Patents by Inventor Nimesh SINHA
Nimesh SINHA has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Publication number: 20230359679Abstract: A system can include one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: receiving, at a search engine, a user search query submitted by a user; generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user; and generating, using the search engine, search results for the user search query based, at least in part, on the simulated query that accounts for the personalization preferences of the user. Other embodiments are disclosed herein.Type: ApplicationFiled: July 17, 2023Publication date: November 9, 2023Applicant: Walmart Apollo, LLCInventors: Hyun Duk Cho, Sushant Kumar, Kannan Achan, Nimesh Sinha, Aysenur Inan
-
Publication number: 20230245196Abstract: A consideration intent system can include a computing device configured to receive an indication of an event occurring from a user device, obtain a set of parameters associated with the event and retrieve a set of item intent values corresponding to the set of items. The computing device is configured to determine a first value based on at least one parameter of the set of parameters and classify the event as one of: (i) low consideration intent and (ii) high consideration intent by inputting the set of item intent values and the first value as features to a machine learning algorithm. The computing device is configured to, based on the classification, identify a set of recommendation models, generate a set of recommended item identifiers by implementing at least one recommendation model of the set of recommendation models, and transmit the set of recommended item identifiers to the user device.Type: ApplicationFiled: January 28, 2022Publication date: August 3, 2023Inventors: Spencer Galbraith, Parth Ramesh Vajge, Sooraj Mangalath Subrahmannian, Divya Chaganti, Yue Xu, Hyun Duk Cho, Sushant Kumar, Kannan Achan, Nimesh Sinha
-
Publication number: 20230245202Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.Type: ApplicationFiled: January 31, 2022Publication date: August 3, 2023Inventors: Nimesh Sinha, Sneha Gupta, Rishi Rajasekaran, Yue Xu, Yokila Arora, Hyun Duk Cho, Sushant Kumar, Kannan Achan
-
Publication number: 20230245203Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.Type: ApplicationFiled: January 31, 2022Publication date: August 3, 2023Inventors: Sneha Gupta, Rishi Rajasekaran, Nimesh Sinha, Yue Xu, Yokila Arora, Hyun Duk Cho, Sushant Kumar, Kannan Achan
-
Patent number: 11704374Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a search engine that includes, or communicates with, a recall personalization model configured to generate personalized recall sets of search results for users; receiving, at the search engine, a search query submitted by a user; generating, using the recall personalization module, a feature vector for the user that includes contextual features associated with the user; generating, using the recall personalization model, a simulated narrowing query that includes the search query submitted by the user and the feature vector; generating, using the search engine, a recall set of search results based, at least in part, on the simulated narrowing query. Other embodiments are disclosed herein.Type: GrantFiled: January 30, 2021Date of Patent: July 18, 2023Assignee: WALMART APOLLO, LLCInventors: Hyun Duk Cho, Sushant Kumar, Kannan Achan, Nimesh Sinha, Aysenur Inan
-
Publication number: 20230196434Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.Type: ApplicationFiled: December 20, 2021Publication date: June 22, 2023Inventors: Luyi Ma, Nimesh Sinha, Parth Ramesh Vajge, Hyun Duk Cho, Sushant Kumar, Kannan Achan
-
Publication number: 20230169565Abstract: A seasonal recommendation system can include a computing device that is configured to receive a request to identify a set of recommendations associated with a season, obtain historical data over a threshold period for a set of product types, and compute a seasonality index score based on the historical data over a target period and the threshold period. The computing device is also configured to select a subset of product types based on the seasonality index score and by applying a theme-aware model to the product types and identify and store a set of items corresponding to at least one product type of the subset of product types. The computing device is configured to, in response to a user navigating to a webpage using a user device, select and display at least one item of the set of items on a user interface of the user device.Type: ApplicationFiled: November 29, 2021Publication date: June 1, 2023Inventors: Luyi Ma, Nimesh Sinha, Parth Ramesh Vajge, Hyun Duk Cho, Sushant Kumar, Kannan Achan
-
Patent number: 11468494Abstract: Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.Type: GrantFiled: November 12, 2020Date of Patent: October 11, 2022Assignee: WALMART APOLLO, LLCInventors: Nimesh Sinha, Luyi Ma, Hyun Duk Cho, Sushant Kumar, Kannan Achan, Rahul Ramkumar
-
Publication number: 20220245209Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a search engine that includes, or communicates with, a recall personalization model configured to generate personalized recall sets of search results for users; receiving, at the search engine, a search query submitted by a user; generating, using the recall personalization module, a feature vector for the user that includes contextual features associated with the user; generating, using the recall personalization model, a simulated narrowing query that includes the search query submitted by the user and the feature vector; generating, using the search engine, a recall set of search results based, at least in part, on the simulated narrowing query. Other embodiments are disclosed herein.Type: ApplicationFiled: January 30, 2021Publication date: August 4, 2022Applicant: Walmart Apollo, LLCInventors: Hyun Duk Cho, Sushant Kumar, Kannan Achan, Nimesh Sinha, Aysenur Inan
-
Publication number: 20220245698Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating one or more attribute affinity scores for one or more attributes associated with an item type category, wherein the one or more attribute affinity scores predict a user's affinity for attribute values associated with the one or more attributes; generating a respective attribute importance score for each of the one or more attributes, the respective attribute importance score predicting a respective importance of each of the one or more attributes to the user; and generating personalized search results that are ordered based, at least in part, on the one or more attribute affinity scores and the respective attribute importance scores. Other embodiments are disclosed herein.Type: ApplicationFiled: January 30, 2021Publication date: August 4, 2022Applicant: Walmart Apollo, LLCInventors: Hyun Duk Cho, Sushant Kumar, Kannan Achan, Nimesh Sinha, Aysenur Inan
-
Publication number: 20220245705Abstract: A method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.Type: ApplicationFiled: January 30, 2021Publication date: August 4, 2022Applicant: Walmart Apollo, LLCInventors: Luyi Ma, Nimesh Sinha, Hyun Duk Cho, Sushant Kumar, Kannan Achan
-
Publication number: 20220148062Abstract: Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.Type: ApplicationFiled: November 12, 2020Publication date: May 12, 2022Inventors: Nimesh SINHA, Luyi MA, Hyun Duk CHO, Sushant KUMAR, Kannan ACHAN, Rahul RAMKUMAR