Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of Invention:
A SYSTEM AND METHOD FOR DETERMINING OR ANALYSING AND/OR INTERPRETING INNOVATION DISCLOSURES

Applicant:
TECH MAHINDRA LIMITED
A company Incorporated in India under the Companies Act, 1956
Having address:
Tech Mahindra Limited, Phase III, Rajiv Gandhi Infotech Park Hinjewadi,
Pune - 411057, Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.
TECHNICAL FIELD
[002] The present invention relates generally to the field of intellectual property analysis. Particularly, the present invention involves a system and method for determining or analysing and/or interpreting innovation disclosures using Substance-Field (Su-Field) analysis, derived from TRIZ (Theory of Inventive Problem Solving) combined with natural language processing (NLP) and large language models (LLM).
BACKGROUND OF THE INVENTION
[003] Generally, analysing and determining the substance and technical merit of invention or innovation disclosures is a time-consuming and expertise-intensive process. It requires a deep understanding of the disclosed concept, its functional and structural aspects, relevant domain knowledge, and the ability to assess its distinctiveness in light of existing technologies or prior developments.
[004] In recent years, there have been no such attempt is made to automate and streamline the evaluative process of invention disclosures. Conventional approaches often involve rule-based systems, templates, or simple natural language processing (NLP) techniques. These methods fall short in generating coherent insights or performing reliable assessments—particularly for complex and nuanced innovation disclosures.
[005] Moreover, no such method or system has been developed, which understand the complex language used in patent documents, which often include technical jargon, legal terminology, and domain-specific vocabulary. Additionally, ensuring consistency and depth across such evaluations is difficult. With the advent of artificial intelligence (AI), especially large language models (LLMs), there is increasing interest in utilizing these models to support the interpretation and analysis of invention disclosures. Although LLMs demonstrate potential in processing technical content, their use in structured evaluative contexts is still developing. Therefore, there is a significant need to develop improved systems capable of determining, analysing, and interpreting invention disclosures by integrating the contextual understanding capabilities of LLMs with structured models that represent the core components and relationships within a disclosed innovation. Traditional methods rely heavily on manual comparison with prior work to assess novelty or advancement, making the process slow and inconsistent. Existing approaches to evaluating technical merit or originality remain largely dependent on expert interpretation of prior developments. Therefore, to overcome the problems associated with the traditional methods, there is need for a system for determining the patentability of the invention by using a Su-Field analysis. The Su-Field analysis is a technique from TRIZ used to model systems in terms of substances and fields. A typical Su-Field model includes two substances (S1 and S2) and a field (F) that describes the energy interaction between them. By analysing the configuration and energy characteristics of these components, the system assess the novelty and inventive step of a proposed invention.
OBJECTS OF THE INVENTION
[006] Primary objective of the present invention is to provide Artificial Intelligence (AI) based system to determine the patentability of the invention.
[007] Another objective of the present invention is to provide an AI based NLP prompts/model for testing and developing the patent application.
[008] Yet another objective of the present invention is rapidly generate the draft of claims for the patent application.
SUMMARY OF THE INVENTION
[009] Before the present system is described, it is to be understood that this application is not limited to the particular machine, device, or system, as there can be multiple possible embodiments that are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is to describe the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system and method for determining the patentability of an invention using substance-field (su-field) analysis, and the aspects are further elaborated below in the detailed description. This summary is not intended to identify essential features of the proposed subject matter nor is it intended for use in determining or limiting the scope of the proposed subject matter.
[0010] In an embodiment, the present invention provide a system (102) for determining, analyzing, and interpreting innovation disclosures comprises a processing unit (202) and a storage device (206) coupled to the processing unit (202), wherein the processing unit (202) is capable of executing a plurality of modules stored in the storage device (206). These modules include a natural language processing (NLP) model (208) configured to receive and parse input data, such as textual descriptions, technical drawings, flowcharts, or CAD models associated with an invention, and to extract and structure system elements, including technical components, their functions, relationships, and operational context. A large language model (LLM) (210), communicatively coupled to the NLP model (208), is configured to receive structured outputs from the NLP model (208), perform semantic, syntactic, and functional analysis on the extracted data, identify and retrieve prior art from the storage device (206) by querying one or more patent, scientific, and technical databases for similar elements or functionally equivalent systems, and generate a contextual mapping of the NLP-extracted components in relation to the identified prior art. A Substance-Field (Su-Field) model (212), communicatively coupled to the NLP model (208) and LLM (210), is configured to receive the NLP-extracted components and LLM-identified prior art and construct comparative Su-Field models and generate the Su-field diagram of the invention and the retrieved prior art. An energy calculator module (214), communicatively coupled to the Su-Field model (212), is configured to compute an energy profile and energy footprint for both the invention and the prior art based on Su-Field interactions and calculate an energy delta (ΔE) representing the differential in energetic or functional complexity between the invention and the prior art. A decision module (216), communicatively coupled to the energy calculator module (214), is configured to compare the energy delta (ΔE) to a predefined innovation threshold value and determine whether the invention exhibits a degree of technical advancement or efficiency gain sufficient to qualify as patentable. An output generator module, stored in the storage device (206) and communicatively coupled to the decision module (216).
[0011] In another embodiment, the present invention provides a Su-Field models of the invention and the retrieved prior art, which comprises N number of substances such as Substance 1 (S1), Substance 2 (S2)---- Substance 2 (Sn), and Field (F) interactions, with annotations for field type, intensity, polarity (positive/negative), and interaction efficiency.
[0012] In yet another embodiment, the present invention provides the NLP model (208) further configured to classify the extracted system elements using a TRIZ-based framework.
[0013] In still another embodiment, the present invention provides the LLM (210) trained on a corpus comprising patent literature, technical specifications, and industrial design document
[0014] In another embodiment, the present invention provides the decision module (216) configured to determine patentability based on: ΔE = Energy Footprint (prior art) − Energy Footprint (invention)
[0015] In still another embodiment, the present invention provides the energy calculator module (214) configured to calculate the energy footprint using the formula:
Energy Footprint = (Number of Substances) + (Positive Field Count × Avg. Intensity) − (Negative Field Count × Avg. Intensity)
[0016] In another embodiment, the present invention provides the output generator module further configured to generate annotated Su-Field diagrams highlighting novel field interactions identified during analysis.
[0017] In yet another embodiment, the present invention provides the decision module (216) comprising logic for threshold tuning based on domain-specific innovation criteria or user-defined novelty benchmarks.
[0018] In still another embodiment, the present invention provides the output generator module configured to generate draft patent claims based on features associated with field improvements or reductions in energy complexity.
[0019] In still another embodiment, the present invention provides the method for determining, analyzing, and interpreting innovation disclosures using a system (102) comprising a processing unit (202) coupled with a storage device (206) involves multiple steps executed by the processing unit (202). The method includes receiving and parsing, by a natural language processing (NLP) model (208), input data comprising one or more of textual descriptions, technical drawings, flowcharts, or CAD models associated with an invention, and extracting and structuring system elements, including technical components, their functions, relationships, and operational context. A large language model (LLM) (210), communicatively coupled to the NLP model (208), receives structured outputs from the NLP model (208), performs semantic, syntactic, and functional analysis on the extracted data, identifies and retrieves prior art from the storage device (206) by querying one or more patent, scientific, and technical databases for similar elements or functionally equivalent systems, and generates a contextual mapping of the NLP-extracted components in relation to the identified prior art. A Substance-Field (Su-Field) model (212), communicatively coupled to the NLP model (208) and LLM (210), receives the NLP-extracted components and LLM-identified prior art and constructs comparative Su-Field models and generate the Su-field diagrams of the invention and the retrieved prior art, each model comprising Substance 1 (S1), Substance 2 (S2), and Field (F) interactions with annotations for field type, intensity, polarity, and interaction efficiency. An energy calculator module (214), communicatively coupled to the Su-Field model (212), computes an energy profile and energy footprint for both the invention and the prior art based on Su-Field interactions and calculates an energy delta (ΔE) representing the differential in energetic or functional complexity between the invention and the prior art. A decision module (216), communicatively coupled to the energy calculator module (214), compares the energy delta (ΔE) to a predefined innovation threshold value and determines whether the invention exhibits a degree of technical advancement or efficiency gain sufficient to qualify as patentable.
BRIEF DESCRIPTION OF DRAWING
[0020] The foregoing summary, as well as the following detailed description of embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, there is shown in the present document example constructions of the disclosure, however, the disclosure is not limited to the specific methods and device disclosed in the document and the drawing. The detailed description is described with reference to the following accompanying figures.
[0021] Figure 1: illustrates a network implementation of a system for determining the patentability of the invention, in accordance with an embodiment of the present subject matter.
[0022] Figure 2: illustrates the architecture of the system for determining the patentability of the invention, in accordance with an embodiment of the present subject matter.
[0023] Figure 3: illustrates a flowchart showing the working of the proposed method for determining the patentability of the invention using a Su-field analysis, in accordance with an embodiment of the present subject matter.
[0024] Figure 4: illustrates a flow chart performing a method for determining or analyzing and interpreting innovation disclosures using a system (102), in accordance with an embodiment of the present subject matter.
[0025] The figures depict various embodiments of the present disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
[0026] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising", “having”, 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 as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any devices and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, devices and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0027] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0028] Following is a list of elements and reference numerals used to explain various embodiments of the present subject matter.
Reference Numeral Element Description
100 Network implementation of a system
102 System
104 Interfaces
106 Network
200 Architecture
202 Processing Unit
204 User Interfaces
206 Storage Device
208 Natural language processing (NLP) model
210 Large Language Model (LLM)
212 Substance-field (Su-field) model
214 Energy Calculator Module
216 Decision Module
218 Output generator
220 Data Repository
400 Method
[0029] Referring now to Figure 1, a network implementation (100) of a system (102) is illustrated. The system (102) is being implemented on a server, it can be understood that it may also operate on various computing systems, such as laptops, desktops, notebooks, workstations, mainframes, or within cloud-based environments. Multiple users can access the system (102) through various user devices (104-1) (104-2) (104-3) (104-4), collectively referred to as users or stakeholders, which may include IoT devices, IoT gateways, portable computers, personal digital assistants, handheld devices, and workstations, all communicatively coupled to the system (102) through a network (106). This network (106) may be wireless, wired, or a combination of both, and can take the form of intranets, local area networks (LAN), wide area networks (WAN), or the internet, utilizing various protocols such as HTTP, HTTPS, and TCP/IP. Furthermore, the network (106) may comprise a range of devices, including routers, bridges, servers, and storage devices.
[0030] In accordance with an embodiment of Figure 2, which illustrates the architecture (200) of a system (102) designed for analysing, determining, and interpreting innovation disclosures using Su-Field analysis in conjunction with natural language processing (NLP) and large language models (LLMs).
[0031] . The system (102) comprising at least one processing circuitry or processing unit (202), an user interface (204), and a storage device (206). The processing unit (202), which may consist of microprocessors, microcontrollers, or digital signal processors, is responsible for executing computer-readable instructions stored in the storage device (206). The user interface (204) facilitates interaction with users and communication with other computing devices, supporting multiple network types and protocols. The storage device (206) encompasses various computer-readable media, including volatile and non-volatile memory, and contains plurality of modules and data repository (220). The plurality of modules comprises routines and programs that execute specific tasks, and the data repository (220) serves as a storage space for processed, received, and generated data, including data associated with the invention.
[0032] In an embodiment, of figure 2, the plurality of modules includes a language processing (NLP) module (208), a Large Language Module (LLM) (210), a Substance-field (Su-field) module (212), an energy calculator module (214), and an Output generator (218) are illustrated. In an embodiment, the data repository (220) may be referred to as patent database and technical database. In other aspect, the present invention provides a system (102) and method (400) for determining the patentability of a proposed invention using Su-Field analysis in combination with natural language processing (NLP) model (208). The system (102) receives, as input, data and textual descriptions of the invention through a user interface (204). The NLP model (208) processes the input texts, data, description, sketch to identify and classify components as substances and fields.
[0033] Using the LLM (210) and NLP model (208), a search is performed on the patent database to identify similar designs or inventions. The purpose for identifying the relevant prior art is to determine the patentability of the inputted invention by using a Su-Field analysis. The Su-Field analysis is a technique from a Theory of Inventive Problem Solving (TRIZ). The TRIZ is used to model systems in terms of substances and fields. A typical Su-Field model (210) includes two substances (S1 and S2) and a field (F) that describes the energy interaction between them. By analysing the configuration and energy characteristics of these components, the proposed system (102) assess the novelty and inventive step of an inputted invention.
[0034] In an embodiment, during the Su-field analysis, the new data associated with the invention and the prior arts retrieved from the previous searches are broken in terms of substance, fields and objects. In an embodiment, the data associated with the invention may be design, sketches, description, and files also. The data is inputted into the system (102) through the user interface (204). In one embodiment, every component in the design represents a substance and the overall design is a strong network of substance and fields.
[0035] The LLM (210) and NLP model (208) parses the input to the system (102) identify and classify substances and fields. In an embodiment, the LLM (210) is configured to be trained on a corpus. The corpus may be a patent database, patent literature, technical specifications, and design documents. The LLM (210) is configured to conduct a su-field analysis on both the input design and the base design. The Su-Field analysis is automated using the natural language processing (NLP) model (208) to classify elements associated with the data of the invention into substances and fields. This analysis breaks down each design (input data and prior art) into its constituent substances and energies, allowing for a detailed comparison.
[0036] In an embodiment, the energy calculator module (214) calculates the total energy footprint for both the Su-field model (212) of the input data and the retrieved prior art from the patent databases. In an embodiment, each model represents the relationship between substances and the energy or field connecting within the input data of the invention and the prior art. The energy calculator module (214) providing a quantitative measure of their energy impacts. A delta function is employed to determine the difference in energy footprints between the two designs. The decision module (216) is configured to make a decision regarding the patentability of the invention based on the negative or threshold values of the delta function, ensuring that only novel and non-obvious inventions are considered for patent protection. The LLM (210) further prepares a set of claims as per the delta function or difference in the key features of patentable invention. The output generator (218) creates visual comparisons, reports, and draft patent claims.
[0037] In an embodiment, the energy footprint is calculated for both the invention and retrieved prior art based on the quantity and quality of substances and fields. In one embodiment, the prior may be the identical patent document, literature, design, sketch and alike. The difference between these footprints, referred to as the energy delta, is used to determine whether the invention demonstrates sufficient novelty and inventive step. In an embodiment, determining the patentability of input invention includes comparing the energy difference against a predefined innovation threshold. The system (102) finally generates a set of potential patent claims based on the delta by highlighting the inventive aspects of the input invention.
[0038] Particularly, the system architecture illustrated in Figure 2 operates through a structured, multi-step process that automates the analysis and interpretation of invention disclosures. It begins when a user submits an invention disclosure through the user interface (204), which supports diverse input formats, including textual descriptions, technical drawings, CAD files, flowcharts, and supplementary documentation. Once the disclosure is submitted, the processing unit (202) takes over as the central control mechanism, executing instructions and orchestrating the various analytical modules housed within the storage device (206).
[0039] The first analytical step engages the NLP Model (208), which parses the disclosure’s text using natural language processing techniques. This model identifies and extracts technical components, operational functions, and system relationships. These elements are then categorized under the TRIZ Su-Field theory constructs—Substance 1 (S1), typically representing tools or active elements; Substance 2 (S2), representing targets or recipients of actions; and Field (F), denoting the nature of interaction such as data flow, mechanical force, or electromagnetic signals
[0040] Following this, the Large Language Model (LLM) (210) deepens the understanding of the invention. Leveraging its ability to process context, it performs a thorough search across patent databases, scientific literature, and technical repositories to retrieve prior art or analogous systems. This ensures that the invention is evaluated against the most relevant existing technologies.
[0041] With both the disclosure and prior art information in hand, the system constructs a Su-Field Model (212)—a structured, often diagrammatic representation of the invention that shows the functional and energy relationships between S1, S2, ---Sn and F. The energy calculator module (214) then analyses each field for parameters such as intensity, efficiency, and transformation type, creating a detailed energy profile of the invention. This same process is applied to the prior art, and a comparison is performed by calculating the Energy Delta (ΔE), which measures the difference in energy flow or interaction efficiency between the invention and the prior art. This metric quantifies how much the invention alters or improves upon known technologies.
[0042] The Decision Module (216) interprets the ΔE value in light of predefined thresholds. If the energy delta is significant, the invention is flagged as potentially novel or inventive, suggesting it may meet the criteria for patentability. Conversely, a small delta may categorize the invention as an incremental improvement. Based on this evaluation, the output generator (218) prepares detailed documentation, which may include Su-Field diagrams, analytical comparison reports, and even draft patent claims that highlight the novel aspects identified by the energy delta. Finally, all data—from user inputs to analytical outputs—is securely stored in the Data Repository (220). This repository serves not only as a recordkeeping solution but also as a knowledge base for future disclosures, comparisons, and machine learning model enhancements. This comprehensive and automated approach enables robust, repeatable, and technically grounded evaluation of innovation disclosures.
[0043] Further, in the context of TRIZ Su-Field analysis, the concept of an Energy Profile plays a central role in understanding and evaluating the functional and energetic characteristics of a system. In this a system the Energy Profile refers to a comprehensive representation—both quantitative and qualitative—of how energy is transmitted, transformed, or exchanged between multiple components such as S1, S2, S3 and so on through F. For mechanical systems, this profile may include parameters such as force, torque, friction, or vibration, while in software systems, it could account for data transfer rates, latency, signal strength, or computational load. This profile captures the nature and efficiency of the interactions that drive the system’s functions.
[0044] An important analytical measure derived from the energy profile is the Energy Delta (ΔE), which indicates the difference in energy characteristics between an invention and a comparable system or prior art. It is mathematically expressed as:
[0045] ΔE = E_invention − E_prior_art,
[0046] where E_invention and E_prior_art represent the total energy profiles of the new disclosure and the known baseline system, respectively. This delta helps determine how much the invention modifies, improves, or optimizes energy flows or interactions within the system.
[0047] The calculation of an energy profile proceeds through a structured multi-step approach. First, a Su-Field Model is constructed using natural language processing (NLP) and large language models (LLMs) to extract system elements—components, functions, actors—from the invention disclosure and classify them into S1, S2,--Sn and F. This forms the basis for a functional model or graph of the system. Next, attributes are assigned to each Field (F), such as type (e.g., electrical, mechanical, data), intensity (e.g., voltage, speed, bandwidth), and quality (e.g., efficiency, noise level, latency). These are derived from input data, design specifications, or simulations.
[0048] This calculation is repeated for the prior art system, after which the energy delta (ΔE) is determined. If the delta exceeds a predefined decision threshold, the system concludes that the invention likely introduces a novel or significant functional transformation. If not, the improvement may be deemed incremental or negligible.
[0049] This energy-centric approach is particularly powerful because it moves beyond superficial keyword matching or descriptive similarities. Instead, it evaluates inventions based on functional, energetic, and systemic distinctions, enabling deeper technical comparisons, better novelty assessments, and the potential for automated generation of claims or patent recommendations based on actual technical merit.
[0050] In an embodiment, each field (F) in the Su-Field is analysed to assign qualitative and quantitative attributes. These include the type of field (e.g., mechanical, electrical, software), its intensity (which represents the strength or magnitude of the interaction), and efficiency or quality (which reflects how effectively the function is performed). This step enriches the Su-Field diagram into a fully annotated model capable of energy-based evaluation.
[0051] Afterwards, an energy profile is computed for each Su-Field based on the assigned attributes.
[0052] This composite value captures the total qualitative and quantitative energy engagement of the system. Profiles are calculated separately for both the invention and the prior art, forming the basis for comparative analysis.
[0053] Further, an energy footprint calculation, a holistic score is computed for each system to represent its overall energy structure. The formula used is:
[0054] Energy Footprint = (Number of Substances) + (Positive Field Count × Avg. Intensity) − (Negative Field Count × Avg. Intensity)
[0055] This score reflects the total energy load, combining structural complexity (via substances) and functional efficiency or inefficiency (via fields and their quality).
[0056] Once energy footprints are available, the delta function calculation (ΔE) is performed. This step evaluates the energy differential between the prior art and the invention. There are two valid approaches:
[0057] ΔE = Energy Footprint (prior art) − Energy Footprint (invention)
[0058] A significantly negative ΔE value implies that the invention operates with less energy complexity or higher efficiency, indicating potential technical advancement.
[0059] The system then compares the ΔE value against a predefined threshold. If ΔE exceeds this threshold in the positive direction, the invention is likely not a substantial improvement. Conversely, if ΔE is sufficiently negative (i.e., lower energy profile or footprint), the invention is likely to represent a genuine technical innovation.
[0060] Finally, the output generation is triggered if the ΔE meets criteria for novelty. In this phase, the system automatically produces a comprehensive report that includes annotated Su-Field diagrams, comparative delta visuals, detailed energy analysis, and even draft patent claims derived from the novel features identified. This end-to-end pipeline automates and objectifies the assessment of inventive step, reducing the dependency on manual review and enabling scalable, cross-domain patent evaluation.
[0061] In an embodiment, the Figure 3 presents a systematic workflow for evaluating the patentability of a technical design by leveraging Su-Field modelling and energy footprint analysis through large language models (LLMs). The process begins when a user enters a design or model for patent evaluation. In the next step, the system employs a Su-Field model, using LLMs to extract and classify the components of the design into Substance 1 (S1), Substance 2 (S2), and Function/Field (F). This breaks the system down into functional interactions necessary for energy modelling. Once the Su-Field representation is complete, the energy footprint of the design is computed, capturing the overall energy characteristics of the system.
[0062] Simultaneously, the system asks the LLM to retrieve similar prior art or known models. The energy footprints of these retrieved models are also calculated, using the same method, thereby creating a baseline for comparison. The system then calculates the delta, or energy differential (ΔE), by subtracting the energy footprint of the invention from that of the prior models. This delta provides a quantitative measure of innovation—a large negative delta typically signifies a more efficient or technically advanced solution.
[0063] If the energy difference exceeds a user-defined threshold, the system considers the design to be potentially unique and patentable. The process then proceeds with other standard checks for patentability, such as novelty, inventive step and utility. If the invention passes these checks, the energy delta itself becomes a foundational basis for claim drafting. The LLM is tasked with automatically generating patent claims based on this quantified innovation, anchoring them in the unique efficiency or improvement highlighted by the energy analysis.
[0064] In an embodiment, the Figure 4 illustrates a flow chart performing a method (400) for determining or analyzing, and interpreting innovation disclosures using a system (102) comprising a processing unit (202) coupled with a storage device (206), in accordance with an embodiment of the present subject matter. The order in which the method (400) may be described may be not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method (400) or alternate methods. Additionally, individual blocks may be deleted from the method (400) without departing from the spirit and scope of the subject matter described herein. Furthermore, the method (400) may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method (400) may be considered to be implemented as described in the system (102) for determining the patentability of the invention using the Su-field analysis.
[0065] At block 402, receiving and parsing, by a natural language processing (NLP) model (208) executed by the processing unit (202), input data comprising one or more of textual descriptions, technical drawings, flowcharts, or CAD models associated with an invention.
[0066] At block 404, extracting and structuring, by the NLP model (208), system elements including technical components, their functions, relationships, and operational context.
[0067] At block 406, receiving, by a large language model (LLM) (210) communicatively coupled to the NLP model (208), structured outputs from the NLP model (208).
[0068] At block 408, performing, by the LLM (210), semantic, syntactic, and functional analysis on the extracted data.
[0069] At block 410, identifying and retrieving, by the LLM (210), prior art from the storage device (206) by querying one or more patent, scientific, and technical databases for similar elements or functionally equivalent systems.
[0070] At block 412, the generating, by the LLM (210), a contextual mapping of the NLP-extracted components in relation to the identified prior art.
[0071] At block 414, receiving, by a Substance-Field (Su-Field) model (212) communicatively coupled to the NLP model (208) and LLM (210), the NLP-extracted components and LLM-identified prior art.
[0072] At block 416, constructing, by the Su-Field model (212), comparative Su-Field models (212) of the invention and the retrieved prior art, each model comprising Substance 1 (S1), Substance 2 (S2), and Field (F) interactions with annotations for field type, intensity, polarity, and interaction efficiency.
[0073] At block 418, generating, the Su-field diagrams for invention and the retrieved prior art, on the basis of Su-field model.
[0074] At block 420, computing, by an energy calculator module (214) communicatively coupled to the Su-Field model (212), an energy profile and energy footprint for both the invention and the prior art based on Su-Field interactions.
[0075] At block 422, calculating, by the energy calculator module (214), an energy delta (ΔE) representing the differential in energetic or functional complexity between the invention and the prior art.
[0076] At block 424, comparing, by a decision module (216) communicatively coupled to the energy calculator module (214), the energy delta (ΔE) to a predefined innovation threshold value.
[0077] At block 426, determining, by the decision module (216), whether the invention exhibits a degree of technical advancement or efficiency gain sufficient to qualify as patentable.
[0078] The Disclosure is explained with the help of example:
[0079] Su-Field Analysis, a core component of TRIZ (Theory of Inventive Problem Solving), is a systematic methodology used to model and resolve issues in technological systems by analyzing interactions between two substances (S1 and S2) and a field (F), which represents the energy or force enabling their interaction. Here, only two substances are used but it can be N number of substances. The primary goal is to eliminate undesirable effects or enhance desired functions by modifying these interactions. The fields in Su-Field Analysis encompass seven types of energy: mechanical (forces and motion), thermal (heat), chemical (reactions), electrical (currents and fields), magnetic (magnetic forces), optical (light and radiation), and acoustic (sound waves).
[0080] For instance, in the case of scissors failing to cut paper effectively, the model identifies S1 as the scissors, S2 as the paper, and F as the mechanical force applied, allowing targeted improvements like sharpening the blades.
[0081] Similarly, in a chemical reactor, inefficiencies might involve raw materials (S1), a catalyst (S2), and chemical energy (F), prompting adjustments to optimize reactions. In pharmaceutical drug discovery, poor bioavailability could involve a drug compound (S1), a biological membrane (S2), and chemical interactions (F), guiding enhancements in drug formulation.
[0082] Even in software development, bug tracking issues can be modelled with software code (S1), a bug tracking tool (S2), and information flow (F), and streamlining debugging processes.
[0083] In the context of invention development, Su-Field Analysis serves as a structured approach to innovate and assess patentability. The process begins with an initial design (Design 1) and uses a language model (LLM) to identify a comparable prior art design (Base Design). Both designs undergo Su-Field Analysis to break them down into their substances and fields, enabling the creation of a new Su-Field model for the invention. A step involves calculating the energy footprint of both models, which quantifies the sum of substances, the number and intensity of fields with positive impacts, and subtracts those with negative impacts. The energy difference between the base and new designs is computed as the product of the number of displaced substances and their degree of displacement, minus any reduction in the number of fields. The delta, or difference in energy footprints between the base and new designs, indicates whether the invention is sufficiently novel for patentability. If patentable, this delta informs the development of key patent claims, crafted with the LLM’s assistance. Energy difference can also be calculated as = (Number of substances displaced between Base and New design * Degree of Displacement) – (Reduction in number of fields)
[0084] Energy Footprint = Sum of Substances + Number of fields which have a positive impact + Intensity of field which have a positive impact - Number of fields which have a negative impact - Intensity of field which have a negative impact
[0085] Delta or energy difference = Energy footprint of Base design – Energy footprint of new design. By leveraging these formulas—energy footprint, and delta—Su-Field Analysis provides a rigorous framework to innovate, optimize, and protect new technological solutions, ensuring they are both effective and distinct from existing designs
[0086] Equivalents
[0087] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for the sake of clarity.
[0088] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present.
[0089] Although implementations for the system and the method for determining the patentability of an invention using substance-field (Su-field) analysis have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features described. Rather, the specific features are disclosed as examples of implementation for the system and the method for determining the patentability of an invention using substance-field (Su-field) analysis.
, C , Claims:

1. A system (102) for determining or analysing and/or interpreting innovation disclosures, the system (102) comprising:
a processing unit (202); and
a storage device (206) coupled to the processing unit (202), wherein the processing unit (202) is capable of executing a plurality of modules stored in the storage device (206), the plurality of modules comprising:
a natural language processing (NLP) model (208) configured to
receive and parse input data comprising one or more of textual descriptions, technical drawings, flowcharts, or CAD models associated with the invention;
extract and structure system elements including technical components, their functions, relationships, and operational context;
a large language model (LLM) (210) communicatively coupled to the NLP model (208), configured to
receive structured outputs from the NLP model (208) and perform semantic, syntactic, and functional analysis on the extracted data, identify and retrieve prior art from the storage device by querying one or more patent, scientific, and technical databases for similar elements or functionally equivalent systems, and generate a contextual mapping of the NLP-extracted components in relation to identified prior art;
a Substance-Field (Su-Field) model (212) communicatively coupled to the NLP model (208) and LLM (210), the Su-Field model (212) being configured to:
receive as input the NLP-extracted components and LLM-identified prior art, construct comparative Su-Field models (212) of the invention and the retrieved prior art and generate a Su-field diagrams for the invention and the retrieved prior art;
an energy calculator module (214) communicatively coupled to the Su-Field model (212), the energy calculator module (214) being configured to:
compute an energy profile and energy footprint for both the invention and the prior art based on Su-Field interactions; and calculate an energy delta (ΔE) representing the differential in energetic or functional complexity between the invention and the prior art;
a decision module (216) communicatively coupled to the energy calculator module (214), the decision module (216) being configured to:
compare the energy delta (ΔE) to a predefined innovation threshold value and determine whether the invention exhibits a degree of technical advancement or efficiency gain sufficient to qualify as patentable;
an output generator module stored in the storage device (206) and communicatively coupled to the decision module (216).
2. The system (102) as claimed in claim 1, wherein the Su-Field models (212) of the invention and the retrieved prior art, comprising: Substance 1 (S1), Substance 2 (S2), and Field (F) interactions;
annotations for field type, intensity, polarity (positive/negative), and interaction efficiency;

3. The system (102) of claim 1, wherein the NLP model (208) is further configured to classify the extracted system elements using a TRIZ-based framework.

4. The system (102) as claimed in claim 1, wherein the LLM (210) is trained on a corpus comprising patent literature, technical specifications, and industrial design documents.

5. The system as claimed in claim 1, wherein the decision module (216) determines patentability based on:
  
    ΔE = Energy Footprint (prior art) − Energy Footprint (invention).

6. The system (102) as claimed in claim 1, wherein the energy footprint is calculated using the formula:
Energy Footprint = (Number of Substances) + (Positive Field Count × Avg. Intensity) − (Negative Field Count × Avg. Intensity).

7. The system (102) as claimed in claim 1, wherein the output generator module is further configured to generate annotated Su-Field diagrams highlighting novel field interactions identified during analysis.

8. The system (102) as claimed in claim 1, wherein the decision module (216) comprises logic for threshold tuning based on domain-specific innovation criteria or user-defined novelty benchmarks.

9. The system (102) as claimed in claim 1, wherein the output generator module generates draft patent claims based on features associated with field improvements or reductions in energy complexity.

10. A method (400) for determining or analyzing and interpreting innovation disclosures using a system (102) comprising a processing unit (202) coupled with a storage device (206), the method comprising:
receiving and parsing, by a natural language processing (NLP) model (208) executed by the processing unit (202), input data comprising one or more of textual descriptions, technical drawings, flowcharts, or CAD models associated with an invention;
extracting and structuring, by the NLP model (208), system elements including technical components, their functions, relationships, and operational context;
receiving, by a large language model (LLM) (210) communicatively coupled to the NLP model (208), structured outputs from the NLP model (208);
performing, by the LLM (210), semantic, syntactic, and functional analysis on the extracted data;
identifying and retrieving, by the LLM (210), prior art from the storage device (206) by querying one or more patent, scientific, and technical databases for similar elements or functionally equivalent systems;
generating, by the LLM (210), a contextual mapping of the NLP-extracted components in relation to the identified prior art;
receiving, by a Substance-Field (Su-Field) model (212) communicatively coupled to the NLP model (208) and LLM (210), the NLP-extracted components and LLM-identified prior art;
constructing, by the Su-Field model (212), comparative Su-Field models (212) of the invention and the retrieved prior art, each model comprising Substance 1 (S1), Substance 2 (S2), and Field (F) interactions with annotations for field type, intensity, polarity, and interaction efficiency;
generating, the Su-field diagrams for invention and the retrieved prior art, on the basis of Su-field model;
computing, by an energy calculator module (214) communicatively coupled to the Su-Field model (212), an energy profile and energy footprint for both the invention and the prior art based on Su-Field interactions;
calculating, by the energy calculator module (214), an energy delta (ΔE) representing the differential in energetic or functional complexity between the invention and the prior art;
comparing, by a decision module (216) communicatively coupled to the energy calculator module (214), the energy delta (ΔE) to a predefined innovation threshold value; and
determining, by the decision module (216), whether the invention exhibits a degree of technical advancement or efficiency gain sufficient to qualify as patentable.