Job Segmentation Taxonomy for Real-Time Need Identification
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Solution Overview
Problem
Existing systems fail to provide accurate and timely product roadmap investment decisions and risk assessments due to the complexity and volume of information from diverse sources, lacking a systematic way to gather, organize, and structure data in real-time, leading to manual processes and guesswork that often results in failed product roadmaps.
Innovation Solution
The system employs a language model and data taxonomy to identify job steps and unmet needs, utilizing a server to gather, classify, and structure data from various sources, forming a taxonomy that enables real-time product roadmap investment decisions and risk assessments through statistical models and user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes and guesses are used for product roadmap decisions, then implementation simplicity is maintained, but decision accuracy and timeliness deteriorate
Solution Approach 1:
The patent segments the complex decision-making process into distinct functional modules: data gathering mechanism, data classification model, taxonomy formation, statistical model analysis, and user interface presentation. Each module handles a specific aspect of information processing, transforming the overwhelming complex task into manageable segmented operations that collectively achieve high decision accuracy
Solution Approach 2:
The patent introduces a taxonomy of data as an intermediary structure that bridges raw diverse data and decision-making analysis. The taxonomy serves as a structured intermediary framework that organizes gathered data into meaningful categories and relationships, enabling the statistical models to process information systematically and produce accurate investment decisions and risk assessments
2Loss of time
If real-time data gathering and processing is implemented, then decision timeliness is improved, but system complexity and resource requirements worsen
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the taxonomy structure and classification frameworks before actual decision-making occurs. The data classification models and taxonomy relationships are prepared in advance, so when real-time data arrives, it can be quickly mapped into the existing structured framework without requiring complex real-time processing of classification logic
Solution Approach 2:
The patent implements continuous data gathering mechanisms that operate relentlessly to collect relevant data from multiple sources. The system maintains continuous processing pipelines where data flows continuously through classification and analysis stages, eliminating idle time and ensuring that decision-making information is always up-to-date without requiring periodic batch processing interruptions
3Loss of information
If comprehensive data from multiple sources is gathered, then information completeness is improved, but data processing complexity and time loss worsen
Solution Approach 1:
The patent extracts only the essential and relevant features from the comprehensive gathered data that are necessary for investment decision and risk assessment. The classification models identify and extract key attributes and relationships from the full data set, filtering out redundant information while preserving the critical elements needed for accurate analysis, thus reducing processing time without sacrificing information completeness
4Reliability
If systematic data structuring and taxonomy formation are implemented, then decision quality is improved, but system complexity and development effort worsen
Solution Approach 1:
The patent creates a universal taxonomy structure and classification framework that can be applied across different product roadmap scenarios, industries, and data types. The systematic data structuring approach is designed to be multi-functional, handling various kinds of data sources and decision contexts with the same underlying framework, which reduces implementation complexity by avoiding the need to build custom structuring solutions for each specific case
Data Source
AI summary
Systems and methods for job segmentation and identification of unmet needs are provided. An example method includes receiving, from a user, identification of a job to be performed. The method also includes obtaining a set of job steps corresponding to segmentation of the job to be performed and obtaining a set of product features for a product designed to assist with completion of the job to be performed. The method further includes assigning each product feature of the set of product features to a corresponding job step of the set of job steps, assigning respective effort scores to the set of job steps, assigning respective measures of importance to the set of product features based on the respective effort scores, and causing the set of product features to be presented to the user with the respective measures of importance.