Multiparty Stream Monitoring Using Communication Vectors

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Solution Overview

Problem

Multiparty communication platforms struggle to effectively track and process inputs from disparate sources for analysis and evaluation.

Innovation Solution

An apparatus and method for monitoring multiparty stream communication using a processor and memory to generate communication vectors, retrieve target vectors, and compare them to assess semantic targets, employing machine learning algorithms for depth detection and feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If multiparty communication platforms stream multiple participants' inputs, then the quantity of communication data increases, but the ability to track and process those inputs effectively deteriorates

Engineering Contradiction:

Improvequantity of communication dataVSAvoidability to track and process inputs

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The patent segments the multiparty communication data into individual packet series from each participant, allowing separate tracking and processing of each participant's input while maintaining overall system coherence. This segmentation enables effective processing despite the increasing quantity of data from multiple sources.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If data from disparate sources is tracked simultaneously, then the comprehensiveness of monitoring increases, but the complexity of data processing increases

Engineering Contradiction:

Improvecomprehensiveness of monitoringVSAvoidcomplexity of data processing

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces communication vectors as intermediary representations that simplify the processing of data from disparate sources. These vectors serve as a standardized format that maintains comprehensiveness of monitoring while reducing the complexity of handling multiple different data sources simultaneously.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If machine learning algorithms are used for depth detection and feedback, then the precision of semantic analysis improves, but the computational resources required increase

Engineering Contradiction:

Improveprecision of semantic analysisVSAvoidcomputational resources required

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent applies machine learning algorithms selectively to generate communication vectors and perform depth detection only where needed for semantic analysis, rather than processing all communication data with full ML complexity. This partial application maintains precision where required while managing computational resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12549400B2Apparatus and method for monitoring multiparty stream communications

Publication Date: 2026.02.10 BREAKOUT LEARNING INC

AI summary

This disclosure pertains to an innovative approach for enhancing monitoring multiparty stream communication analysis and machine learning applications. The apparatus monitor a multiparty stream communication, wherein the multiparty stream communication includes a first and a second packet series to generate, retrieve, and compare data elements. Training data is filtered and categorized into specific sub-populations, increasing relevance to particular subjects of analysis. These tailored datasets optimize machine learning models, resulting in more precise comprehension assessment.