A system and method for ai-based evaluation and mapping innovations to emerging industry needs

Abstract

A computer-implemented system (1300) and method that enables collaboration between researchers, academic institutions, industries of all sizes and expert users to commercialize research innovations. The system includes a user registration module (1302) for onboarding role-specific users; a problem submission module (1304) for receiving technical challenges from industry; and a solution submission module (1306) for academic responses. An integrated AI engine (1308) comprises a semantic parsing module (1310) and an AI-powered evaluation module (1312) for performing novelty, inventiveness and market viability assessments. A patent matching engine (1314) semantically aligns problems with institutional IP. The collaboration matchmaking module (1318) recommends pairings based on domain compatibility. A commercialization automation module (1320) generates licensing drafts and manages revenue-sharing workflows. Access is regulated by a token-based access control module (1322). A structured workflow engine (1323) supports IP lifecycle transition, hosted on a cloud-based infrastructure (1324) ensuring secure, real-time collaboration. The system outputs downloadable commercialization insights.

Description

A SYSTEM AND METHOD FOR AI-BASED EVALUATION AND MAPPING INNOVATIONS TO EMERGING INDUSTRY NEEDSFIELD OF INVENTION

[0001] The present invention relates to a computer implemented system comprising a collaborative platform designed to bridge the gap between academic research, industry innovation, and individual researchers. . In particular, the present invention provides a system and method that utilizes artificial intelligence to assess innovations and strategically align them with the evolving demands and opportunities of emerging industries It aims to facilitate the transition of innovations to market-ready products, support industry challenges in innovation and provide opportunities for individual and academic researchers to advance their ideasBACKGROUND OF THE INVENTION

[0002] Innovation and commercialization of new technologies are critical for economic growth and societal advancement. However, several key challenges hinder the efficient transition of academic research to market-ready products, innovation within industries, and the advancement of individual researchers' groundbreaking ideas.

[0003] Academic research is often a rich source of innovation but lacks a direct pathway to market applicability. Many academic projects, while theoretically sound, fail to address market needs and customer demands, leading to underutilization and remaining merely theoretical. Researchers may also lack the resources and knowledge to navigate the commercialization process effectively.

[0004] Indian application IN202311057294A discloses a system aimed at mapping the industry-academia gap by collecting and analyzing data from both sectors to identify curricular disparities and skill mismatches. While it introduces important functions such as skills evaluation and collaborative communication platforms, it largely focuses on curriculum alignment and lacks mechanisms for deeper integration into the commercialization pipeline, such as IP evaluation or transaction workflows that facilitate market-readiness of academic innovations.

[0005] Similarly, IN201641010688A introduces a platform connecting IP creators, buyers, and investors, serving as a bridge between researchers and industries for commercial benefit. Although this invention promotes the exchange of academic research for industrial use, it remains limited to a marketplace framework without incorporating intelligent evaluation of the intellectual property, automated licensing workflows, or structured collaboration models informed by Al-driven insights. It does not address the nuances of patentability, inventiveness scoring, or personalized matchmaking based on historical collaboration data.

[0006] IN02024MU2012A outlines a student-centric social network that facilitates project sharing and collaboration between students, alumni, and industry professionals. This model is innovative in enhancing academic learning and mentorship, but is primarily designed for educational continuity and professional discovery rather than enabling structured commercialization or intellectual property transactions. The system lacks automation, IP intelligence, and a formalized route for industry-academic co-creation that results in productization.

[0007] Another prior art, IN202511004494A, offers a platform that emphasizes networking, team formation for hackathons, and career development through alumni and mentorship channels. Its core functionality is limited to event coordination and community-building. It does not provide Al-assisted tools for evaluating, matching, and commercializing academic research, nor does it incorporate institutional IP infrastructure or licensing automation features.

[0008] These prior inventions underscore the importance of bridging the gap between academia and industry, yet leave significant opportunities for technical advancement. Most existing systems focus either on education alignment, IP trading, or community engagement in isolation, and fall short in offering a cohesive, end-to-end technological solution that transforms academic research into market-ready innovations.

[0009] Hence, there is a need for a platform to overcome all the challenges in the research field. The present invention addresses this problem by providing a Market- Driven Approach, Collaborative Partnerships and Evaluation and Validation. It also addresses industrial research challenges by providing Access to Academic and individual Innovations in order to enable targeted collaboration with researchers, and streamline the commercialization of relevant intellectual property through Al-assisted evaluation, matching, and licensing workflows. The present invention aligns research, industry needsand individual creativity, transforming innovative ideas into viable, market-ready products that benefit society.SUMMARY OF THE INVENTION

[0010] The present invention is designed to create a collaborative ecosystem among academia, industry and individual researchers. It ensures that innovations from individuals, academies, and industries of all sizes are market-driven, assists industries in solving product challenges-and provides researchers with a marketplace to showcase their innovations. The platform integrates advanced Al tools for preliminary market analysis and novelty checks, enhancing the market relevance and potential impact of the innovations.

[0011] The present invention is a computer-implemented system for enabling collaboration among individuals, research institutions, industries of all sizes and expert users to commercialize research innovations through automated evaluation and workflow execution. The system comprises modules to facilitate user onboarding, problem and solution submissions, Al-driven evaluation of novelty and market viability, semantic matching of intellectual property, strategic collaboration matchmaking, and automated licensing and commercialization workflows.

[0012] A user registration module is configured to onboard users, each with rolespecific access to the platform; wherein the users include institutions, companies, verified mentors, domain experts and individual researchers. A problem submission module is configured to receive and standardize technical problem statements submitted by industry users. A solution submission module allows academic users to submit commercially-oriented responses to industry-defined problem statements, with the intent of facilitating productization and licensing opportunities.

[0013] An artificial intelligence engine is configured for semantic parsing and AI- powered evaluation. The Al-powered evaluation module performs novelty assessment, inventiveness scoring, and market viability estimation.

[0014] A patent matching engine is configured to semantically map industry-submitted problem statements against institutional intellectual property databases utilizing large language model-based vector embeddings, and rank results by relevance and prior commercialization outcomes. A collaboration matchmaking module is configured to recommend strategic pairings between academic institutions, researchers, andcompanies based on Al-inferred domain compatibility, historical collaboration success rates, and innovation readiness indicators

[0015] A commercialization automation module configured to generate draft licensing agreements, compute patent valuation metrics, and initiate structured revenue -sharing workflows between academic institutions and industry users, based on matched intellectual property assets, with audit-trail logging for each transaction step. A tokenbased access control module is integrated with the Al engine, configured to enable managing tiered usage of system features through virtual tokens. A structured workflow engine facilitates multi-stage transition of academic IP to market-ready solutions, with timestamped digital audit trails. A cloud-based infrastructure operable to provide realtime status updates, secure multi-user collaboration, and access control for role-based services, wherein the system is further configured to generate downloadable assessment reports containing novelty insights, inventiveness scoring, market viability ratings, and commercialization recommendations .

[0016] The commercialization automation module is configured to generate contract templates based on configurable licensing models and industry norms. The system assigns submitted problems or solutions to domain experts using a classification model trained on prior domain mapping, reviewer expertise, and historical project outcomes.

[0017] The semantic parser module is configured to identify cross-disciplinary keyword clusters to suggest innovation reapplications in alternate domains. The commercialization automation module supports real-time intellectual property lifecycle tracking, including filing status, legal milestones, and industry interest levels.

[0018] A computer-implemented method for commercializing research innovations by aligning academic solutions with industry-defined problems and automating their evaluation and transaction workflows. The method includes receiving metadata of academic intellectual property and storing it in a digital repository; standardized technical problem statements from industry users; receiving standardized technical problem statements from industry users; generating vector representations of problem statements and submitted solutions utilizing a semantic parsing model to extract domain-specific technical concepts; evaluating submitted solutions utilizing the AI- powered evaluation module, comprising steps: performing novelty analysis by querying global patent and publication databases using semantic similarity scoring and generating risk assessments and claim structure suggestions; performing inventiveness scoring based on comparative technical distinctiveness; performing market viabilityestimation using trend analysis, market data, and product-market fit indicators; generating an automated, downloadable evaluation report containing novelty scores, patentability insights, inventiveness estimates and market fit indicators; semantically matching received problem statements against a repository of institutional intellectual property assets using a patent matching engine and ranking matches based on contextual relevance and prior licensing outcomes; recommending academic-industry collaborations based on Al-inferred domain compatibility, innovation maturity and historical collaboration data; generating licensing drafts, patent valuation metrics and revenue-sharing workflows using a commercialization automation module and recording all transaction stages with digital audit logs; managing access to Al evaluation services through a token-based system that enables quota-based and monetized service usage; and facilitating a structured, multi-stakeholder workflow comprising IP discovery, expert review, Al validation, institutional approval, licensing execution, and commercialization tracking.

[0019] The Al engine incorporates outcome-based feedback to retrain semantic similarity scoring models based on prior commercialization success.

[0020] The method further comprising automated filtering of external grants and funding opportunities based on technology domain and eligibility alignment.

[0021] The domain experts are automatically assigned to submissions based on an expertise-matching classifier trained on prior review data. The system integrates external patent databases via real-time APIs to retrieve up-to-date global and domainspecific publication data for novelty and commercialization evaluation.

[0022] The system further comprises security protocols for data privacy, including rolebased access controls, encryption of unpublished submissions and digital watermarking of confidential documents.

[0023] The Al evaluation module employs reinforcement learning to improve semantic similarity and novelty scoring based on historical commercialization outcomes and token consumption metrics. The system further comprises a collaborative innovation workspace module to enable authenticated users to concurrently edit solution drafts, exchange feedback, and track version history in a shared digital environment.

[0024] The system further comprises a peer feedback submodule where vetted domain experts or community reviewers may contribute prior art suggestions or qualitative comments on submitted ideas.

[0025] In one aspect of the invention, the platform leverages existing institutional IP and Al to connect with companies by updating the IP database, using Al to match company ideas with relevant patents and facilitating commercialization agreements if the IP is deemed valuable. This streamlines the use of existing academic IP and fosters industry collaboration.

[0026] The system provides a technical effect by enabling automated, Al-driven evaluation of academic innovations for commercialization. It improves the accuracy, scalability and efficiency of novelty detection, inventiveness scoring and market viability estimation through semantic parsing, real-time patent database integration and reinforcement learning. These enhancements go beyond mere software execution and contribute to technical advancement in innovation lifecycle management. A method of operation for the same is also disclosed.BRIEF DESCRIPTION OF THE DIAGRAM

[0027] The foregoing summary, as well as the following detailed description of the invention will be better understood when read in conjunction with the appended drawings. For the purpose of assisting in the explanation of the invention, embodiments are shown in the drawings which are presently preferred and considered illustrative. It should be understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown therein. In the drawings:Figure. 1 depicts the process flow of industries / companies connecting with the platformFigure. 2a depicts the process flow of the institution connecting with the platformFigure. 2b depicts the process flow of student registration as a part of the institution with the platformFigure. 3 depicts the Artificial intelligence process moduleFigure. 4 depicts the existing IP commercialization moduleFigure 5 depicts the homepage and institutional registration module of the platform.Figure 6 depicts the user dashboard accessible to individual researchers and academic users.Figure 7 depicts the IP Marketplace and IP Management modules.Figure 8 depicts the prior art request management interface.Figure 9 depicts the user-side view of the Challenge Hub interface.Figure 10 depicts the Al engine module with novelty, inventiveness, and market feasibility reports.Figure 11 depicts the token-based access and pricing interface for Al services.Figure 12 depicts the institutional / industry administrator dashboard for challenge review and submission metricsFigure. 13 Depicts the system architecture diagram of the systemReference Numerals1302 User Registration Module1304 Problem Submission Module1306 Solution Submission Module1308 Artificial Intelligence / Al Engine1310 Semantic Parsing Module1312 AI-Po wered Evaluation Module1314 Patent Matching Engine1316 Collaboration Matchmaking Module1318 Commercialization Automation Module1320 Token-Based Access Control Module1322 Structured Workflow Engine1324 Cloud-Based InfrastructureDETAILED DESCRIPTION OF THE INVENTION

[0028] The present invention will now be described more fully herein after. For the purposes of the following detailed description, it is to be understood that the invention may assume various alternative variations and step sequences, except where expressly specified to the contrary. Thus, before describing the present invention in detail, it is to be understood that this invention is not limited to particularly exemplified systems orembodiments that may of course, vary. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to the various embodiments given in this specification.

[0029] As used herein, the singular forms "a," "an”," and "the" include plural reference unless the context clearly dictates otherwise. The term "and / or" means one or all of the listed elements or a combination of any two or more of the listed elements.

[0030] The terms “preferred” and “preferably” refer to embodiments of the invention that may afford certain benefits, under certain circumstances. However, other embodiments may also be preferred, under the same or other circumstances. Furthermore, the recitation of one or more preferred embodiments does not imply that other embodiments are not useful, and is not intended to exclude other embodiments from the scope of the invention.

[0031] When the term “about” is used in describing a value or an endpoint of a range, the disclosure should be understood to include both the specific value and endpoint referred to.

[0032] As used herein the terms “comprises”, “comprising”, “includes”, “including”, “containing”, “characterized by”, “having” or any other variation thereof, are intended to cover a non-exclusive inclusion.

[0033] The platform in the present invention is a comprehensive solution designed to bridge the gap between academic research, industry innovation, and individual researchers' innovative ideas. It aims to foster a collaborative ecosystem that benefits all stakeholders and ultimately serves society. The platform addresses several key issues through a structured approach.

[0034] In one embodiment, the platform provides market validation for academic research by identifying market needs. The platform utilizes advanced Al tools to conduct preliminary market analysis. These tools help researchers understand current market demands, trends, and customer needs. By facilitating partnerships between academicinstitutions and industry stakeholders, the platform ensures that research projects are guided by market requirements from the outset. This collaboration helps align academic innovations with real-world applications. The platform assists in conducting novelty checks and competitive analysis to ensure that the proposed innovations are unique and have a high potential for market success. This process includes assessing existing patents and similar research to identify gaps and opportunities.

[0035] In one embodiment the platform provides the industries with access to academic innovations, opening a channel for industries to access cutting-edge research and fresh ideas from academic environments. This infusion of new thoughts and approaches helps industries overcome existing product problems and develop new products more efficiently. The platform facilitates industry-academia collaboration, where companies can present specific challenges and work with academic researchers to find innovative solutions. This partnership accelerates the innovation process and leverages the strengths of both parties. By connecting with academia, industries can optimize their R&D resources, tapping into the intellectual capital of universities and research institutes without significantly increasing their internal research budgets, thus making innovation more cost-effective.

[0036] In another embodiment, the platform addresses this by providing a marketplace for individual researchers to showcase their innovations. This visibility connects them with industry stakeholders interested in their ideas. Researchers can collaborate with industry partners to refine their ideas, conduct further research, and develop prototypes. This collaboration is crucial for turning theoretical concepts into tangible products or services. The platform offers support and mentorship programs to help researchers navigate the commercialization process, including guidance on patent applications, business development and market entry strategies.

[0037] To enhance the platform's effectiveness, the present invention integrates advanced Al tools that perform several critical functions. Al algorithms analyze market data to identify trends, customer needs and potential demand for innovations. This analysis helps researchers and industries understand the market landscape and align their projects accordingly. Al tools conduct comprehensive searches to ensure that the proposed innovations are unique and do not infringe on existing patents. This step is crucial for protecting intellectual property and avoiding legal issues. The platform uses Al to evaluateexisting products and technologies in the market, identifying gaps and opportunities to ensure new innovations have a competitive edge.

[0038] The workflow of the platform is designed to be intuitive and efficient. Researchers, academics, and industry professionals submit their ideas, challenges, or research proj ects to the platform. Al tools conduct an initial assessment of the submissions, including market analysis, novelty checks and competitive analysis. The platform matches submissions with potential collaborators from academia, industry, or individual researchers based on the project's technical requirements and intended outcomes. Collaborative teams work on refining the ideas, conducting further research and developing prototypes, with the platform providing tools and resources to support this process. The platform assists in validating the developed innovations through market testing, customer feedback and pilot projects. The platform offers support for patent applications, business development and market entry strategies to help bring innovations to market.

[0039] Figure 1 describes the process of the collaborative platform designed to bridge the gap between academic research, industry innovation and individual researchers which begins when a new industry / company registers on the platform. Following registration, the company outlines its problem shelf in a prescribed format, detailing specific challenges or issues it seeks to address. This problem shelf undergoes a central review and approval process to ensure it aligns with the platform's standards and requirements. Once approved, the problem shelf is made available to the institution. The institution then reviews these problem statements and shares relevant ones with their students, encouraging them to develop and submit appropriate solutions. These student-submitted solutions are initially reviewed by the institution and then submitted for further review based on specific parameters. After a thorough evaluation, a shortlist of the most viable solutions is shared with the company. The company reviews the shortlisted solutions and identifies the most feasible one for implementation. Upon identifying a feasible solution, the company proceeds with commercialization, transitioning the solution from an academic concept to a market-ready product. Finally, a revenue-sharing agreement is put in place to ensure fair compensation and benefit sharing among all parties involved, thus facilitating a collaborative environment that supports innovation and advancement.

[0040] Figures 2a describe the process for the collaborative platform that begins when a new institution registers on the platform. Following registration, the institution onboards students who are interested in research. These students then log in to the platform and submit their ideas, which can be derived from the problem shelf provided by organizations. The institution reviews these submitted ideas and approves the most viable ones. Approved ideas are then transferred to the organization that had initially submitted the problem statement, closing the loop between academic research and industry needs.

[0041] In parallel, as shown in Figure 2b, students also register as part of the institution on the platform. They can choose to work on original ideas or select from the problem shelf. After reviewing the problem statements, students submit appropriate solutions. These solutions are then reviewed by the institution and, if found feasible, are passed on to the company for final selection. This streamlined process ensures that academic innovations are effectively matched with industry challenges, fostering a collaborative environment that bridges the gap between academia and industry.

[0042] Figure 3 illustrates the process flow for utilizing the Al engine to assist in patent landscape analysis. The workflow begins with the user submitting a structured idea to the system, which is processed by the IR-NOVA submodule — configured to perform semantic parsing and extract domain-specific technical concepts. These concepts are transformed into optimized search vectors and passed to integrated patent databases via real-time APIs. The retrieved data is then processed by a Large Language Model (LLM) embedded within the Al engine, which consolidates prior art references and generates a structured summary of relevant patents. This summary includes semantic similarity scores, novelty indicators and suggested claim structures, enabling users to assess the patentability of their idea. The system further allows users to download the evaluation report for reference during the patent drafting and filing process.

[0043] In another embodiment, the process flow leveraging institutional IP and Al capabilities to connect with companies begins with the institution updating its existing intellectual property (IP) into their database described in figure 4. The Al engine then reviews this database and identifies companies that could potentially benefit from the filed / granted patents. When a company expresses interest, it enters its idea into the system. This idea is split into relevant search terms by the Al model and used to search theacademic IP database for matches. Once a suitable patent is identified, it is shared with the company after the necessary sign-off procedures are completed. The company then reviews the patent and if it finds the IP valuable, enters into a commercialization agreement with the institution. This streamlined process ensures efficient utilization of academic IP and fosters collaboration between institutions and industry.

[0044] In further embodiments of the present invention, various user interface modules and dashboard functionalities have been developed to enhance usability, traceability, and operational efficiency for the stakeholders of the system, namely academic institutions, individual researchers, and industrial entities. These implementations are illustrated in the user interface embodiments shown in Figures 5 through 12 and represent the functional realization of the modules, workflows, and processes described in the earlier parts of the specification.

[0045] In one embodiment, the system further includes an Innovation Index Module designed to quantitatively assess the innovation potential and market readiness of submitted ideas. This module computes a composite score based on key parameters such as novelty, inventiveness, commercialization outcomes, collaboration effectiveness, and market fit. By assigning weighted scores to each factor, the system generates an Innovation and Marketability Index that enables institutions, researchers, and industry stakeholders to benchmark submissions, identify high-potential innovations, and prioritize ideas with the greatest likelihood of successful commercialization. This scoring mechanism adds an additional layer of intelligence and strategic decision support, making the platform not just a facilitation tool but a performance-driven ecosystem for innovation.

[0046] Figure 5 illustrates the homepage of the collaborative innovation platform, which serves as the entry point for all users. The interface introduces the objectives and functionalities of the platform, highlighting modules such as the Al Engine, Innovation Guide, and Institutional Registration. It includes a structured form that enables academic institutions to register with the system by providing requisite credentials including institutional details, admin identification, email address, contact number and location. This figure corresponds to the initial onboarding step of institutional users as described in the process flow of Figure 2a.

[0047] Figure 6 shows the user dashboard interface available to academic or individual researcher users. The dashboard presents a consolidated view of the user’s activity, including submitted challenges, their approval statuses, and navigational access to different modules such as the Challenge Hub, Prior Art, Al Engine, and IP Management. It serves as the operational interface for managing submissions, tracking feedback and progressing through various stages of validation and commercialization, as elaborated in the claimed invention’s workflow.

[0048] Figure 7 depicts both the IP Marketplace and the IP Management modules. The upper section presents the marketplace interface through which institutional intellectual property can be displayed for discovery and commercial interest from industrial partners. The lower section illustrates the management module that allows institutions to upload and maintain records of patents, monitor their statuses and initiate sharing actions. These interfaces correspond to the commercialization engine and institutional IP workflows described in the invention and supported by Figure 4.

[0049] Figure 8 illustrates the Prior Art Request interface through which users submit technical ideas for Al-powered novelty checks. The platform timestamps and categorizes requests based on their status (e.g., draft, submitted), allowing users to initiate structured searches against global patent databases. The workflow shown here corresponds to the novelty assessment and prior art search capabilities of the Al module described in Figure 3.

[0050] Figure 9 shows the Challenge Hub interface from the perspective of the academic or researcher user. The interface categorizes the user’s challenges into tabs such as Published, Draft, and Under Review. Users may track submission status, receive notifications and take follow-up actions. This embodies the structured problem and solution submission modules claimed in the invention, and complements the process flows described in Figures 1 and 2.

[0051] Figure 10 displays the Al Engine interface, which enables users to initiate automated evaluations of submitted ideas through integrated submodules: IR-NOVA for novelty analysis, IR-Edge for inventiveness scoring, and IR-Vibe for market viability estimation. Each submodule is configured to perform domain-specific assessments usingsemantic embeddings, comparative feature analysis, and trend-based viability metrics. The interface also presents a history of prior Al evaluations, options to download structured assessment reports, and configurable selection mechanisms for targeted analysis. This figure operationalizes the semantic parsing, novelty scoring and commercialization estimation functionalities described in the claims, demonstrating the system’s technical effect in automating intellectual property evaluation workflows.

[0052] Figure 11 shows the Token Management interface, which enables users to manage access to advanced Al modules through a quota-based token system. The interface supports configurable usage tiers for individual researchers, institutions, and companies, allowing differentiated access to evaluation and workflow services. The token mechanism functions as a technical access control layer, regulating feature availability and service consumption based on predefined thresholds. This design aligns with the system’s scalable SaaS-based architecture and ensures secure, auditable allocation of Al resources without relying on business method logic.

[0053] Figure 12 depicts the administrative dashboard accessible to institutional and industrial administrators. The dashboard presents structured submission metrics, including the total number of challenges initiated by affiliated users, the count of pending and approved submissions, and configurable review options. It operationalizes the institutional oversight and feedback mechanisms described in the collaboration matchmaking and structured workflow modules of the invention. The dashboard is configured to support role-based access control, timestamped review logs and decision tracking, thereby enabling secure and auditable administration of innovation challenges within the platform.

[0054] Figure 13 illustrates the system architecture of a computer-implemented platform (1300) designed to enable collaboration among academic, industry and expert users for the purpose of commercializing research innovations. The system comprises multiple interconnected modules. A User Registration Module (1302) is configured to onboard users with role-specific access rights. A Problem Submission Module (1304) receives and standardizes technical problem statements submitted by industry stakeholders, while a Solution Submission Module (1306) enables academic users to respond with commercially viable solutions.

[0055] At the core of the system lies an Artificial Intelligence Engine (1308), which includes a Semantic Parsing Module (1310) for extracting domain-specific technical concepts and generating vector embeddings. This is followed by an Al-Powered Evaluation Module (1312) responsible for novelty assessment, inventiveness scoring, and market viability analysis. The Al engine also includes a Patent Matching Engine (1314) that semantically maps industry-submitted problems to existing institutional intellectual property.

[0056] In one embodiment, the novelty assessment submodule, which is specifically designed to evaluate the uniqueness and patentability of a submission. This submodule is integrated with global patent databases, scientific publications, and technical repositories via APIs, enabling real-time access to up-to-date prior art information. By employing advanced semantic search algorithms and vector-based similarity models, the system parses the technical content of a submission, such as descriptions, claims, or abstracts, and generates similarity scores against existing patents and disclosures. Based on these scores and the contextual overlap of technical features, the system produces structured risk assessments that highlight potential overlaps or threats to novelty.

[0057] In one embodiment, the novelty assessment submodule utilizes a large language model (LLM) to interpret the intent and inventive steps of a submission, beyond keyword matching, thus providing more accurate and contextual comparisons. It then auto-generates claim structure suggestions, offering a template or baseline from which patent claims can be drafted, tailored to highlight the innovative aspects while avoiding areas of potential conflict with prior art. In another implementation, the submodule can be configured to provide visual mapping of technical similarities, enabling users to quickly understand where their innovation stands in the global IP landscape. The system may also allow researchers or tech transfer offices to download a detailed novelty report, including references to similar prior art, risk categorization (e.g., high, medium, low novelty), and guidance on potential patentability routes. This helps reduce the manual burden of patent analysis and supports informed decision-making early in the research-to-commercialization pipeline .

[0058] In one embodiment, the Al-powered evaluation module (1312) includes an inventiveness scoring submodule, which is configured to estimate the non-obviousnessof a submited innovation by conducting a comparative technical feature analysis. This submodule evaluates how distinct the proposed solution is from existing technologies by analyzing its core components, methods, and technical claims in comparison to prior art retrieved from global patent databases and relevant literature. Unlike traditional keyword-based searches, the submodule employs semantic understanding and vectorbased embeddings to detect nuanced differences and technical advancements across similar inventions. It identifies whether the combination or modification of known elements demonstrates a sufficient "inventive step" as required by patent law standards.

[0059] In one embodiment, the inventiveness scoring submodule uses a trained classifier model that has been fed thousands of historical patent prosecution cases, including granted and rejected patents, to learn paterns associated with nonobviousness. It computes an inventiveness score, typically on a probabilistic or percentile scale, indicating how likely the invention is to be considered non-obvious by a patent examiner. In another implementation, the submodule breaks down the technical features of a submission into discrete elements and compares them with those in similar prior art, highlighting novel technical combinations, unexpected outcomes, or technical effects not found in existing disclosures. The output may include a structured report detailing key differentiators, comparative feature matrices, and commentary on inventive merit. Thus, researchers, IP professionals, and institutions can proactively refine their inventions and strengthen their patent filings, while reducing the likelihood of rejection during patent examination due to a lack of inventive step.

[0060] The Al-powered evaluation module (1312) further includes a market viability estimator, which is configured to assess the commercialization potential of submited research innovations. This estimator leverages real-time data analytics and Al models to evaluate whether a proposed solution aligns with current technology trends, addresses existing market demands, and stands competitively among similar offerings. By integrating external data sources such as market intelligence reports, startup databases, patent landscapes, and product databases, the estimator can benchmark the innovation against comparable technologies or products in the market. It analyzes competitive density, emerging trend signals, funding activity, and technology adoption rates to provide a quantified assessment of market readiness.

[0061] In one embodiment, the market viability estimator uses machine learning models trained on historical commercialization outcomes to predict the likelihood of successful market entry for a given innovation. The estimator may output a market fit score, identify target sectors, and recommend commercialization pathways such as licensing, co-development, or spin-offs. In another implementation, it provides interactive dashboards that visualize competitive positioning, highlighting gaps or saturated segments in the market. It can also generate customized go-to-market insights, such as potential customer segments, regulatory considerations, or geographical markets with high demand. These capabilities help researchers, institutions, and industry partners to make informed strategic decisions, reduce time-to-market, and increase the success rate of academic innovations transitioning into viable commercial products.

[0062] Connected to the Al core are additional modules including a Collaboration Matchmaking Module (1316), which pairs researchers and companies based on inferred compatibility and historical success metrics. The collaboration matchmaking module (1316) is designed to recommend strategic pairings between academic institutions, researchers, and industry partners by leveraging Al to analyze and infer domain compatibility, historical collaboration success rates, and innovation readiness indicators. This module functions as an intelligent partner-matching engine that identifies the most suitable collaborators for a given project or research submission, ensuring that partnerships are not only aligned by subject matter expertise but are also likely to be productive and outcome-driven. It analyzes user profiles, institutional research focus areas, publication and patent histories, prior collaborative projects, and engagement metrics to build a dynamic compatibility model.

[0063] In one embodiment, the module utilizes graph-based neural networks to map relationships and interaction histories between entities on the platform, allowing it to identify patterns of successful collaborations across disciplines and sectors. In another implementation, it applies natural language processing (NLP) to assess the thematic alignment of submitted proposals or problem statements with the domain strengths of potential collaborators.

[0064] A Commercialization Automation Module (1318) configured to draft licensing agreements and initiate revenue-sharing workflows. The commercialization automation module (1318) is configured to generate draft licensing agreements tailored to the type of intellectual property, collaboration model, and applicable industry standards. It also computes patent valuation metrics by analyzing comparable IP transactions, market trends, and technical relevance, and initiates structured revenue-sharing workflows between academic institutions and industry users. These workflows are logged with audit trails at each transaction step to ensure transparency, traceability, and compliance with institutional and legal norms.

[0065] A Token-Based Access Control Module (1320) governs tiered usage of Al services. The token-based access control module (1320), governs platform usage through virtual tokens, enabling institutions and users to access Al services, evaluation tools, and advanced matchmaking features based on subscription tiers or usage quotas. This allows for scalable monetization and controlled feature access across different user categories.

[0066] A Structured Workflow Engine (1322) orchestrates multi-stage transitions from idea to market-ready IP. The structured workflow engine (1322) that guides each research submission through multiple stages, from initial evaluation and expert review to IP validation, institutional approval, licensing, and post-deal tracking. Each stage is timestamped and digitally logged, offering a complete audit history and enhancing accountability across all stakeholders. Supporting this entire ecosystem is a cloud-based infrastructure (1324) that enables real-time status updates, multi-user collaboration, and secure access controls based on user roles, ensuring a scalable and responsive user experience.

[0067] All modules are hosted on a Cloud-Based Infrastructure (1324), enabling realtime collaboration, secure access and dynamic status updates across stakeholders. The system enables stakeholders to download Al-generated assessment reports that consolidate key findings from the evaluation modules, including novelty insights, inventiveness scores, market viability ratings, and commercialization recommendations. These reports serve as decision-support tools for tech transfer offices, researchers, and industry partners to prioritize, refine, and accelerate innovation pipelines. Collectively, these modules create an end-to-end digital infrastructure for managing, evaluating, and commercializing academic intellectual property at scale.

[0068] These interface embodiments collectively illustrate the system’s practical deployment, ensuring structured collaboration, transparency, and effective use of artificial intelligence in academic-industrial innovation ecosystems. The modular dashboard architecture ensures that the platform remains user-centric while complying with the technical requirements outlined in the invention. Each of these figures supports the core claim of enabling intelligent, real-time collaboration between academia, industry and individual researchers through Al-augmented workflows and commercialization pathways.

[0069] In various embodiments, the present invention provides a computer- implemented method for the commercialization of research innovations by aligning academic solutions with industry-defined problems and automating their evaluation and transaction workflows. The method begins by receiving metadata associated with academic intellectual property (IP) and storing it in a digital repository, alongside standardized technical problem statements submitted by industry users. The metadata includes critical information such as technical descriptions, proof-of-concept data, and intended applications of the academic IP. Industry users submit standardized technical problem statements, which are then processed to generate vector representations using a semantic parsing model that extracts domain-specific technical concepts. The solutions submitted are evaluated using an Al-powered evaluation module, which performs novelty analysis by querying global patent and publication databases, generating risk assessments, and providing claim structure suggestions. Additionally, the module assesses inventiveness by comparing the technical distinctiveness of the solutions and evaluates market viability using trend analysis, market data, and product-market fit indicators. The system generates an automated, downloadable evaluation report containing novelty scores, patentability insights, inventiveness estimates, and market fit indicators.

[0070] The system uses a patent matching engine to semantically match the received problem statements with institutional intellectual property assets, ranking the matches based on contextual relevance and prior licensing outcomes. The system recommends potential academic -industry collaborations based on Al-inferred domain compatibility, innovation maturity, and historical collaboration success. A commercialization automation module generates licensing drafts, patent valuation metrics, and revenue -sharing workflows while recording each transaction stage with digital audit logs. The method also incorporates a token-based access control system for managing Al evaluation services ona quota-based and monetized usage model. Finally, the system facilitates a structured, multi-stakeholder workflow that spans IP discovery, expert review, Al validation, institutional approval, licensing execution, and ongoing commercialization tracking, ensuring that academic innovations reach the market effectively and efficiently.

[0071] In various embodiments, the Al engine (1308) incorporates outcome-based feedback to continuously enhance the performance of its semantic similarity scoring models. As academic solutions are matched with industry problem statements, and the resulting collaborations move forward toward commercialization, feedback is gathered based on the success or failure of these commercialization efforts. This feedback includes factors such as licensing success, market adoption, revenue generation, and the overall effectiveness of the solution in addressing the industry's needs. The Al engine uses this feedback to retrain its semantic similarity models, refining their ability to evaluate and match solutions based on historical commercialization outcomes. The retraining process involves adjusting model parameters and recalibrating the semantic understanding of key technical concepts, ensuring that future evaluations are better aligned with industry trends and more predictive of successful commercialization. This continuous learning mechanism enhances the system’s ability to make more accurate and informed recommendations, improving the overall efficiency and effectiveness of the commercialization process.

[0072] The method and system described in the present invention further include several advanced features to enhance functionality and ensure the seamless commercialization of academic innovations. Automated filtering is implemented to streamline the discovery of external grants and funding opportunities, leveraging technology domain and eligibility alignment criteria. This ensures that relevant financial support is efficiently matched to the innovations based on their specific technical and market needs. Additionally, domain experts are automatically assigned to review submissions through an expertise-matching classifier that is trained on historical review data, allowing for more accurate and efficient expert assignment. This classifier assesses the submitted solutions and identifies the best-qualified experts based on their past experience and knowledge. The system also integrates external patent databases via realtime APIs, enabling the retrieval of up-to-date global and domain-specific publication data for accurate novelty and commercialization evaluations.

[0073] Security is a key component of the system, with role-based access controls, encryption of unpublished submissions, and digital watermarking applied to confidential documents, ensuring that sensitive intellectual property remains secure throughout thecommercialization process. The Al evaluation module incorporates reinforcement learning to continuously improve its semantic similarity and novelty scoring, adjusting its model based on historical commercialization outcomes and token consumption metrics, ensuring progressively refined evaluations over time. Lastly, the system provides a collaborative innovation workspace module, which enables authenticated users to concurrently edit solution drafts, exchange feedback, and track version history in a shared digital environment, enhancing team collaboration and accelerating the development of marketready solutions.

[0074] The above-described embodiment of this patent is detailed; however, it is not limited to the mentioned embodiment. One skilled in the relevant art can make various changes within the scope of this patent, provided they do not deviate from its intended purpose.

Claims

WE CLAIM:

1. A computer-implemented system ( 1300) to evaluate innovations via artificial intelligence (Al), the system comprising: a) a user registration module (1302) configured to onboard users, each with rolespecific access to the platform; wherein the users include institutions, companies, verified mentors, domain experts and individual researchers. b) a problem submission module (1304) configured to receive and standardize technical problem statements submitted by industries; c) a solution submission module (1306) configured to allow the users to submit commercially-oriented responses to industry problem statements, with the intent of facilitating productization and licensing opportunities; d) an artificial intelligence engine (1308) comprising: i. a semantic parsing module (1310) configured to extract key technical concepts and generate vector embeddings of submitted problem statements and solutions; ii . an Al-powered evaluation module (1312) comprising : a novelty assessment submodule integrated with global patent databases and semantic search algorithms, configured to generate structured risk assessments and patentability insights, including similarity scores and suggested claim templates; an inventiveness scoring submodule configured to estimate nonobviousness based on comparative technical feature analysis; a market viability estimator configured to assess commercialization potential based on technology trends, competitive benchmarks, and market fit indicators; e) a patent matching engine (1314) configured to semantically map industry- submitted problem statements against institutional intellectual property databases utilizing large language model-based vector embeddings, and rank results by relevance and prior commercialization outcomes;f) a collaboration matchmaking module (1316) configured to recommend strategic pairings between academic institutions, researchers, and companies based on Al-inferred domain compatibility, historical collaboration success rates, and innovation readiness indicators; g) a commercialization automation module(1318) configured to generate draft licensing agreements, compute patent valuation metrics, and initiate structured revenue -sharing workflows between academic institutions and industry users, based on matched intellectual property assets, with audit-trail logging for each transaction step; h) a token-based access control module (1320) integrated with the Al engine, configured to enable to manage tiered usage of system features through virtual tokens; i) a structured workflow engine (1322) facilitating multi-stage transition of academic IP to market-ready solutions, with timestamped digital audit trails; j ) a cloud-based infrastructure (1324) operable to provide real-time status updates, secure multi-user collaboration, and access control for role-based services; wherein the system is further configured to generate downloadable assessment reports containing novelty insights, inventiveness scoring, market viability ratings, and commercialization recommendations aligned with emerging industry requirements.

2. The system as claimed in claim 1, wherein the commercialization automation module is configured to generate contract templates based on configurable licensing models and industry norms.

3. The system as claimed in claim 1, wherein the system assigns submitted problems or solutions to domain experts using a classification model trained on prior domain mapping, reviewer expertise, and historical project outcomes.

4. The system as claimed in claim 1, wherein the semantic parser module (1310) is configured to identify cross-disciplinary keyword clusters to suggest innovation reapplications in alternate domains.

5. The system as claimed in claim 1, wherein the commercialization automation module (1318) supports real-time intellectual property lifecycle tracking, including filing status, legal milestones and industry interest levels.

6. A computer-implemented method for commercializing research innovations by aligning academic solutions with industry-defined problems and automating their evaluation and transaction workflows, the method comprising: a) receiving metadata of academic intellectual property and storing it in a digital repository; standardized technical problem statements from industry users; b) wherein the metadata includes technical descriptions, proof-of-concept data and intended applications; c) receiving standardized technical problem statements from industry users; generating vector representations of problem statements and submitted solutions utilizing a semantic parsing model to extract domain-specific technical concepts; d) evaluating submitted solutions utilizing the Al-powered evaluation module, comprising steps: performing novelty analysis by querying global patent and publication databases using semantic similarity scoring and generating risk assessments and claim structure suggestions; performing inventiveness scoring based on comparative technical distinctiveness; performing market viability estimation using trend analysis, market data, and product-market fit indicators; e) generating an automated, downloadable evaluation report containing novelty scores, patentability insights, inventiveness estimates and market fit indicators; f) semantically matching received problem statements against a repository of institutional intellectual property assets using a patent matching engine and ranking matches based on contextual relevance and prior licensing outcomes;g) recommending academic -industry collaborations based on Al-inferred domain compatibility, innovation maturity and historical collaboration data; h) generating licensing drafts, patent valuation metrics and revenue -sharing workflows using a commercialization automation module and recording all transaction stages with digital audit logs; i) managing access to Al evaluation services through a token-based system that enables quota-based and monetized service usage; j) facilitating a structured, multi-stakeholder workflow comprising IP discovery, expert review, Al validation, institutional approval, licensing execution, and commercialization tracking.

7. The method as claimed in claim 6, wherein the Al engine (1308) incorporates outcomebased feedback to retrain semantic similarity scoring models based on prior commercialization success.

8. The method as claimed in claim 6, further comprising automated filtering of external grants and funding opportunities based on technology domain and eligibility alignment.

9. The method of claim 6, wherein domain experts are automatically assigned to submissions based on an expertise-matching classifier trained on prior review data.

10. The system as claimed in claim 1, wherein the system integrates external patent databases via real-time APIs to retrieve up-to-date global and domain-specific publication data for novelty and commercialization evaluation.

11. The system as claimed in claim 1, further comprising security protocols for data privacy, including role-based access controls, encryption of unpublished submissions and digital watermarking of confidential documents12. The system as claimed in claim 1, wherein the Al evaluation module (1312) employs reinforcement learning to improve semantic similarity and novelty scoring based on historical commercialization outcomes and token consumption metrics.

13. The system as claimed in claim 1, wherein a collaborative innovation workspace module (1318) is provided, enabling authenticated users to concurrently edit solution drafts, exchange feedback, and track version history in a shared digital environment.

14. The system as claimed in claim 1, further comprising a peer feedback submodule where vetted domain experts or community reviewers may contribute prior art suggestions or qualitative comments on submitted ideas.

15. The system as claimed in claim 1, further comprising an innovation index module configured to compute an innovation index for submitted solutions using weighted performance metrics, including novelty score, inventiveness rating, commercialization success, collaboration effectiveness, and market viability indicators.

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