Solicitation Response System Using Tagged Content Retrieval
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Solution Overview
Problem
Conventional solicitation response processes for projects and service purchases are labor-intensive and inefficient, relying heavily on manual efforts and often using outdated proposals, which can lead to inaccuracies due to differences in requirements between previous and new proposals.
Innovation Solution
A web-based software system that analyzes digital content in solicitations, automatically identifies capable parties matching the requirements, and generates responsive documents through a user interface, enabling efficient proposal generation and teaming member matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual efforts are used for proposal drafting, then flexibility and customization are improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-tagging and organizing content from previous proposals into reusable segments. When a new solicitation arrives, the system automatically retrieves and assembles relevant pre-prepared content, eliminating the need to manually draft proposals from scratch while maintaining customization through user interaction with the generated draft.
2Productivity
If previous proposals are reused to generate new proposals, then productivity is improved, but measurement precision deteriorates due to requirement differences
Solution Approach 1:
The system applies local quality by tagging specific segments of previous proposals with metadata indicating which solicitation requirements they address. When generating a new proposal, the system selectively retrieves and assembles only the locally relevant tagged segments that match the specific requirements of the new solicitation, ensuring precision while maintaining productivity.
Solution Approach 2:
The system incorporates feedback mechanisms where users can review, edit, and provide feedback on the automatically generated proposal draft. This feedback loop allows the system to learn from user corrections and improve its matching accuracy over time, ensuring that reused content precisely meets the new solicitation's requirements.
3Productivity
If automated document generation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional platform that handles multiple tasks: storing and tagging content from previous proposals, analyzing new solicitations, retrieving relevant segments, assembling draft proposals, and allowing user editing. This single unified system performs all these functions through a common architecture, avoiding the need for multiple separate systems and reducing overall complexity while maintaining high productivity.
Data Source
AI summary
In one embodiment, a system for generating a document includes one or more processors, and a non-transitory computer-readable medium storing instructions that cause the one or more processors to display, in an electronic display, a graphical user interface comprising a prior document section, a word processing section, and a tag query text field, receive query text from the tag query text field, retrieve one or more documents from a database having a tag corresponding to the query text, display in the prior document section a section of the one or more documents associated with the tag, receive a request to include text from the one or more documents in the prior document section in the document displayed in the word processing section, and populate the document displayed in the word processing section with the text.