User Group Behavior Profiling via Indirect Interaction Modeling

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Solution Overview

Problem

Existing methods for analyzing user behavioral patterns require direct access to user data, lacking efficient alternatives that do not involve contacting the user.

Innovation Solution

A system utilizing machine learning to analyze user behavioral patterns by processing data from source user groups' interactions with target user groups, employing AI models like hidden Markov models, echo state networks, or Bayesian networks, without requiring direct contact with the target user groups.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If direct user data access is used to analyze behavioral patterns, then measurement precision is improved, but device complexity and operational difficulty increase

Engineering Contradiction:

Improvebehavioral pattern analysis accuracyVSAvoiddata access and processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary system that collects data from source user groups and uses machine learning models to infer target user group behaviors. This mediator approach allows accurate behavioral analysis without direct access to target user data, resolving the contradiction between measurement precision and device complexity by enabling indirect observation through inferred patterns from related data sources

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If machine learning models are applied to predict user behaviors, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improvebehavioral prediction efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent creates simplified representations (copies) of user group behaviors through machine learning models that capture essential patterns from source user group data. These models produce inferred behavioral profiles of target user groups without requiring complex direct data collection systems, thereby improving productivity while managing device complexity through abstraction and inference

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12499376B2Method and system for determining behavioral patterns of user groups based on interactions with other user groups using machine learning

Publication Date: 2025.12.16 CLARI INC

AI summary

The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model. The target process information can include the target's identifiers for each stage, an estimated duration of the stage, deliverables for the stage, and one or more stage transition events for the stage.