US 2026/0219899 A1Published application

Generating interface for communication elements

AssigneeWalmart Apollo, LLC

InventorsAbhinav Prakash, Akash Daxeshkumar Patel, Keerthi Gopalakrishnan, Hongyao Huang, Ishan Karanwal, Archana Venkatachalapathy, Yokila Arora, Sushant Kumar, Kannan Achan, Topojoy Biswas

Life of this patent

Estimating the term…

patentgrep

Loading patent…

Abstract

Example implementations relate to communication element selection in a network environment. In an example, a plurality of user features is received and input data derived from the plurality of user features is processed using a window logic and provided to an attention-based machine learning model. A plurality of communication elements is provided to the attention-based machine learning model, which assigns one or more weights to each of the plurality of communication elements for a subsequent time period based on the plurality of user features obtained from a preceding time period. A score for each of the plurality of communication elements based on the one or more weights for each of the plurality of communication elements is calculated and an interface including a communication element of the plurality of communication elements having a highest calculated score is generated.

Claims20

Dependent claims

A system, comprising:

a processor;
and
a non-transitory memory storing instructions that, when executed, cause the processor to:
receive a plurality of user features;
provide input data that is derived from the plurality of user features and processed using a rolling window logic and divided into a plurality of time windows to an attention-based machine learning model;
provide a plurality of communication elements to the attention-based machine learning model, wherein the attention-based machine learning model assigns one or more weights to each of the plurality of communication elements for a subsequent time period based on the plurality of user features obtained from a preceding time period within a time window of the plurality of time windows;
calculate a score for each of the plurality of communication elements based on the one or more weights for each of the plurality of communication elements, wherein each score is indicative of a probability of interaction with a corresponding communication element based on the plurality of user features;
and
generate an interface including a communication element of the plurality of communication elements having a highest calculated score.

6 more elements

A computer-implemented method, comprising:

receiving a plurality of user features;
providing input data that is derived from the plurality of user features and processed using a rolling window logic and divided into a plurality of time windows to an attention-based machine learning model;
providing a plurality of communication elements to the attention-based machine learning model, wherein the attention-based machine learning model assigns one or more weights to each of the plurality of communication elements for a subsequent time period based on the plurality of user features obtained from a preceding time period within a time window of the plurality of time windows;
calculating a score for each of the plurality of communication elements based on the one or more weights for each of the plurality of communication elements, wherein each score is indicative of a probability of interaction with a corresponding communication element based on the plurality of user features;
and
generating an interface including a communication element of the plurality of communication elements having a highest calculated score.

3 more elements

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising:

receiving a plurality of user features;
providing input data that is derived from the plurality of user features and processed using a rolling window logic and divided into a plurality of time windows to an attention-based machine learning model;
providing a plurality of communication elements to the attention-based machine learning model, wherein the attention-based machine learning model assigns one or more weights to each of the plurality of communication elements for a subsequent time period based on the plurality of user features obtained from a preceding time period within a time window of the plurality of time windows;
calculating a score for each of the plurality of communication elements based on the one or more weights for each of the plurality of communication elements, wherein each score is indicative of a probability of interaction with a corresponding communication element based on the plurality of user features;
and
generating an interface including a communication element of the plurality of communication elements having a highest calculated score.

3 more elements

Dependencies are inferred from the claim text.