Machine Learning Model Vector for Predicting Relevant Insights
Solution Overview
Problem
Identifying relevant data for a specific user from large volumes of data captured from various sources is challenging, as existing technologies lack the mechanism to determine which data is relevant to a particular user.
Innovation Solution
A method that extracts data from disparate sources, identifies trends, and forms a model vector incorporating user feedback. This model vector is then input into a trained machine learning model to predict the relevance of insights, allowing for the selection and reporting of predicted relevant insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually queries data from multiple disparate sources to find relevant information, then the user can obtain some relevant insights, but the process is error-prone and time-consuming
Solution Approach 1:
The system automatically performs data extraction, insight generation, and relevance prediction without requiring manual user queries. The machine learning model self-servingly identifies and ranks relevant insights from disparate data sources based on user profiles and feedback, eliminating the need for users to manually search through data.
Solution Approach 2:
The system incorporates user feedback loops where user interactions with predicted insights are captured and used to retrain and improve the machine learning model. This feedback mechanism continuously enhances the accuracy of relevance prediction over time, making the system progressively better at identifying what each user finds relevant.
2Quantity of substance
If a computer system stores and processes large volumes of data from multiple sources, then comprehensive information is available, but the system lacks the mechanism to determine which data is relevant to a specific user
Solution Approach 1:
The system transforms raw data into standardized model vectors with consistent parameters and structures. By converting disparate data from multiple sources into a unified representation format, the system enables efficient processing and comparison, making relevance detection feasible despite the diversity of source data.
Solution Approach 2:
The machine learning model acts as an intermediary between the stored data and the user's information needs. It processes the raw data, applies user-specific context and preferences, and outputs predicted relevant insights, bridging the gap between comprehensive data storage and personalized relevance identification.
3Loss of information
If users manually analyze data to identify relevant portions, then they can find some useful information, but they may miss important aspects due to human limitations
Solution Approach 1:
The machine learning model performs multiple functions simultaneously: it extracts insights from data, ranks them by predicted relevance, and adapts to individual user preferences. This multi-functional approach ensures comprehensive coverage of relevant information without requiring multiple separate manual analysis processes.
Data Source
AI summary
A method including extracting data from disparate data sources. The data includes data pairs including a corresponding data point and a corresponding time associated with the corresponding data point. The method also includes extracting insights from the data at least by identifying a trend in the data pairs. The method also includes forming a model vector including the insights and an additional attribute to the insights. The additional attribute characterizes the insights. The additional attribute includes at least user feedback including a user ranking of a ranked subset of the insights from a user. The method also includes inputting the model vector into a trained insight machine learning model to obtain a predicted ranking of the insights. The method also includes selecting, based on the predicted user ranking, a pre-determined number of insights to form predicted relevant insights. The method also includes reporting the predicted relevant insights.