AI Digital Assistant for Consolidating Data from Diverse Sources
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Solution Overview
Problem
Industrial systems face inefficiencies and data inaccuracies due to the manual and labor-intensive process of retrieving and consolidating data from diverse sources, which are often fragmented and exist in various formats, leading to data silos and scalability challenges.
Innovation Solution
An artificial intelligence (AI) digital assistant that semantically analyzes user information requests to identify the types of data needed and retrieves them in a consolidated format from diverse data sources, using vector embeddings and language models to enrich data sources and enable dynamic data retrieval without the need for explicit linking between data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data retrieval and consolidation processes are used, then operators can access data from multiple systems, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service through automated data retrieval and consolidation. The digital assistant autonomously queries multiple data sources, retrieves relevant information, and consolidates it without requiring manual operator intervention for each data gathering task, thereby resolving the contradiction between ease of operation and time consumption
Solution Approach 2:
Manual mechanical processes of data collection are replaced with an automated digital assistant system that uses natural language processing and semantic analysis to retrieve and consolidate data from diverse sources, eliminating the time-consuming manual labor while maintaining operational simplicity
2Adaptability or versatility
If data is stored in diverse formats across distinct platforms, then comprehensive data coverage is achieved, but data consolidation becomes arduous and creates data silos
Solution Approach 1:
The digital assistant is designed with universal capabilities to query and retrieve data from multiple diverse data sources including databases, files, and external systems. It handles various data formats and structures through a unified interface, achieving comprehensive data coverage without requiring separate systems for each data source type
Solution Approach 2:
The system introduces a semantic layer as an intermediary between diverse data sources and the user. This semantic layer translates various data formats and structures into a unified representation that the digital assistant can process, enabling data consolidation across platforms without directly managing the complexity of each source system
3Productivity
If manual data entry and consolidation are performed, then data can be aggregated from multiple sources, but human error introduces inaccuracies
Solution Approach 1:
Manual data entry operations are replaced with automated computational processes. The digital assistant uses programmatic data retrieval and processing methods that eliminate human errors such as data entry mistakes, misinterpretation, and omissions, thereby maintaining high productivity while significantly improving data accuracy and reliability
Solution Approach 2:
The system implements automated validation and quality control mechanisms that provide feedback on data retrieval and consolidation processes. This includes verifying data integrity, checking for inconsistencies, and ensuring accuracy standards are met, thereby maintaining high productivity while improving reliability through systematic error detection and correction
Data Source
AI summary
An artificial intelligence digital assistant that retrieves and consolidates data from diverse sources is described. After a system initiates a response to an information request received from a user, a language model generates a high-dimensional vector that represents the information request from the user. The language model identities data access tools that access corresponding data types identified for the information request. The system executes the identified data access tools that retrieve, from corresponding data sources, data values from corresponding data records represented by corresponding unique high-dimensional vectors that have similarities, which exceed a confidence level, to the high-dimensional vector that represents the information request. The system sends a consolidation of the data values in the response to the user.