Personalized Perspective Search with Trained Avatars for Data Filtering
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Solution Overview
Problem
The overwhelming volume of digital data makes it challenging for individuals to sift through and make accurate predictions or evaluations, lacking effective technological means to filter information from the perspectives of others.
Innovation Solution
A computer-implemented system uses trained avatar mechanisms to search and filter data based on the perspectives of specific individuals, utilizing their textual digital footprints to emulate their viewpoints and generate search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search methods are used to sift through large volumes of digital data, then comprehensive data coverage is achieved, but the time and effort required to process and evaluate the data becomes unmanageable
Solution Approach 1:
The patent introduces AI-powered avatar mechanisms as intermediaries between users and the vast corpus of digital data. These avatars are trained on specific individuals' digital footprints and act as mediators that automatically search, filter, and evaluate data according to those individuals' perspectives, eliminating the need for users to manually process overwhelming volumes of information while maintaining comprehensive data coverage
Solution Approach 2:
The patent creates digital copies (avatars) of individuals' perspectives by training machine learning models on their textual digital footprints. These avatar copies can independently search and evaluate data from the perspective of the original individuals, allowing simultaneous exploration of multiple perspectives without proportionally increasing user time investment
2Adaptability or versatility
If manual evaluation of data from multiple perspectives is attempted, then diverse insights are obtained, but the complexity of the evaluation process becomes unmanageable
Solution Approach 1:
The patent creates simplified digital copies (avatars) of complex human perspectives. Each avatar encapsulates an individual's unique viewpoint, values, and knowledge base in a trainable machine learning model. Users can effortlessly deploy multiple avatar perspectives without managing the inherent complexity of simulating diverse human viewpoints, as the avatar training process automatically handles the complexity of perspective representation
Solution Approach 2:
The avatar mechanisms are self-sufficient in performing perspective-based data evaluation. Once trained on an individual's digital footprint, the avatars autonomously search, filter, and evaluate data according to that person's perspective without requiring continuous user configuration or management. This self-service capability eliminates the operational complexity users would otherwise face in managing multiple perspective evaluations
3Measurement precision
If comprehensive data filtering based on multiple individual perspectives is implemented, then personalized insights are improved, but the computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary action by training avatar mechanisms in advance on individuals' digital footprints. This upfront training phase captures and stores perspective-specific knowledge structures that can then be efficiently applied during actual data search and filtering operations. By pre-processing the perspective modeling work, the system reduces computational resource consumption during runtime while maintaining high personalization accuracy
Solution Approach 2:
The patent creates lightweight digital copies (avatars) that encapsulate perspective information in compressed vector representations. These avatar copies require significantly fewer computational resources to operate than full-scale analysis of original digital footprints during each search operation, enabling efficient deployment of multiple perspective filters while preserving personalization accuracy through the condensed avatar models
Data Source
AI summary
A method includes performing a search on a body of information based on a first perspective, wherein the first perspective is determined using a first corpus of information associated with a first particular set of people; and providing at least some results of the search. The search may be a perspective search. Results of the search may be evaluated based on a second perspective, which is based on a second corpus of information associated with a second particular set of people. The perspective may be determined using an avatar mechanism that was trained using a corpus of information.