Data Transfer Context Summaries from Metadata and Device Data
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Solution Overview
Problem
Existing data transfer records are often high-level, non-intuitive, and easily lost, leading to inaccurate record keeping and difficulty in detecting fraudulent or erroneous transfers.
Innovation Solution
A system utilizing trained machine learning models, including generative artificial intelligence, to generate context summaries by collecting metadata and device data from various sources, such as location, calendar, and email data, and generating a natural language explanation of the data transfer context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data transfer records are used, then the record keeping process is simple, but the records become high-level, non-intuitive, and easily lost
Solution Approach 1:
The system performs preliminary actions by automatically collecting metadata and device data (location, calendar, email) before the data transfer occurs, and generates context summaries in advance. This preliminary data gathering and processing ensures that when records are reviewed later, comprehensive contextual information is already available, improving record keeping accuracy without requiring complex manual processes at the time of transfer.
Solution Approach 2:
The machine learning model acts as an intermediary between raw data transfer records and human understanding. It processes structured data (metadata, device data) and transforms it into intuitive context summaries in natural language. This intermediary layer bridges the gap between simple record keeping and comprehensive information retrieval, maintaining system simplicity while improving accuracy.
2Difficulty of detecting and measuring
If detailed context information is collected and processed, then fraudulent or erroneous transfers become easier to detect, but the processing time and computational resources increase
Solution Approach 1:
The system collects and processes contextual data (location, calendar, email metadata) in advance before fraud detection is needed. By preparing this data beforehand and storing it associated with data transfer records, the system enables rapid fraud detection without requiring time-consuming analysis at the moment of detection. The preliminary processing separates the detection task from the data collection task, reducing real-time processing requirements.
Solution Approach 2:
The system extracts only the necessary contextual information relevant to fraud detection from the available data sources. Instead of processing all possible data, it selectively collects and processes metadata and device data that are most indicative of fraudulent behavior (such as location anomalies, timing patterns, communication context). This extraction approach maintains strong fraud detection capability while minimizing processing time and computational resources.
3Ease of operation
If machine learning models are used to generate context summaries, then the records become more intuitive and comprehensive, but the technology complexity and data requirements increase
Solution Approach 1:
The machine learning model serves as an intermediary that translates complex structured data (metadata, device data) into simple natural language summaries that are easy to understand. This intermediary transformation layer handles the technology complexity internally while presenting simplified, intuitive records to the user. The model learns patterns from training data and automatically generates human-readable summaries without requiring users to understand the underlying complexity.
Solution Approach 2:
The system creates a simplified copy or representation of the complex data transfer context in the form of natural language summaries. Instead of presenting raw structured data that requires technical understanding, it generates summarized versions that capture the essential information in an intuitive format. This copying approach maintains comprehensive information while presenting it in an easily operable form, hiding the technology complexity behind the simplicity of the summary output.
Data Source
AI summary
The present disclosure relates to systems and methods for generating summaries about context of data transfers using trained machined learning models. There is provided a computer system, comprising a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores instructions that, when executed, configure the processor to receive an indication to view a record of a data transfer on a device, collect metadata associated with the data transfer and device data associated with the data transfer from the device, generate a context summary of the data transfer based on the metadata and the device data using a trained machine learning model, and transmit a signal to the device to display the context summary in association with the record of the data transfer.