Live Data Subset Processing for Real-Time CRM Insights
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Solution Overview
Problem
Existing data processing systems for CRM and similar applications struggle to provide dynamic and real-time insights due to the cumbersome processing of vast amounts of data, lacking the ability to offer instant insights and efficient data representation.
Innovation Solution
A computer-implemented method that scans data to determine a limited subset (about 20% of the entire data set) for processing, drills down to obtain workable results, and saves these results in a compact form for real-time management, using machine learning to learn from user interactions and provide live suggestions, with a tags-based notifications system for immediate data filtration and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data processing systems process vast amounts of data in real-time, then complete data coverage is achieved, but processing speed and response time deteriorate
Solution Approach 1:
The patent segments the vast data set into manageable portions using AI-driven sampling techniques. Instead of processing all data simultaneously, the system divides data into representative samples that can be processed quickly while still providing comprehensive insights. This segmentation allows real-time processing without sacrificing data coverage.
Solution Approach 2:
The patent applies partial action by processing a carefully selected subset (sample) of the total data rather than the complete data set. The AI algorithms determine the optimal sample size and selection criteria to ensure that processing a partial portion of data yields results equivalent to processing all data, thereby reducing processing time while maintaining information completeness.
2Measurement precision
If complete data sets are processed for real-time insights, then accuracy is improved, but system complexity and resource requirements worsen
Solution Approach 1:
The patent introduces AI algorithms as intermediaries between the raw data and the analysis process. These AI intermediaries pre-process and filter the data, identifying and extracting only the most relevant information for real-time processing. This intermediary layer simplifies the subsequent analysis while maintaining accuracy by ensuring that only high-quality, relevant data proceeds to the insight generation stage.
Solution Approach 2:
The system performs preliminary actions by using AI to pre-analyze and prepare data before real-time processing. The AI algorithms conduct initial filtering, aggregation, and prioritization of data elements, so that when real-time processing occurs, the system is already working with pre-processed, high-value data. This preliminary action reduces the complexity of real-time processing while preserving accuracy.
3Productivity
If dynamic data representation is provided in real-time, then operational efficiency is improved, but data processing capacity requirements worsen
Solution Approach 1:
The patent dynamically changes data representation parameters based on real-time needs. Instead of processing and displaying all data in uniform detail, the system adjusts parameters such as data granularity, aggregation levels, and visualization depth according to the specific operational context. This allows the system to provide dynamic, relevant insights with reduced processing capacity by focusing computational resources on the most critical data parameters.
Data Source
AI summary
A method for providing live information, comprising: a) scanning data for determining limited data to work on among data sets that are too large or complex to be dealt with; b) drilling down to the following data that correlates with the previously determined limited data until obtaining the desired number of workable live results; and c) saving the results as new data that capture only limited capacity, thereby providing an in-house collection that, due to its limited capacity, information can be retrieved and worked on it live.