Electronic Data Analysis Using Machine Learning for Multi-Source Visualization
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Solution Overview
Problem
The process of searching, charting, and analyzing data from multiple disparate sources is complex and inefficient, making it difficult to perform tasks such as comparing data and analyzing trends, especially at a low cost.
Innovation Solution
An electronic data analysis system that receives search queries, identifies relevant terms, sends requests to multiple data sources, assigns data into visualization categories, and displays the results, utilizing machine learning and APIs for efficient data retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from multiple disparate sources using standard search engines, then data collection capability is improved, but the ability to perform complex analysis and visualization deteriorates
Solution Approach 1:
The patent introduces an intermediary system that sits between multiple data sources and the user interface. This intermediary automatically collects data from various sources, processes it through machine learning algorithms, and presents unified visualizations. The intermediary handles the complexity of multi-source integration, allowing users to benefit from diverse data without directly managing the complexity.
Solution Approach 2:
The patent replaces manual data collection and analysis mechanisms with automated machine learning systems. Instead of requiring users to manually search, collect, and analyze data from multiple sources, the system uses ML models to automatically perform these tasks, substituting mechanical human effort with intelligent automation.
2Reliability
If comprehensive data from multiple sources is collected, then analysis completeness is improved, but processing time and cost increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-analyzing data as it is collected from sources. The machine learning system continuously processes incoming data, preparing it for analysis before users need it. This preliminary processing reduces the time required when users initiate queries, as the heavy lifting of data preparation has already been completed.
Solution Approach 2:
The system maintains continuous data collection and processing operations rather than batch processing. The machine learning models continuously analyze incoming data streams, ensuring that analysis is always ready when needed. This continuous operation eliminates idle time and maintains constant productivity in data processing.
3Ease of manufacture
If manual data collection and analysis is performed, then cost is reduced, but productivity and accuracy deteriorate
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform data collection, processing, and analysis without requiring extensive manual intervention. The machine learning models autonomously navigate data sources, extract relevant information, and generate insights, allowing the system to serve itself in the data analysis process while maintaining high productivity.
4Quantity of substance
If data from multiple sources is integrated, then information completeness is improved, but data relevance and quality control worsen
Solution Approach 1:
The patent applies local quality by using machine learning to assign different levels of processing and validation to different data sources based on their reliability and relevance. The system identifies and weights data from high-quality sources more heavily while filtering or down-weighting data from less reliable sources. This localized quality control ensures that each data source is handled appropriately according to its specific characteristics.
Data Source
AI summary
An electronic data analysis method including at least one server, the electronic data analysis method comprising the steps of: a) Receiving, from an electronic device associated with a user to an electronic data analysis system, a search query including one or more search query terms; b) Maintaining an electronic search term database in association with the electronic data analysis system, the search term database comprising a plurality of search terms; c) Identifying, using the electronic data analysis system, one or more relevant search terms from the plurality of search terms in the electronic search term database based on the one of more search query terms; d) Sending, using the electronic data analysis system, a search request related to the one or more relevant search terms to a plurality of data sources external to the electronic data system; e) Receiving, using the electronic data system, data associated with the one or more relevant search terms from the plurality of data sources; f) Assigning, using the electronic data system, the data retrieved from the plurality of data sources into a plurality of data visualisation categories; and g) Displaying, using the electronic data system, the plurality of data visualisation categories on the electronic device.