ML Embedding for Patent Message Identification
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Solution Overview
Problem
In group-based communication systems, users often fail to identify patent-relevant messages due to lack of technical or legal expertise, and these messages may be scattered across multiple channels, hidden by visibility or privacy settings, leading to missed valuable patent ideas within organizations.
Innovation Solution
A machine learning model with an embedding function is used to create an embedding space for patent applications and compare it with message embeddings, identifying patent-relevant messages by determining similarity, and providing indications to users for review, with feedback loop for model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review messages to identify patent-relevant content, then identification accuracy may be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical review with an automated machine learning system that uses embedding functions and similarity comparison algorithms to identify patent-relevant messages, eliminating time-consuming human review while maintaining identification accuracy
Solution Approach 2:
The system enables self-service automated identification where the machine learning model independently analyzes messages, generates similarity scores, and surfaces potential patent-relevant content without requiring human intervention for initial screening
2Loss of information
If users manually search across multiple channels for patent ideas, then comprehensive identification may be achieved, but operational complexity and time loss increase
Solution Approach 1:
The machine learning system provides universal multi-channel analysis capability, automatically scanning and analyzing messages across all organization channels simultaneously through a single unified interface, eliminating the need for users to manually navigate multiple channels
3Reliability
If privacy settings restrict message visibility, then organizational security is maintained, but patent-relevant information becomes inaccessible
Solution Approach 1:
The machine learning system acts as an intermediary that processes messages within their existing privacy contexts, analyzing content without requiring users to change visibility settings, thus maintaining security while enabling patent-relevant information discovery through automated analysis
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, users may post messages discussing potentially patentable concepts. To support automatic identification of patent-relevant messages within the group-based communication system, the system may use a machine learning model including at least an embedding function. The system may generate a set of features for one or more messages and may input the set of features into the machine learning model. Using the embedding function, the model may create a message embedding and may compare the message embedding with embeddings of patent application language to determine a level of similarity. The machine learning model may output an indication of whether the one or more messages are associated with a patentable concept based on the embedding, and the indication may be surfaced to a user associated with managing patents for an organization of the group-based communication system.