IP Data Analysis via ML Mapping
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Solution Overview
Problem
Conventional systems are inefficient and inaccurate in analyzing intellectual-property data to determine relationships between intellectual-property assets and products/services, leading to labor-intensive and unusable information for organizations.
Innovation Solution
The implementation of techniques and systems that utilize machine learning and natural language processing to analyze intellectual-property data from various sources, generate models, and provide services by mapping intellectual-property assets to products/services within a technology taxonomy, enabling accurate valuation and exposure assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to analyze intellectual-property data, then the analysis process is simple, but the accuracy and efficiency of determining relationships between intellectual-property assets and products/services deteriorates
Solution Approach 1:
The patent replaces conventional manual or simple automated analysis methods with machine learning models and natural language processing systems. These intelligent systems automatically extract relationships between intellectual-property assets and products/services from unstructured data, significantly improving accuracy while managing complexity through automated processing pipelines.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization modules, feature extraction components, and relationship mapping mechanisms that bridge raw intellectual-property data and product information. These intermediaries transform complex unstructured data into structured relationships that can be accurately analyzed and utilized.
2Productivity
If manual analysis methods are used, then the system complexity is low, but the productivity and efficiency of intellectual-property analysis deteriorates
Solution Approach 1:
The patent implements self-service capabilities where the system automatically performs data collection, processing, and relationship extraction without requiring manual intervention at each step. The machine learning models continuously learn from data and automatically update their analysis capabilities, enabling high productivity while reducing the need for complex manual orchestration.
Solution Approach 2:
The patent performs preliminary actions by pre-processing intellectual-property data and product information before analysis, including data cleaning, normalization, and feature extraction. This preliminary preparation work is done automatically using scripting and automated pipelines, which increases productivity by having data ready for analysis without requiring manual preparation during the analysis process.
3Loss of information
If comprehensive intellectual-property data is collected from multiple sources, then the information completeness improves, but the difficulty of analyzing and processing the data increases
Solution Approach 1:
The patent segments comprehensive intellectual-property data from multiple sources into distinct categories and types, such as patent data, trademark data, product information, and market data. Each segment is processed using specialized methods and models appropriate to its type, making the overall processing more manageable while maintaining information completeness. The segmentation allows parallel processing and reduces the complexity of handling all data uniformly.
Solution Approach 2:
The patent develops a universal data processing framework that can handle multiple types of intellectual-property data from various sources through a common architecture. The system uses universal data models and standardized processing pipelines that can adapt to different data types and sources, reducing the difficulty of processing comprehensive data while maintaining completeness across all information types.
Data Source
AI summary
Techniques described herein are directed to analyzing intellectual-property data according to provide various intellectual property related services to organizations. In particular implementations, information related to products and/or services may be obtained from a number of data sources. Additionally, information related to intellectual-property assets, such as patents, trademarks, copyrights, trade secrets, and know-how, may be obtained. In various situations, the intellectual-property assets may be mapped to respective products and/or services. The mappings between the products and/or services and intellectual-property assets may be used to provide intellectual property related services that correspond to the intellectual-property assets, such as valuation services, strategy-related services, or risk-related services.