Semi-Automated Data Transformation via Contextual UI
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Solution Overview
Problem
Traditional data transfer between applications/services is inefficient due to lack of synergy and requires manual intervention, as current AI solutions struggle to accurately prioritize and transform unstructured data, failing to analyze content contextually for effective presentation.
Innovation Solution
A system and method for semi-automated data transformation that uses an improved user interface to generate and apply data transformation suggestions, leveraging machine learning to evaluate and rank content for importance, and automatically generate presentation documents across different applications/services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual data transfer methods are used between applications/services, then users can control and direct the data movement process, but user productivity decreases and processing burden increases due to numerous manual operations required
Solution Approach 1:
The system enables self-service by allowing the destination application/service to automatically request and receive data from the source application/service without requiring manual user intervention. The destination service publishes data requirements and the source service automatically fulfills them, eliminating the need for users to perform numerous manual copy-paste operations while maintaining control through automated negotiation protocols.
2Productivity
If current AI solutions are used to automate data transfer, then processing efficiency improves, but data transformation accuracy deteriorates because AI cannot accurately determine content importance and prioritize content for transformation
Solution Approach 1:
The patent introduces an intermediary component that acts as a bridge between the source and destination services. This intermediary analyzes the data transformation requirements, determines content importance based on contextual understanding, and prioritizes which content should be transformed and transferred. It coordinates the negotiation between services to ensure accurate content selection and transformation while maintaining processing efficiency.
3Adaptability or versatility
If traditional copy/paste operations are used for content transfer, then simplicity is maintained, but contextual analysis capability is lost as the system cannot identify related content or enhance content for presentation in the destination document
Solution Approach 1:
The system performs preliminary actions by pre-analyzing the destination document's context, requirements, and structure before the actual data transfer occurs. The destination service publishes its data requirements in advance, allowing the source service to pre-process and prepare appropriate content. This preliminary contextual analysis enables the system to identify related content, determine appropriate transformations, and enhance content for presentation without adding significant complexity during the actual transfer operation.
Data Source
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AI summary
The present disclosure relates to systems and methods that are configured to semi-automate data transformation processing so that content can be transformed, from one type of electronic document, for presentation in another type of electronic document. For instance, data transformation suggestions, for transforming content from a first form into a presentation form (second form), may be generated and presented to a user through an improved user interface of an application/service that is used to display the first form of the content. The improved user interface provides a new user interface menu to manage the data transformation suggestions and/or export/import processing of data from one type of electronic document to another. Based on user selection of the confirming data transformation suggestions through the user interface menu, a presentation document is automatically generated on behalf of the user, for example, in a different application/service.