Multi-threaded Profiles with Smart Data Filters
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Solution Overview
Problem
Enterprise systems face challenges in efficiently processing and filtering event information for multiple users due to exponential growth, which complicates resource usage and network bandwidth optimization.
Innovation Solution
A computing platform generates multi-threaded profiles, determines filter banks, and sets time-to-live parameters to process and filter event information effectively, generating recommendations and updating user devices with optimized resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprise systems process event information for multiple users, then user service coverage is improved, but system complexity and resource consumption increase exponentially
Solution Approach 1:
The patent segments user profiles into multiple independent threads, where each thread represents a specific aspect of user behavior or data type. This segmentation allows the system to process and filter events for different user segments independently, reducing the exponential complexity that would arise from processing all user data as a single monolithic structure.
Solution Approach 2:
The patent introduces a new dimensional organization by creating multi-threaded profiles that add a thread dimension to the traditional flat user profile structure. This dimensional change enables the system to handle complex user data by distributing it across multiple threads, thereby managing system complexity more effectively while maintaining comprehensive user service coverage.
2Reliability
If enterprise systems filter and process event information for all users, then data processing completeness is improved, but network bandwidth consumption and resource usage increase
Solution Approach 1:
The patent extracts and applies filters specifically to relevant threads within multi-threaded profiles based on event type and user context. Instead of processing all data uniformly across all users, the system extracts only the necessary data portions that match filter criteria, thereby maintaining data processing completeness while significantly reducing network bandwidth consumption and resource usage.
Solution Approach 2:
The patent implements partial processing by applying filtering operations only to specific threads and user segments that are relevant to particular events, rather than processing all user data completely. This partial action approach ensures that necessary data processing completeness is maintained for relevant users while avoiding excessive resource consumption on unrelated data.
3Productivity
If enterprise systems optimize resource usage, then efficiency is improved, but the ability to handle exponential growth of event information deteriorates
Solution Approach 1:
The patent implements dynamic multi-threaded profiles that can adapt their structure and content based on user behavior, event types, and system conditions. This dynamic organization allows the system to efficiently manage resources by activating and processing only the relevant threads needed for current operations, while maintaining the scalability to handle exponential growth as new threads can be created and integrated dynamically without restructuring the entire system.
Data Source
AI summary
Aspects of the disclosure relate to using smart data filters to create multi-threaded profiles. A computing platform may generate a multi-threaded profile corresponding to a user. Thereafter, the computing platform may receive, via the communication interface and from a user device, external event information corresponding to the multi-threaded profile. Then, the computing platform may determine, based on the external event information, a filter bank corresponding to a first thread of the multi-threaded profile. Subsequently, the computing platform may determine, based on the external event information and the filter bank, a time to live parameter corresponding to the external event information. Next, the computing platform may retrieve, from a multi-threaded profile server and based on the multi-threaded profile and the filter bank, first thread information corresponding to the first thread of the multi-threaded profile.