US-12619646-B2 - Automating generation of persona classification data to customize integration data into compatible distributed data sources at various networked computing devices

Abstract

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. Further, a computing platform is configured to receive inputs as natural language to facilitate automatic generation and integration to form a modified distributed file responsive to events, or moments, among other things including data relevant to an entity, which may provide a good or service.

Inventors

• Adam Eric Katz
• Jacob Maximillian Miesner

Assignees

• Sightly Enterprises, Inc.

Dates

Publication Date

20260505

Application Date

20240904

Claims (18)

1. 1 . A method comprising: receiving data inputs into a computing platform including a processor and memory that stores at least a portion of executable instructions to generate persona classification data; applying a subset of the data inputs into a context data generator, the subset of the data inputs includes entity data, entity communication link data, and entity profile data; generating persona characterization data to include descriptive data or image data, or both; applying the persona characterization data to a vector database; generating targeting parameters based on data representing distributed computing platforms configured to host distributed files; applying the targeting parameters to a first large language model (“LLM”) to identify distributed computing platform-specific segments as a function of persona characterization data; embedding into a vector database data the descriptive data or image data as vectors to form vectorized data; generating at the large language model an output configured to automatically modify operation of a brand profile generator using the vectorized data; deriving content based on the output including an event composite value; generating automatically integration data to include the image data and text-based data to integrate with at least one of the distributed computing platforms; and causing creative content to align with a targeted distribution source including a social media computing platform.
2. 2 . The method of claim 1 wherein the entity data, the entity communication link data, and the entity profile data further include a name of a good or a service as a brand name, a web page link, and brand profile data.
3. 3 . The method of claim 1 wherein generating the persona characterization data to include the descriptive data or the image data, or both, further comprises: applying the persona characterization data to a persona generator.
4. 4 . The method of claim 3 wherein applying the persona characterization data to the persona generator further comprises: apply the persona characterization data as one or more prompts to a second large language model (“LLM”).
5. 5 . The method of claim 4 wherein the first LLM and the second LLM are the same.
6. 6 . The method of claim 1 wherein generating the persona characterization data to include the image data further comprises: analyzing multiple sources of image data from the data representing the distributed computing platforms configured to host the distributed files to extract persona-related data from the multiple sources of image data.
7. 7 . The method of claim 1 wherein generating the persona characterization data to include the image data further comprises: applying the image data to a computer vision application.
8. 8 . The method of claim 1 wherein the first large language model (“LLM”) is configured to implement retrieval-augmented generation (“RAG”).
9. 9 . A system comprising: a data store configured to receive streams of data via a network into an application computing platform; and a processor configured to execute instructions to implement an application configured to: receive data inputs into a computing platform including a processor and memory that stores at least a portion of executable instructions to generate persona classification data; apply a subset of the data inputs into a context data generator, the subset of the data inputs includes entity data, entity communication link data, and entity profile data; generate persona characterization data to include descriptive data or image data, or both; apply the persona characterization data to a vector database; generate targeting parameters based on data representing distributed computing platforms configured to host distributed files; apply the targeting parameters to a first large language model (“LLM”) to identify distributed computing platform-specific segments as a function of persona characterization data; embed into a vector database data the descriptive data or image data as vectors to form vectorized data; generate at the large language model an output configured to automatically modify operation of a brand profile generator using the vectorized data; derive content based on the output including an event composite value; generate automatically integration data to include the image data and text-based data to integrate with at least one of the distributed computing platforms; and cause creative content to align with a targeted distribution source including a social media computing platform.
10. 10 . The system of claim 9 wherein the entity data, the entity communication link data, and the entity profile data further include a name of a good or a service as a brand name, a web page link, and brand profile data.
11. 11 . The system of claim 9 wherein the processor configured to generate the persona characterization data to include the descriptive data or the image data, or both, is further configured to: apply the persona characterization data to a persona generator.
12. 12 . The system of claim 11 wherein the processor configured to apply the persona characterization data to the persona generator is further configured to: apply the persona characterization data as one or more prompts to a second large language model (“LLM”).
13. 13 . The system of claim 12 wherein the first LLM and the second LLM are the same.
14. 14 . The system of claim 9 wherein the processor configured to generate the persona characterization data to include the image data is further configured to: analyze multiple sources of image data from the data representing the distributed computing platforms configured to host the distributed files to extract persona-related data from the multiple sources of image data.
15. 15 . The system of claim 9 wherein the processor configured to generate the persona characterization data to include the image data is further configured to: apply the image data to a computer vision application.
16. 16 . The method of claim 9 wherein the first large language model (“LLM”) is configured to implement retrieval-augmented generation (“RAG”).
17. 17 . A non-transitory computer readable medium having one or more computer program instructions configured to perform a method, the method comprising: receiving data inputs into a computing platform including a processor and memory that stores at least a portion of executable instructions to generate persona classification data; applying a subset of the data inputs into a context data generator, the subset of the data inputs includes entity data, entity communication link data, and entity profile data; generating persona characterization data to include descriptive data or image data, or both; applying the persona characterization data to a vector database; generating targeting parameters based on data representing distributed computing platforms configured to host distributed files; applying the targeting parameters to a first large language model (“LLM”) to identify distributed computing platform-specific segments as a function of persona characterization data; embedding into a vector database data the descriptive data or image data as vectors to form vectorized data; generating at the large language model an output configured to automatically modify operation of a brand profile generator using the vectorized data; deriving content based on the output including an event composite value; generating automatically integration data to include the image data and text-based data to integrate with at least one of the distributed computing platforms; and causing creative content to align with a targeted distribution source including a social media computing platform.
18. 18 . The method of claim 17 further comprising: applying the persona characterization data to a persona generator.

Description

CROSS-REFERENCE TO APPLICATIONS This nonprovisional application is a continuation-in-part (“CIP”) application of co-pending U.S. patent application Ser. No. 17/314,643 filed May 7, 2021 and entitled “CORRELATING EVENT DATA ACROSS MULTIPLE DATA STREAMS TO IDENTIFY COMPATIBLE DISTRIBUTED DATA FILES WITH WHICH TO INTEGRATE DATA AT VARIOUS NETWORKED COMPUTING DEVICES;” this nonprovisional application is a continuation-in-part (“CIP”) application of co-pending U.S. patent application Ser. No. 18/590,859 filed Feb. 28, 2024 and entitled “UPDATING COMPATIBLE DISTRIBUTED DATA FILES ACROSS MULTIPLE DATA STREAMS OF AN ELECTRONIC MESSAGING SERVICE ASSOCIATED WITH VARIOUS NETWORKED COMPUTING DEVICES;” this nonprovisional application is a continuation-in-part (“CIP”) application of co-pending U.S. patent application Ser. No. 18/590,863 filed Feb. 28, 2024 entitled “AGGREGATING DATA TO FORM GENERALIZED PROFILES BASED ON ARCHIVED EVENT DATA AND COMPATIBLE DISTRIBUTED DATA FILES WITH WHICH TO INTEGRATE DATA ACROSS MULTIPLE DATA STREAMS.” all of which are herein incorporated by reference in their entirety for all purposes. FIELD Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. Further, a computing platform is configured to receive inputs as natural language to facilitate automatic generation and integration to form a modified distributed file responsive to events, or moments, among other things including data relevant to an entity, which may provide a good or service. BACKGROUND Advances in computing hardware and software have fueled exponential growth in delivery of vast amounts of information due to increased improvements in computational and networking technologies. Also, advances in conventional data network technologies provide an ability to exchange increasing amounts of generated data via various electronic messaging platforms. Thus, improvements in computing hardware, software, network services, and storage have bolstered growth of Internet-based messaging applications, such as social networking platforms and applications, especially in a technological area aimed at exchanging digital information concerning products and services expeditiously. As an example, various organizations and corporations (e.g., retailer sellers) may exchange information through any number of electronic messaging networks, including social media networks (e.g., Twitter®, or X®, and Reddit™), as well as user-generated content (e.g., YouTube®) and news-related web sites. Such entities aim to provide time-relevant data and content to users online to manage brand loyalty and reputation, and to enhance customer engagement. And since different audiences and users prefer consuming content over different communication channels and various different data networks, traditional implementations of computing systems and computer-implemented processes have various drawbacks. Hence, traditional approaches are not well-suited to update distributed data files to optimize engagement with customers and potential customers in ever-increasingly dynamic computing environments. For example, traditional computing architectures typically require executable code to be deployed and maintained on a server, whereby some conventional server architectures hinder scalability. Known server architectures also may be single threaded. Examples of single threaded servers include conventional database servers, such as SQL servers (e.g., a PostgreSQL server). As a result, calls to application programming interfaces (“APIs”) are processed sequentially, which further hinders scalability. Consequently, traditional server architectures and processes are not well-suited to update distributed data files and content in real-time (or near real-time). Further, general techniques of updating distributed files, including those hosted by data sources, such as those platforms hosting social media application, typically are agnostic to aims of optimizing connecting with classifications of users associated with an entity delivering a good or service (e.g., a brand), especially in a dynamic environment (e.g., temporal events that affect an entity, such as a reputation of the entity). Thus, what is needed is a solution for facilitating techniques that optimize computer utilization and performance associated with updating data files and content via an electronic messaging service in association with an entity and its goods or services in a dynamic environment, without the