AI Knowledge Corpus Maturation via Anonymized User Clustering
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Solution Overview
Problem
Existing AI systems require a large amount of data and time to mature, and there are concerns about user data privacy, with users lacking control over who accesses their data and for what purposes.
Innovation Solution
A method that utilizes anonymous networks for multi-user collaboration, allowing users to share subsets of their knowledge corpus data, enabling AI systems to mature more efficiently while ensuring user data privacy through anonymization and user consent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI systems use user data from multiple sources to mature faster, then the maturation speed improves, but user data privacy is compromised
Solution Approach 1:
The patent introduces an intermediary anonymization layer between data collection and AI training. User data is transformed into anonymized representations that preserve training value while removing personally identifiable information. This intermediary process enables multi-source data aggregation for faster maturation while protecting user privacy through systematic anonymization techniques.
Solution Approach 2:
The patent segments user data into different components: identifiable information is separated from anonymized training data. By dividing the data processing pipeline into distinct stages (collection, anonymization, training), the system can leverage multiple user sources for accelerated maturation while ensuring privacy protection through segmented data handling procedures.
2Reliability
If AI systems collect and process large amounts of user data, then the quality and completeness of knowledge corpus improves, but the time required for maturation increases
Solution Approach 1:
The patent implements preliminary actions by pre-processing and anonymizing user data before it enters the training pipeline. Data validation, anonymization, and initial processing are performed in advance, allowing the AI system to immediately utilize prepared datasets from multiple users without sequential processing delays. This preliminary preparation enables parallel data ingestion while maintaining high knowledge corpus quality.
Solution Approach 2:
The patent establishes continuous data collection and processing workflows where user data flows continuously through anonymization and training pipelines. Instead of batch processing that causes interruptions, the system maintains continuous operation across multiple data sources, ensuring uninterrupted maturation progress while accumulating comprehensive knowledge corpus data over time.
3Object-affected harmful factors
If AI systems implement strict user data privacy controls, then user privacy protection improves, but data sharing and collaboration between users is limited
Solution Approach 1:
The patent changes the parameter of data representation from identifiable to anonymized form. By transforming data parameters (removing PII while preserving patterns), the system maintains strong privacy protection controls while enabling versatile data sharing and collaboration. Users can contribute to collective knowledge corpus maturation with anonymized data, achieving both privacy security and collaborative adaptability through parameter transformation.
Data Source
AI summary
In an approach to mature a knowledge corpus using artificial intelligence (AI) and user collaboration, embodiments create, by an AI response system, a knowledge corpus based on retrieved data associated with a first user. Additionally, embodiments execute, by the AI response system, a search to locate one or more matching knowledge corpora based on a request of the first user, and identify, by an anonymous network, at least one cluster of one or more anonymous second users having respective search requests that substantially match the search request of the first user. Furthermore, embodiments execute an AI exchange between the identified one or more anonymous second users and the first user, and mature the knowledge corpus, via the AI response system, based on the AI exchange between the first user and the identified one or more anonymous second users.