Strategic Competitive Intelligence Data Aggregation
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Solution Overview
Problem
Current solutions fail to comprehensively integrate technology development data with intellectual property data to generate descriptive and predictive strategic competitive intelligence, limiting their effectiveness in informing critical business decisions.
Innovation Solution
A hardware processor-based method and system that aggregates technology development data and intellectual property data using a data mining module, rule engine, and analytics engine to create entity-aligned clusters, populate relevancy scores in matrices, and generate predictive intelligence data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing solutions aggregate IP data and technology development data, then data integration is achieved, but comprehensive descriptive and predictive strategic competitive intelligence cannot be generated
Solution Approach 1:
The system segments the data aggregation and analysis process into distinct functional modules: data mining module for collecting IP and technology data, rule engine for filtering based on entity-specific rules, aggregator for combining filtered data, and analytics engine for generating descriptive and predictive intelligence. This segmentation allows each module to specialize in specific tasks, enabling comprehensive strategic competitive intelligence generation that integrates both data types effectively.
Solution Approach 2:
The system performs preliminary data filtering using entity-specific rules stored in the rule engine before aggregation. This preliminary action ensures that only relevant technology development data and IP data are combined, preparing the data in advance for meaningful analysis and generating actionable strategic competitive intelligence rather than overwhelming raw data.
2Adaptability or versatility
If technology development data from public domain is fused with internal IP data, then comprehensive analysis is enabled, but system complexity increases
Solution Approach 1:
The system employs a universal data mining module that can collect both internal IP data and external technology development data from multiple public domain sources. The same rule engine handles filtering for both data types using entity-specific rules, and the aggregator uniformly combines them. This multi-functionality approach enables comprehensive data integration while managing complexity through standardized processing logic.
Solution Approach 2:
The rule engine acts as an intermediary between the data mining module and the aggregator. It receives raw data from both internal and external sources, filters it according to entity-specific rules, and passes only the relevant filtered data to the aggregator. This intermediary layer simplifies the overall system architecture by centralizing the filtering logic and preventing unnecessary data from reaching subsequent processing stages.
3Measurement precision
If data filtering and aggregation processes are implemented, then relevant intelligence is generated, but processing time increases
Solution Approach 1:
The system performs preliminary filtering of both internal IP data and external technology development data using entity-specific rules stored in the rule engine before aggregation. This preliminary action reduces the volume of data that needs to be processed in subsequent stages, ensuring that only relevant information is combined and analyzed, thereby reducing overall processing time while maintaining high intelligence accuracy.
Solution Approach 2:
The rule engine extracts and removes irrelevant data elements from the raw technology development data and IP data before aggregation. By taking out only the necessary filtering logic and executing it efficiently, the system reduces the data volume for subsequent processing while maintaining the precision of the final intelligence output.
Data Source
AI summary
Strategic competitive intelligence data generation systems and methods are provided. The system stores internal intellectual property data of an entity, receives external technology development data from predefined sources and intellectual property data from the internal portfolio database of the entity, stores a set of relevant rules, executes them, filters the technology development data and the intellectual property data, based on the rules executed by the rule engine. It stores in one of an entity aligned cluster database, the filtered technology development data and the intellectual property data. The entity aligned cluster databases are created against predefined entity aligned clusters. The system executes an input query, determines one relevant entity aligned cluster database for executing the search query, and populates relevancy scores in two matrices, aggregates the relevancy scores through multiplication of the matrices. The system then analyses the resultant matrix based on set of rules and displays results of the analytics.