Ontology-Based Needs Processing Engine for Strategy Formulation
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Solution Overview
Problem
Current business information systems lack a unifying ontology, leading to subjective evaluations and inefficient decision-making due to the inability to accurately predict sustainable market trends and the impact of new ideas, resulting in many mistaken beliefs and failures in business strategy formulation.
Innovation Solution
The implementation of a universal strategy and innovation management system (USIMS) that utilizes the Outcome-Driven Innovation (ODI) methodology to define customer needs, which involves a set of rules for creating need statements and a system that gathers and formulates needs electronically, ensuring universal acceptance and relevance, and minimizes the time and expense associated with market research.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional MIS and BI systems are used to track sales results and demographics, then data retrieval and reporting efficiency is improved, but the ability to provide empirically defensible predictions and transform management decision making deteriorates
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between raw business data and decision-making processes. This ontology provides a standardized framework for representing business concepts, relationships, and rules, enabling consistent interpretation and empirically defensible predictions across different data domains while maintaining efficient data retrieval capabilities
Solution Approach 2:
The ontology serves multiple functions simultaneously: it acts as a data model, a semantic framework, a prediction engine, and a decision-support tool. This universal framework enables the system to handle diverse business data types (sales, demographics, market trends) while providing consistent analytical capabilities for prediction and decision-making
2Adaptability or versatility
If each information domain operates as an independent island with local rules, then domain-specific analysis flexibility is improved, but integration and holistic business insight deteriorates
Solution Approach 1:
The patent merges multiple independent information domains into a unified ontological framework. The ontology defines standardized classes, properties, and relationships that allow data from different domains (sales, marketing, operations) to be integrated while preserving domain-specific characteristics through hierarchical structuring and local rule definitions
3Loss of information
If market research is conducted using traditional methods to identify customer needs, then comprehensive market understanding is improved, but time and expense associated with research increases
Solution Approach 1:
The patent performs preliminary structuring of market research through the ontology framework before actual data collection. The ontology pre-defines the conceptual structure, relationships, and analytical models needed for market analysis, allowing research to focus on populating and validating the framework rather than creating analytical structures from scratch, thereby reducing time and expense while maintaining comprehensiveness
Data Source
AI summary
A mechanism is disclosed that dramatically minimizes the time it takes to gather needs, dramatically minimizes the expense it takes to gather those needs, and ensures those statements are formulated in manner that comply with a set of rules designed to ensure the right inputs are used in downstream strategy formulation, marketing, product development, and related company workflows. In addition, the mechanism may or may not minimize the time it takes for a company to acquire the capability to uncover these needs statements.