GenAI Recommendations Using Real-Time Vectors for Release Planning
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Solution Overview
Problem
The product development lifecycle faces challenges such as fragmented approaches, disjointed customer and internal data handling, complex requirement analysis, and difficulty in predicting software application lifespan, leading to increased time and cost, and inefficient bug correction.
Innovation Solution
A Generative Artificial Intelligence (GenAI) model is used to analyze complexity, change, and customer impact in real-time, generating recommendations based on internal and external data for product release planning, including sentiment analysis, competition analysis, and internal KPIs like deployment success and defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fragmented approach is followed in product development lifecycle, then specialized handling of requirements is achieved, but collaboration efficiency deteriorates and time consumption increases
Solution Approach 1:
The patent merges previously siloed requirements into a unified product requirements document generated by the GenAI model. The model integrates system requirements, infrastructure requirements, availability requirements, usability requirements, support requirements, and training requirements into a single cohesive document, eliminating the fragmented approach while maintaining specialized handling through structured sections for each requirement type.
Solution Approach 2:
The GenAI model serves multiple functions simultaneously: it acts as a requirements analyst, collaborator, and document generator. The single model handles diverse requirement types (system, infrastructure, availability, usability, support, training) and produces a comprehensive document that serves both development and operations teams, replacing multiple specialized processes with one universal solution.
2Loss of information
If output documents contain all product information, then comprehensive documentation is achieved, but understanding accuracy deteriorates due to misunderstandings and incorrect implementations
Solution Approach 1:
The GenAI model applies local quality by providing detailed, specific information for each requirement type rather than generic comprehensive documentation. Each section (system requirements, infrastructure requirements, availability requirements, etc.) contains targeted details appropriate to that domain, improving understanding accuracy while maintaining comprehensiveness through structured organization.
Solution Approach 2:
The model incorporates feedback mechanisms by analyzing input data from multiple sources (customer feedback, competition analysis, internal KPIs) and using this feedback to generate accurate, context-aware requirements. The iterative generation process allows for refinement based on feedback, ensuring both comprehensive coverage and precise understanding.
3Adaptability or versatility
If product lines become complicated, then feature complexity increases, but bridging gap between developers and operations team becomes difficult
Solution Approach 1:
The patent segments complicated product line requirements into distinct, manageable categories (system requirements, infrastructure requirements, availability requirements, usability requirements, support requirements, training requirements). This segmentation makes complex information easier to understand and communicate across different teams while maintaining the full scope of product complexity.
Solution Approach 2:
The GenAI-generated requirements document serves as an intermediary between developers and operations teams. It translates complex product line requirements into a common language that both teams can understand, bridging the communication gap caused by increasing product complexity and enabling coordinated collaboration.
4Adaptability or versatility
If process planning is insufficient, then development flexibility is maintained, but project time and cost increase
Solution Approach 1:
The GenAI model performs preliminary action by generating comprehensive requirements documentation before product development begins. This advance planning, based on analysis of input data including historical information and current requirements, establishes a solid foundation that guides subsequent development activities, reducing delays and rework while maintaining flexibility through structured yet adaptable requirements.
Data Source
AI summary
The disclosure relates to a method and system of visually inspecting computational geometry code. The method may include receiving, from a user, a query associated with a subject data, and selecting, in real time, one or more relevant vectors associated with subject data from a plurality of vectors associated with the subject data, based on the query. The method may further include inputting vectors associated with the query along with the one or more relevant vectors associated with subject data based on the query, to a Generative Artificial Intelligence (GenAI) model, and receiving, from the GenAI model, recommendations corresponding to the vectors associated with the query and the one or more relevant vectors associated with subject data based on the query inputted to the GenAI model.