User-Specific Interaction Recall via High-Dimensional Vector Mapping

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Solution Overview

Problem

The vast amount of information generated from computer-based interactions, such as meetings and conversations, is difficult for individuals to remember due to its volume and complexity, necessitating an efficient method for recalling user-specific interactions across various computer application programs.

Innovation Solution

A computer-implemented method that tracks and stores user-specific application data, translates it into content vectors, and maps these vectors to a high-dimensional space for geometric mathematical operations to identify relevant interactions, allowing users to query and retrieve specific information using a virtual conscience model trained with NLP techniques.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If user-specific application data from multiple computer application programs is collected and stored, then the completeness and coverage of interaction information is improved, but the complexity of data management and processing increases

Engineering Contradiction:

Improvecompleteness of interaction informationVSAvoidcomplexity of data management

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments interaction data from different computer application programs into separate data streams, each processed independently through its own content vector translation pipeline. This segmentation allows the system to manage complex multi-source data by treating each application's data separately, reducing overall management complexity while maintaining complete information coverage across all applications.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces content vectors as an intermediary representation layer between raw application data and the recall system. This intermediary transformation converts diverse application-specific data formats into a unified vector space, simplifying subsequent processing and comparison operations while preserving the complete information content from multiple sources.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If detailed user-specific interaction data is stored across multiple applications, then the accuracy of recall results is improved, but the time and computational resources required for processing increase

Engineering Contradiction:

Improveaccuracy of recall resultsVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary translation of user-specific application data into content vectors and maps these vectors to a high-dimensional content space in advance, before recall operations are needed. This pre-processing creates ready-to-query vector representations, significantly reducing the time required for actual recall operations while maintaining high accuracy through the detailed vector representations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent transforms interaction data from application-specific formats into standardized content vector parameters with fixed dimensions and properties. This parameter standardization enables efficient computational processing while preserving the detailed information needed for accurate recall, as the vector parameters can be rapidly compared and searched using optimized mathematical operations.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If a high-dimensional content space is used to map content vectors, then the ability to perform geometric mathematical operations and identify correlations is improved, but the computational complexity and resource requirements increase

Engineering Contradiction:

Improveability to identify correlationsVSAvoidcomputational complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent maps content vectors to a high-dimensional content space, adding dimensional depth to the data representation. This dimensional expansion enables the system to capture complex relationships and correlations between interactions that cannot be represented in lower dimensions, while the structured vector space allows efficient geometric operations despite the increased dimensionality.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS11789944B2User-specific computer interaction recall

Publication Date: 2023.10.17 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A computer-implemented method for recalling user-specific interactions is disclosed. User-specific application data for each of a plurality of different computer application programs is received at a computing system. The user-specific application data is translated into different content vectors representing different user-specific interactions between a user and one or more other users while using the plurality of different computer application programs. Each content vector includes parameters quantifying interaction attributes of the corresponding user-specific interaction. The content vectors are mapped to a high-dimensional content space. A query is received at the computing system and translated into a query vector. Geometric mathematical operations are performed to compare content vectors in the high-dimensional content space to the query vector to identify a content vector that correlates to the query vector. A response to the query that identifies a user-specific interaction corresponding to the identified content vector is output from the computing system.