Expert Recommendation System for Product Design
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Solution Overview
Problem
Large companies face difficulties in identifying and accessing subject matter experts due to their size, making it a long and frustrating process for users to find relevant expertise, as existing search systems often provide inaccurate or unfocused results.
Innovation Solution
A system that analyzes a product design description to determine relevant terms, compares them to stored records, and outputs a subset of relevant records and associated contacts with potential knowledge, automatically providing users with recommended contacts and records relevant to the design, thereby streamlining the process of finding subject matter experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually searches for subject matter experts in a large company, then they may find relevant expertise, but the process becomes long and frustrating with inaccurate or unfocused results
Solution Approach 1:
The patent introduces an intermediary system that automatically analyzes product design descriptions, extracts relevant terms, compares them against stored records, and generates recommended contacts. This intermediary eliminates the need for users to manually search through company directories, providing both accurate and time-efficient expert identification by acting as a mediator between the user's information need and the company's expertise database.
2Adaptability or versatility
If the company grows larger to handle more projects, then more expertise is available, but it becomes harder for users to know who has worked on related projects
Solution Approach 1:
The system enables self-service by automatically performing the expert identification task without requiring users to manually navigate company structures. The system takes the product design description as input, autonomously analyzes it, queries the stored records, and returns relevant expert contacts, allowing users to obtain expert information easily regardless of company size.
Solution Approach 2:
The system performs preliminary action by pre-storing project records and expertise information in a structured database before they are needed. When a user needs expert information, the system has already organized the data by project, skills, and contacts, enabling rapid retrieval and comparison against the current product design description without requiring users to search through unorganized company data.
3Reliability
If users need to search for subject matter experts with specific skills, then they can find relevant expertise, but the search process is frustrating and most users do not even try
Solution Approach 1:
The system acts as an intermediary that handles the complex task of matching skills and expertise by analyzing product design descriptions, extracting key terms, and comparing them against stored project records and contact information. This intermediary process ensures reliable expert matching while keeping the user interface simple, as users only need to provide the product design description rather than navigate complex search parameters.
Data Source
AI summary
In one embodiment, a description of a product design is received. The description is analyzed to determine terms describing the product design. The determined terms are compared to a plurality of stored records. A subset of the stored records is determined based on the comparison. The stored records are considered relevant to the description of the product design. A set of recommended contacts associated with the subset of stored records is then determined. The recommended contacts are considered to have knowledge relevant to the description of the product design. At least one of the subset of stored records and the set of recommended contacts is output. For example, at least one of the subset of stored records and the list of recommended contacts may be displayed on an interface that is being used to input the description of the product design.