Humanoid Data Stream Modeling for Multi-Party Conversation Engagement
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Solution Overview
Problem
Machines, such as humanoids, struggle to effectively participate in multi-party conversations by understanding when and how to engage or interject, lacking natural senses to infer appropriate participation, which can lead to a lack of indistinguishability from human interaction.
Innovation Solution
A computer-executed humanoid process models electronic communications as data streams, labeling and sorting them in a database or nodal graph representation to determine participation in conversations, mimicking human behavior by interpreting context and subtle indications for timely and effective interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a machine communicates in multi-party conversations without natural senses, then it can process information systematically, but it cannot infer when and how to engage or interject naturally
Solution Approach 1:
The patent introduces data stream modeling as an intermediary layer between the machine's systematic processing and natural conversation engagement. By representing conversations as structured data streams with roles, turns, and participants, the system gains the adaptability to infer appropriate engagement moments while maintaining systematic processing capabilities.
Solution Approach 2:
The conversation is segmented into discrete data streams representing different participants and communication channels. This segmentation allows the machine to systematically analyze each stream while identifying patterns that indicate when intervention is appropriate, bridging the gap between automated processing and natural engagement.
2Reliability
If a humanoid provides customer support indistinguishably from a human, then customer satisfaction improves, but the complexity of modeling human-like communication increases
Solution Approach 1:
The patent creates a simplified copy of human communication patterns through data stream modeling. Instead of replicating the full complexity of human senses and cognition, the system copies the essential structure of conversations (participants, turns, roles) to achieve human-like interaction with reduced complexity.
Solution Approach 2:
The system changes parameters from raw unstructured communication to structured data streams with defined attributes (participant IDs, roles, turn numbers). This parameter transformation enables systematic analysis while maintaining the appearance of natural human-like communication, improving reliability without proportionally increasing complexity.
3Ease of operation
If a machine monitors all electronic communications to determine participation, then it can respond appropriately, but it requires complex modeling of multiple communication streams
Solution Approach 1:
The patent segments the complex communication environment into separate data streams for each participant and channel. This segmentation simplifies monitoring by allowing the system to process each stream independently according to its characteristics, making appropriate response determination easier without requiring monolithic complex modeling.
Solution Approach 2:
Different data streams are modeled with different qualities and attributes appropriate to their specific context (e.g., customer vs. agent vs. supervisor streams). This local quality approach allows the system to handle each communication type with tailored simplicity, reducing overall complexity while improving ease of determining appropriate responses.
Data Source
AI summary
A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid process software,” can be configured to participate in multi-parry conversations. The humanoid can monitor electronic communications in a conversation involving the humanoid and at least one other party. The humanoid can model the electronic communications by uniquely identifying each of the electronic communications as a stream of data. For example, the data can be labeled and sorted in a database and/or arranged in a nodal graph representation. The humanoid can participate in the conversation based on the modeling.