Influence Probability Measurement via Semantic Embeddings
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Solution Overview
Problem
Identifying and recognizing influencers in communication networks is challenging due to the difficulty in tracing their impact, leading to suboptimal resource utilization and lack of recognition, which reduces team efficiency.
Innovation Solution
A system using semantic embeddings and multidimensional statistics to calculate probabilities of influence, determining whether content is derived from historic knowledge or influenced by other communications, generating influence attribution recommendations to acknowledge and optimize influencer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify influencers, then the system complexity remains low, but the measurement precision of influence probability is insufficient
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary representation layer between raw communication data and influence analysis. These embeddings capture semantic meaning and serve as a mediator that enables precise probability calculations without directly processing complex communication networks, thus resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
The patent replaces traditional mechanical/algorithmic influence detection methods with statistical probability models based on semantic embeddings. This substitution allows for more precise measurement of influence probability while maintaining manageable system complexity through mathematical abstraction rather than complex computational graphs.
2Loss of information
If influence attribution is not tracked, then the ease of operation remains high, but the loss of information about influencer impact is significant
Solution Approach 1:
The patent implements feedback mechanisms that automatically track and attribute influence information back to communication sources. By continuously monitoring communication patterns and comparing them against semantic embeddings, the system provides feedback about influencer impact without requiring manual analysis, thus reducing information loss while maintaining operational simplicity.
Solution Approach 2:
The system performs self-service by automatically detecting and attributing influence relationships without requiring external intervention. The semantic embedding model autonomously analyzes communication data, calculates influence probabilities, and generates attributions, thereby preventing information loss while keeping the operation simple for users.
3Productivity
If manual influencer identification is used, then the device complexity is low, but the productivity of resource allocation is reduced
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing semantic embeddings of communications before influence analysis is needed. This preparation allows the system to quickly calculate influence probabilities and allocate resources efficiently without requiring complex real-time analysis, thus improving productivity while keeping the actual resource allocation process simple.
Solution Approach 2:
The patent changes the parameters of communication data transformation into semantic embeddings, which standardized representations that enable efficient influence measurement. This parameter transformation allows for rapid resource allocation decisions based on pre-processed data, improving productivity without requiring complex computational operations during decision-making.
Data Source
AI summary
The disclosure herein describes a system for measuring probability of influence in digital communications to determine if communication content originated in a person's own prior knowledge or new information more recently obtained from interaction with communications of others. An estimated probability a new communication by a first user comes from the same distribution as prior communications of the first user are generated using multidimensional statistics on embeddings representing the communications. A second estimated probability that the new communication comes from the same distribution as communication(s) of a second user that were accessible to the first user are generated. If the second probability is greater than the first probability, the new communication is more likely influenced by exposure of the first user to the second user's communications rather than the first user's own historical knowledge. An influence attribution recommendation is generated, including an influence attribution or other recommended action.