Collaborative IP Project Analytics for Novelty and Contribution Scoring
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Solution Overview
Problem
Existing systems lack efficient methods for evaluating patent claim inputs and authorized contributions within collaborative intellectual property projects, particularly in determining novelty and compensation, while addressing potential conflicts and overlaps.
Innovation Solution
A computer-implemented method using artificial intelligence to analyze patent claim inputs, evaluate authorship conditions, and determine patentability profiles, including novelty likelihood and participant contributions, while managing authorized contributions and compensation based on predefined criteria and databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of patent claims and contributions is performed, then accuracy in determining novelty and compensation can be maintained, but productivity and efficiency of collaborative IP projects deteriorate
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between patent claims and the evaluation process. This system uses natural language processing and machine learning models to automatically analyze patent claims, compare them against prior art databases, and determine novelty and compensation metrics, thereby maintaining accuracy while significantly improving productivity
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated computational systems. Machine learning models and algorithms substitute human reviewers, enabling high-volume processing of patent claims while maintaining consistent evaluation standards and accuracy through trained predictive models
2Productivity
If automated systems are used to evaluate patent claims, then productivity improves, but measurement precision and accuracy in determining novelty deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive patent databases and prior art collections before actual evaluation. This pre-processing phase enables the automated system to achieve high accuracy by learning from historical data, ensuring that productivity gains do not compromise measurement precision
Solution Approach 2:
The patent implements feedback mechanisms where the automated evaluation system continuously refines its predictions based on outcomes and corrections. This feedback loop allows the system to improve its accuracy over time while maintaining high productivity, as the learned patterns are applied to subsequent evaluations
3Measurement precision
If comprehensive databases of prior work are searched to determine novelty, then measurement precision improves, but loss of time and processing duration increase
Solution Approach 1:
The patent segments the comprehensive prior art database into organized categories and subsets. The evaluation system then queries only relevant segments based on the specific patent claim being evaluated, rather than searching the entire database. This segmentation maintains measurement precision by ensuring thorough search of relevant areas while reducing overall processing time
Solution Approach 2:
The system performs partial searching by initially querying a subset of the database that is most likely to contain relevant prior art based on keyword matching and classification. Only if initial results are inconclusive does the system expand to more comprehensive searching, thus balancing time investment with accuracy requirements
4Quantity of substance
If multiple authorized participants contribute to a project, then quantity of contributions increases, but difficulty of detecting and measuring individual contributions and determining compensation increases
Solution Approach 1:
The patent creates digital copies or representations of each participant's contributions in a structured format. These digital replicas capture the essence, scope, and impact of each contribution, enabling automated comparison and evaluation. This copying approach allows the system to handle multiple participants efficiently while maintaining precise measurement of individual contributions through standardized data structures
Data Source
AI summary
A system and method for collaborative creation and management of intellectual property (IP) projects is disclosed. The system includes a server configured to dynamically assign roles and permissions to participants based on predefined criteria. A graphical user interface (GUI) facilitates real-time collaboration, editing, and version control, while a semantic analysis engine ensures compliance with intellectual property standards and assesses content novelty. Predictive analytics generate metrics, such as patentability scores and inventorship overlap, to guide project development. Contributions are electronically tracked and stored in a secure, non-transitory computer-readable medium alongside generated metrics. The system supports role-specific recommendations, automated notifications, and integration with external data sources for enhanced functionality. Participants can visualize contributions, resolve conflicts, and simulate commercialization outcomes, enabling efficient project management and innovation. This invention improves the collaborative development process, ensuring compliance, enhancing productivity, and maximizing the value of intellectual property assets.