Behavioral Co-occurrence Detection in Collaborative Interaction Evaluation
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Solution Overview
Problem
Current evaluation methods for interactions, such as group problem-solving tasks and negotiations, are limited by human-induced errors and time-consuming direct observation, which fail to accurately capture individual contributions and behavioral cues.
Innovation Solution
A computer-implemented system generates temporal records of verbal and non-verbal behavior features during interactions, analyzing co-occurrences to produce a co-occurrence record that can be used for automated scoring and feedback, enabling improved evaluation of collaborative activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct human observation is used to evaluate interactions, then evaluation can be performed, but measurement precision is limited due to human-induced errors and inability to detect all behavioral cues
Solution Approach 1:
The patent replaces human observation with an automated computer-based evaluation system that uses video cameras, audio recorders, and computational algorithms to detect and analyze behavioral cues. The system processes visual and auditory data through image processing, speech recognition, and pattern matching algorithms to identify prototypical behavior states and co-occurrence patterns, thereby eliminating human-induced errors while maintaining comprehensive detection capability.
Solution Approach 2:
The patent introduces an intermediary processing layer between the interaction and the evaluation. Video and audio data are captured by recording devices, then processed through computational intermediaries that extract behavioral features, identify prototypical states, and determine co-occurrence patterns. This intermediary system bridges the gap between raw data collection and meaningful evaluation, enabling precise measurement without direct human intervention.
2Productivity
If direct observation is used to evaluate interactions, then evaluation can be performed, but loss of time increases due to time-consuming observation and review processes
Solution Approach 1:
The patent implements continuous automated recording and analysis during the interaction itself, rather than requiring post-event review. The system operates continuously throughout the interaction, capturing video and audio streams, extracting behavioral features in real-time, and generating co-occurrence records without interruption. This eliminates the time loss associated with reviewing recorded interactions after the fact.
Solution Approach 2:
The system performs preliminary processing of behavioral data during the interaction, pre-identifying prototypical behavior states and co-occurrence patterns before the interaction concludes. By preparing and analyzing data in advance during the interaction itself, the system eliminates the need for subsequent review time, significantly improving evaluation productivity.
3Loss of information
If post-interaction evaluation of output is used, then evaluation can be performed, but loss of information increases because individual contributions cannot be correctly weighed
Solution Approach 1:
The patent segments the interaction data into individual contributor records, assigning unique temporal records to each participant. The system analyzes and weights individual behavior features separately before aggregating to group-level evaluations. This segmentation enables precise tracking of individual contributions while maintaining the ability to assess overall interaction quality, eliminating information loss about individual roles.
Solution Approach 2:
The patent adds a temporal dimension to the evaluation by creating time-stamped co-occurrence records that track when specific behavior patterns occur during the interaction. This temporal dimension allows the system to distinguish individual contributions based on timing and sequence, providing granular information about individual roles while maintaining comprehensive group-level analysis capability.
Data Source
AI summary
Systems and methods for computer-implemented evaluation of a performance are provided. In a first aspect, a computer-implemented method of evaluating an interaction generates a first temporal record of first behavior features exhibited by a first entity during an interaction between a first entity and a second entity. A second temporal record is generated including second behavior features exhibited by a second entity during an interaction with a first entity. A determination is made that a first feature of a first temporal record is associated with a second feature of a second temporal record. The length of time that passes between the first feature and second feature is evaluated, and a determination is made that the length of time satisfies a temporal condition. A co-occurrence record associated with a first feature and a second feature is generated and included in a co-occurrence record data-structure.