured to determine the attribute data by tagging the one or more queries with a sequence tagger. ### Claim 4 (depends on 1) The system of claim 1, wherein the interaction of customers with a retrieved item comprises a customer viewing the retrieved item on an e-commerce website, the customer purchasing the retrieved item on the e-commerce website, or the customer adding the retrieved item to an online shopping cart. ### Claim 5 (depends on 1) The system of claim 1, wherein the processor is further configured to determine the cumulative engagement data by combining the sequence attribute data and sequence value data with the catalog attribute data and catalog value data, and generating a cumulative engagement count, wherein the sequence attribute data, sequence value data, the catalog attribute data, and catalog value data comprises an evaluation set. ### Claim 6 (depends on 5) The system of claim 5, wherein the cumulative engagement count comprises a number of times customers interacted with a retrieved item that corresponds to the evaluation set. ### Claim 7 (depends on 1) The system of claim 1, wherein the probability indicates a degree in which sequence attribute data and sequence value data correspond to catalog attribute data and catalog value data. ### Claim 8 (depends on 5) The system of claim 5, wherein the processor is further configured to generate the evaluation score for the evaluation set by combining string match level features and the generated probability. ### Claim 9 (depends on 5) The system of claim 5, wherein the processor is further configured to: receive a query input into a search field on an e-commerce website; match the received query to an evaluation set; and provide one or more items that correspond to the evaluation set as search results for the received query. ### Claim 10 A method comprising: receiving query data for one or more queries; determining product type data of the one or more queries; determining attribute data for the determined product type data, the attribute data comprising sequence attribute data and sequence value data; retrieving catalog data for one or more items that include product type data that matches the determined product type data, the catalog data comprising catalog attribute data and catalog value data; determining engagement data for the one or more received queries, the engagement data comprising a number of times customers interacted with a retrieved item for a respective query; determining cumulative engagement data based on the retrieved catalog data and the determined engagement data; and generating a probability and an evaluation score based on the cumulative engagement data. ### Claim 11 (depends on 10) The method of claim 10, wherein determining the product type data comprises tagging a product type for the one or more queries. ### Claim 12 (depends on 10) The method of claim 10, wherein determining the attribute data comprises tagging the one or more queries with a sequence tagger. ### Claim 13 (depends on 10) The method of claim 10, wherein the interaction of customers with a retrieved item comprises a customer viewing the retrieved item on an e-commerce website, the customer purchasing the retrieved item on the e-commerce website, or the customer adding the retrieved item to an online shopping cart. ### Claim 14 (depends on 10) The method of claim 10, wherein determining the cumulative engagement data comprises combining the sequence attribute data and sequence value data with the catalog attribute data and catalog value data, and generating a cumulative engagement count, and wherein the sequence attribute data, sequence value data, the catalog attribute data, and catalog value data comprises an evaluation set. ### Claim 15 (depends on 14) The method of claim 14, wherein the cumulative engagement count comprises a number of times customers interacted with a retrieved item that corresponds to the evaluation set. ### Claim 16 (depends on 14) The method of claim 14, further comprising: receiving a query input into a search field on an e-commerce website; matching the received query to an evaluation set; and providing one or more items that correspond to the evaluation set as search results for the received query. ### Claim 17 A computer program product comprising: a non-transitory computer readable medium having program instructions stored thereon, the program instructions executable by one or more processors, the program instructions comprising: receiving query data for one or more queries; determining product type data of the one or more queries; determining attribute data for the determined product type data, the attribute data comprising sequence attribute data and sequence value data; retrieving catalog data for one or more items that include product type data that matches the determined product type data, the catalog data comprising catalog attribute data and catalog value data; determining engagement data for the one or more received queries, the engagement data comprising a number of times customers interacted with a retrieved item for a respective query; determining cumulative engagement data based on the retrieved catalog data and the determined engagement data; and generating a probability and an evaluation score based on the cumulative engagement data. ### Claim 18 (depends on 17) The computer program product of claim 17, wherein the program instructions further comprise: determining the cumulative engagement data comprises combining the sequence attribute data and sequence value data with the catalog attribute data and catalog value data, and generating a cumulative engagement count, and wherein the sequence attribute data, sequence value data, the catalog attribute data, and catalog value data comprises an evaluation set. ### Claim 19 (depends on 18) The computer program product of claim 18, wherein the cumulative engagement count comprises a number of times customers interacted with a retrieved item that corresponds to the evaluation set. ### Claim 20 (depends on 18) The computer program product of claim 18, wherein the program instructions further comprise: receiving a query input into a search field on an e-commerce website; matching the received query to an evaluation set; and providing one or more items that correspond to the evaluation set as search results for the received query.