Method and system for artificial intelligence assisted content lifecycle management
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Solution Overview
Problem
Conventional content lifecycle management techniques face challenges in standardizing and effectively decoding, interpreting, and encoding natural language data due to variations in authorship, leading to ineffective communication and high resource investments.
Innovation Solution
An AI-assisted system that vectorizes inquiries, identifies topics with sentiment values, aggregates information from a data lake, and generates responses with recommended actions, enabling automated content restructuring and authoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional content lifecycle management techniques are used, then content can be managed, but standardizing content is difficult due to variations in authorship and natural language data
Solution Approach 1:
The patent introduces an AI-based intermediary system that mediates between diverse natural language content sources and standardized content representations. The AI models decode, interpret, and encode varied authorship styles into unified content structures, enabling standardization without requiring complex manual processes for each variation type.
Solution Approach 2:
The system transforms content parameters by converting natural language data into structured formats through AI processing. By changing the representation parameters of content from unstructured text to standardized data structures, the system achieves consistency across different authorship styles while maintaining content meaning.
2Productivity
If conventional techniques are used, then content can be processed, but decoding, interpreting, and encoding require heavy investments in knowledge, time, and resources
Solution Approach 1:
The AI system performs self-service processing of content by automatically decoding, interpreting, and encoding natural language data without requiring extensive human intervention. The models independently handle the complex transformations that would otherwise demand significant time and expert knowledge investments.
Solution Approach 2:
The patent replaces manual mechanical processes of content decoding and encoding with AI-based automated systems. This substitution eliminates the need for heavy human resource investments while maintaining or improving processing efficiency through intelligent algorithms that handle content transformations autonomously.
3Reliability
If conventional techniques are used, then content can be managed, but ineffective communication is prevalent due to variabilities in natural language data
Solution Approach 1:
The AI intermediary acts as a communication bridge that translates varied natural language expressions into consistent standardized representations. This mediation ensures reliable communication by eliminating ambiguities and inconsistencies arising from different authorship styles and language variations.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously refine content standardization based on processing results and quality metrics. By monitoring communication effectiveness and adjusting AI models accordingly, the system improves reliability while adapting to handle natural language variations more effectively.
Data Source
AI summary
A method for facilitating content lifecycle management via artificial intelligence for AI assisted authoring, AI assisted editing, and phased AI on AI recursive authoring and editing is disclosed. The method includes receiving, via an application programming interface, inquiries in a natural language format, each of the inquiries including freeform data; vectorizing the inquiries to generate numeric sequences; identifying, by using a model, topics for each of the inquiries based on the corresponding numeric sequences, each of the topics including a subject matter value and a sentiment value; aggregating information that corresponds to the topics from various sources, the sources including a preconfigured data lake; determining, by using the model, solutions in the natural language format for each of the inquiries based on the aggregated information, the solutions including recommended actions based on a predetermined setting; and generating, by using the model, a response that includes the solutions.