DESC:DETAILED DESCRIPTION OF THE INVENTION:
Some embodiments of the present invention, illustrating all its features, may now be discussed in detail. The words "comprising "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise, although any methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present invention, wherein the exemplary methods are described. The disclosed embodiments are merely exemplary of the disclosure of the present invention, which may be embodied in various forms.
It may be understood by all readers of this written description that the example embodiments described herein and claimed hereafter may be suitably practiced in the absence of any recited feature, element, or step that is or is not, specifically disclosed herein. For instance, references in this written description to "one embodiment," "an embodiment," "an exemplary embodiment," and the like, indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. The disclosed embodiments are merely exemplary of various forms or combinations. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one of ordinary skill in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

No terminology in this application should be construed as indicating any non-claimed element as essential or critical. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate example embodiments and does not pose a limitation on the scope of the claims appended hereto unless otherwise claimed.

Recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. Where a specific range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is included therein. All smaller sub-ranges are also included. The upper and lower limits of these smaller ranges are also included therein, subject to any specifically excluded limit in the stated range.

The present invention relates to an AI-powered decision support system (100) developed to assist Original Equipment Manufacturers (OEMs) in enhancing their competitive strategies and planning new product development efficiently. The system addresses key limitations in existing tools such as lack of real-time adaptability, absence of prescriptive insights, and fragmented data integration. With the industry undergoing transformative changes driven by electric vehicles (EVs), autonomous driving, and dynamic consumer preferences, there is a critical need for a comprehensive, scalable, and adaptive analytics platform that supports OEMs and Tier-1 suppliers in making faster, data-driven decisions.

To achieve this objective, the invention utilizes an advanced AI-driven architecture that integrates natural language processing (NLP), predictive and prescriptive analytics, market simulation, and interactive visualization. Users input desired vehicle features, target markets, and budget constraints through a web-based or mobile User Interface Module (101) using natural language. These inputs are parsed by an Input Processing Engine (102), which uses transformer-based NLP models for entity recognition and semantic parsing to convert unstructured language into structured data formats such as JSON or XML.

The structured input is then passed to a Data Integration Layer (103), which aggregates real-time and historical data from various internal and external sources, including ERP systems, CRM databases, IoT devices, customer reviews, competitor benchmarks, market trends, and cost structures. These datasets are continuously updated through streaming frameworks like Kafka, ensuring that the system operates with the latest market intelligence. Once integrated, the data is processed by the Predictive Analytics Engine (104), which utilizes advanced forecasting models such as LSTM networks and gradient-boosted trees to predict product success rates, market demand, and customer response.

Complementing the predictive engine, the Prescriptive Analytics Engine (105) applies optimization techniques including genetic algorithms and constraint programming to recommend optimal feature sets, pricing models, and go-to-market strategies. These strategies are further refined by a Recommendation Engine (108), which tailors suggestions based on the OEM’s specific constraints, risk tolerance, and desired market positioning. The insights generated from these analyses are presented to the user via the Interactive Visualization Layer (106), which leverages industry-standard tools like Tableau and Power BI to create dynamic dashboards. These dashboards visualize performance forecasts, KPIs, competitive benchmarks, and strategic options in an intuitive and actionable format.

A Feedback and Learning Module (107) plays a crucial role in enhancing the system’s intelligence over time. It captures user decisions, market reactions, and post-launch performance data to retrain the underlying machine learning models. This ensures continuous improvement and contextual adaptability, enabling the system to stay relevant in rapidly evolving markets. A Market Simulation Module (109) supports modelling of competitive responses and consumer behaviour under various future scenarios, allowing OEMs to test assumptions and evaluate alternative strategies before implementation.

In addition, the Scenario Testing & Simulation Module (110) enables users to simulate multiple product launch scenarios by adjusting input parameters and constraints, helping them assess risks and opportunities. To ensure seamless data exchange and interoperability with enterprise environments, the API Integration Layer (111) supports secure communication protocols such as RESTful and SOAP APIs. The system also incorporates a Security and Compliance Module (112) that guarantees end-to-end encryption, role-based access control, and adherence to global standards including GDPR and ISO 27001.

Operationally, when a user submits a product concept—for example, a compact EV targeted at urban markets with a specified budget—the Input Processing Engine (102) extracts structured information and the system cross-references this with market and competitor datasets via the Data Integration Layer (103). Predictive models forecast market performance, and the Prescriptive Analytics Engine (105) suggests optimal configurations and timings. These insights are visualized through the Interactive Dashboard (106), where users can simulate scenarios, compare strategies, and make informed decisions. Post-launch results are fed into the Feedback and Learning Module (107), which updates forecasting models to improve precision and relevance.

This invention offers numerous advantages. It delivers real-time, actionable insights that shorten product planning cycles and reduce strategic risk. Its modular and scalable architecture allows seamless expansion, while the use of interactive visualizations enhances strategic clarity. The continuous learning capability ensures sustained performance improvement, and secure data exchange maintains compliance and data integrity. Commercially, the system serves as a cost-effective alternative to high-end consulting services and rigid enterprise software, making advanced strategic planning accessible to mid-size OEMs and suppliers.

A key limitation is that the accuracy of AI-driven recommendations depends on the quality and completeness of input data. To mitigate this, the system includes intelligent prompting mechanisms that request additional context when needed and uses a robust feedback loop to continuously refine its models. In practice, this decision support system can be deployed across strategic planning, new product design, pricing optimization, market entry analysis, and lifecycle management activities in the industrial sector.

The invention thereby empowers OEMs and Tier-1 suppliers with a competitive edge through an intelligent, real-time, and user-centric analytics platform that bridges the gap between descriptive data and strategic action.
,CLAIMS:CLAIMS:
WE CLAIM:
1. A decision support system (100) for original equipment manufacturers (OEMs), comprising:
a. A User Interface Module (101) configured to accept user inputs in natural language;
b. An Input Processing Engine (102) configured to parse the natural language input into structured data using transformer-based natural language processing models;
c. A Data Integration Layer (103) configured to aggregate internal and external datasets including market trends, competitor data, and customer behavior;
d. A Predictive Analytics Engine (104) configured to generate forecasts using time series models and machine learning algorithms;
e. A Prescriptive Analytics Engine (105) configured to generate optimized product strategies using constraint programming and genetic algorithms;
f. A Recommendation Engine (108) configured to generate actionable guidance based on predictive and prescriptive insights;
g. An Interactive Visualization Layer (106) configured to present forecasts and strategies through dashboards;
h. A Feedback and Learning Module (107) configured to retrain models based on post-launch outcomes and user interaction;
i. A Market Simulation Module (109) configured to model competitive responses and simulate future market dynamics based on current inputs and trend projections; and
j. a Scenario Testing and Simulation Module (110) configured to enable users to simulate multiple product launch scenarios by adjusting input parameters and evaluating resulting risks and opportunities.
2. The decision support system of claim 1, wherein the Input Processing Engine (102) performs semantic parsing and entity recognition using transformer-based NLP models to extract structured parameters such as product features, target market, and budget.
3. The decision support system of claim 1, wherein the Data Integration Layer (103) aggregates data from ERP systems, CRM platforms, IoT sensors, and external market databases in real time using streaming frameworks including Apache Kafka.
4. The decision support system of claim 1, wherein the Predictive Analytics Engine (104) employs long short-term memory (LSTM) neural networks and gradient-boosted decision trees to estimate market demand, pricing sensitivity, and customer acceptance for new vehicle models.
5. The decision support system of claim 1, wherein the Prescriptive Analytics Engine (105) applies optimization algorithms including genetic algorithms and constraint satisfaction techniques to determine ideal feature combinations, pricing strategies, and go-to-market timing.
6. The decision support system of claim 1, wherein the Interactive Visualization Layer (106) uses business intelligence tools including Tableau and Power BI to dynamically present performance forecasts, competitive benchmarks, and key performance indicators (KPIs).
7. The decision support system of claim 1, further comprising an API Integration Layer (111) configured to enable secure data exchange with external platforms through RESTful or SOAP APIs.
8. The decision support system of claim 1, further comprising a Security and Compliance Module (112) configured to ensure data encryption, role-based access control, and compliance with industry standards including GDPR and ISO 27001.