Centralized Database for Violent Behavior Probability Inference
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Solution Overview
Problem
Current data processing systems face challenges in identifying disturbed or mentally ill individuals with a predisposition to violence due to the complexity of divergent data, lack of access to relevant information, and inability to compare data at different levels of granularity, leading to difficulties in predicting and preventing violent acts.
Innovation Solution
A computer-implemented method using a centralized database that compares divergent data to infer probabilities of violent behavior by applying rules to query data, assigning probabilities to inferences, and storing results, allowing for the identification of dangerous individuals before violent incidents occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized database compares divergent data to infer probabilities of violent behavior, then the ability to identify dangerous individuals is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex identification process into distinct functional modules: data collection from multiple sources, data normalization and integration, rule-based inference engine, probability calculation, and result presentation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving high identification accuracy
Solution Approach 2:
The patent introduces an intermediary inference engine that acts as a mediator between raw divergent data and final identification results. This intermediary layer applies standardized rules and probability calculations to transform heterogeneous data into meaningful intelligence, reducing the complexity burden on both data collection and result presentation layers
2Measurement precision
If the system analyzes vast amounts of divergent data at different levels of granularity, then the measurement precision of violent behavior prediction is improved, but the loss of time and computational resources increases
Solution Approach 1:
The system dynamically adjusts the level of data granularity analyzed based on the specific inquiry and risk level. For routine monitoring, it processes data at a higher level of aggregation, while for high-risk assessments, it automatically drills down to finer granularity. This dynamic adaptation maintains prediction precision while minimizing processing time and resource consumption
Solution Approach 2:
The system performs preliminary data normalization, filtering, and pre-processing as data enters the database. By preparing data in advance and organizing it into standardized formats with pre-computed features, the system reduces the computational burden during actual analysis, enabling fast processing of multi-granularity data without sacrificing prediction precision
Data Source
AI summary
A computer implemented method, apparatus, and computer usable program code for inferring a probability of a first inference absent from a database at which a query regarding the inference is received. The first inference relates to identifying a potentially violent person or group. Each datum of the database is conformed to the dimensions of the database. Each datum of the plurality of data has associated metadata and an associated key. The associated metadata includes data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum. The query is used as a frame of reference for the search. The database returns a probability of the correctness of the first inference based on the query and on the data.