Output Representation Generator With ML Template Mapping
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Solution Overview
Problem
The process of generating digital representations for marketing purposes is tedious and time-consuming due to the need for extensive manual effort in template searching and optimization, and automated methods face challenges in interpreting complex input formats and requiring manual annotation.
Innovation Solution
A system utilizing a processor with a representation generator, parsing engine, mapping engine, and machine learning model to automatically generate an output representation by mapping parsed input data with pre-stored base templates, enhanced by a self-learning engine for optimization and a module detector for automated module detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual template searching and optimization is performed, then representation quality is improved, but time consumption and labor effort increase significantly
Solution Approach 1:
The system performs self-service by automatically searching, selecting, and optimizing templates without human intervention. The automated template selection system evaluates multiple templates and selects the best match based on predefined criteria, while the optimization module automatically adjusts template parameters to meet representation requirements, eliminating the need for manual template searching and optimization efforts.
Solution Approach 2:
The patent replaces the mechanical manual process of template searching and optimization with an automated computational system. The template selection algorithm automatically evaluates numerous templates against input data characteristics, and the optimization module automatically adjusts parameters, substituting human cognitive and manual operations with automated computational processes that achieve similar or superior results faster.
2Loss of time
If automated representation generation is implemented, then time consumption is reduced, but interpretation accuracy of complex input formats deteriorates
Solution Approach 1:
The system segments the input data into distinct components and processes each segment through specialized parsing modules. The template selection algorithm separately analyzes different aspects of input formats, and the optimization module handles specific parameter adjustments independently. This segmentation allows accurate interpretation of complex formats by breaking them down into manageable components that can be processed systematically.
Solution Approach 2:
The patent introduces an intermediary parsing and processing layer between the raw input data and the template selection/optimization processes. This intermediary module translates complex input formats into standardized internal representations, acting as a mediator that bridges the gap between diverse input formats and the automated generation system, thereby maintaining interpretation accuracy while enabling automation.
3Manufacturing precision
If manual optimization and annotation are performed, then representation accuracy is improved, but labor cost and complexity increase
Solution Approach 1:
The system performs self-service optimization by automatically adjusting template parameters and generating representations without requiring manual annotation. The optimization module independently evaluates generated representations against predefined accuracy criteria and automatically iterates on parameter adjustments, achieving high representation accuracy while eliminating the complexity of manual optimization processes and reducing labor costs.
4Manufacturing precision
If extensive manual effort is invested in template searching and optimization, then output representation quality is improved, but productivity decreases
Solution Approach 1:
The system maintains continuous useful action by running automated template evaluation and optimization processes without interruption. The automated system continuously generates, evaluates, and refines representations in a continuous loop, eliminating the start-stop nature of manual processes. This continuous automated operation achieves both high representation quality and high productivity by maintaining steady progress without the time losses associated with manual intervention.
Data Source
AI summary
Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.