Topic Classification via Random Sample Probability Analysis
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Solution Overview
Problem
Existing classifiers are unable to accurately determine the primary and subordinate topics within an object, fail to recognize topics not present in the predefined set, and require analysis of the entire object, leading to inefficiencies.
Innovation Solution
A method that selects a small number of random samples from an object to determine topic probabilities, identifies new topics by analyzing the difference in probability sums and negativity, and iteratively refines the topic list, allowing for classification of primary and subordinate topics including those not in the predefined set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire object is analyzed to determine topic classification, then classification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by analyzing only a subset of samples from the object rather than the entire object. The system selects a manageable number of samples that are sufficient to determine topic probabilities accurately, thereby reducing processing time while maintaining classification accuracy. This is achieved by randomly selecting samples from the object and analyzing only these samples against the probability distribution profiles of topics.
Solution Approach 2:
The patent segments the object into multiple samples that can be analyzed independently. By dividing the object into discrete sample units, the system can process a representative subset rather than the whole object, reducing computational burden while preserving the ability to accurately determine topic classification through probability analysis of the segmented samples.
2Device complexity
If a predefined set of topics is used for classification, then classification process is simplified, but ability to recognize new topics is reduced
Solution Approach 1:
The patent implements feedback by calculating the sum of probabilities for all topics in the predefined set and comparing this sum to 1. When the sum differs from 1 (or when negative probabilities are detected), the system identifies this as feedback indicating the presence of a new topic not included in the predefined set. This feedback mechanism allows the system to maintain a simple predefined topic structure while still being able to recognize and account for new topics.
Solution Approach 2:
The patent makes the classification system universal by enabling it to handle both predefined topics and new topics through a unified probability analysis framework. The same probability distribution profile analysis used for predefined topics can also detect new topics through probability sum deviations, allowing the system to serve multiple classification needs without requiring separate mechanisms.
3Measurement precision
If more samples are selected from the object, then topic probability determination accuracy is improved, but sample size requirements and processing load increase
Solution Approach 1:
The patent applies partial action by determining that a sufficient number of samples can be analyzed to achieve accurate topic probability determination without needing to analyze all possible samples. The system identifies a manageable sample size that provides statistically significant results for probability calculation, balancing accuracy requirements with processing efficiency.
Data Source
AI summary
An object potentially belongs to a number of topics. Each topic is characterized by a probability distribution profile of a number of representative items that belong to the topic. Sample items are selected from the object, less than a total number of items of the object. A probability that the object belongs to each topic is determined using the probability distribution profile characterizing each topic and the sample items selected from the object.