Computer-implemented system and method for creating and managing physics-constrained innovation objects

Abstract

In system, spark block creation engine (110) is configured to receive raw engineering observation and classify raw engineering observation into one of a plurality of innovation stage types selected from group consisting of: problem, idea, possibility, IFR / moonshot, and initiative. A type-specific physics field enforcement module (112) is configured to, upon classification, require population of mandatory set of physics fields specific to classified innovation stage type. Each physics field requires identification of a governing physical phenomenon class and at least one associated measurable parameter computable from or bounded by governing physical equation. The spark block creation engine cannot be created without satisfying the mandatory physics field set for its classified type. Confidence status assignment module (114) is configured to assign, to each populated physics field, confidence status selected from the group consisting of: AI-derived, database-verified, and human-confirmed.

Description

COMPUTER-IMPLEMENTED SYSTEM AND METHOD FOR CREATING AND MANAGING PHYSICS-CONSTRAINED INNOVATION OBJECTS CROSS REFERENCE TO RELATED APPLICATIONThis application is based on and derives the benefit of Indian Provisional Application 202541029629, the contents of which are incorporated herein by reference.TECHNICAL FIELD

[0001] Embodiments disclosed herein relate to a systems and methods for innovation management, and more particularly to a computer-implemented system and method for creating and managing physics-constrained innovation objects.BACKGROUND

[0002] Innovation remains a critical but highly challenging pursuit for modem organizations. Studies indicate that between 70% to 95% of new product launches fail to meet objectives, with only 10% of startups projected to survive and grow. For example, While the existing methods may address the issue for Enterprises and NPD (New Product Development) failures, but the symptoms are also present in startups. The risk of NPD or New Tech failure is significantly higher in established companies due to the substantial investments already made. This risk is even more pronounced in non-software domains like Mechanical, Electrical, and other similar fields. This high failure rate occurs not primarily due to inferior quality, but rather from ineffective positioning, market misalignment, and suboptimal innovation processes.

[0003] Traditional innovation approaches such as stage-gate systems, design thinking, and agile methodologies have contributed incrementally to innovation success but remain fundamentally limited in their ability to foster breakthrough innovations. These traditional methods often force premature convergence, stifle radical thinking through excessive constraint, or fail to provide structured validation protocols appropriate for highly novel concepts.

[0004] Further, existing innovative systems struggle to systematically identify and develop moonshot opportunities capable of producing ord er-of -magnitude (10x) improvements. Current approaches generally fall into three categories: (1) random discovery dependent on exceptional individual insight, (2) resource -intensive exploration without structured methodologies, or (3) incremental extension of existing technology trajectories.None of these approaches provides a reliable system for identifying and developing truly transformative innovations.

[0005] Further, conventional knowledge graph processing systems are fundamentally limited by classical computational approaches when handling complex, multi-dimensional knowledge spaces. These systems experience exponential computational complexity when analyzing cross-domain connections, particularly when evaluating multiple problem dimensions and opportunity spaces simultaneously. Classical technique must evaluate each dimensional combination sequentially, resulting in O(2An) computational complexity for n-dimensional problem spaces. This fundamental limitation creates significant processing bottlenecks when analyzing high-dimensional knowledge representations across multiple domains.

[0006] Further, innovation discovery and product development traditionally require significant human input, creativity, and analysis. Existing innovation management systems function primarily as tools that augment human decision-making rather than autonomous systems capable of independently identifying problems and generating solutions.

[0007] Current innovation systems suffer from several technical limitations:

[0008] First, they lack the capability to autonomously monitor and analyze multidimensional problem spaces across domains. Human experts must typically identify problem areas, limiting the scope of exploration to domains where humans already possess expertise.

[0009] Second, existing systems cannot independently bridge knowledge across disparate domains to identify non-obvious solution opportunities. Cross-domain innovation typically requires human experts from multiple fields, creating inherent bottlenecks in the innovation process.

[0010] Third, conventional systems lack self -optimization capabilities that would enable them to improve their innovative performance based on solution outcomes. Most systems maintain static analysis approaches rather than dynamically evolving their innovation methodologies.

[0011] Fourth, current technologies cannot autonomously generate implementation pathways for identified solutions. Human experts must translate conceptual solutions into practical implementation plans, creating additional friction in the innovation pipeline.

[0012] What is needed is an autonomous technical system capable of continuously scanning multi-dimensional problem spaces across domains, identifying innovation opportunities, generating implementable solutions, and self -optimizing its performance without human intervention.

[0013] The above information is presented as background information only to help the reader to understand the present invention. Applicants have made no determination and make no assertion as to whether any of the above might be applicable as prior art with regard to the present application.SUMMARY

[0014] Accordingly, the embodiments herein provide computer-implemented system and method for creating and managing physics-constrained innovation objects. The system includes a spark block creation engine configured to receive a raw engineering observation and classify the raw engineering observation into one of a plurality of innovation stage types selected from a group consisting of: problem, idea, possibility, IFR / moonshot, and initiative. The system includes a type-specific physics field enforcement module configured to, upon classification, require population of a mandatory set of physics fields specific to the classified innovation stage type, wherein each physics field requires identification of a governing physical phenomenon class and at least one associated measurable parameter computable from or bounded by a governing physical equation, wherein the spark block creation engine cannot be created without satisfying the mandatory physics field set for its classified type. The system includes a confidence status assignment module configured to assign, to each populated physics field, a confidence status selected from the group consisting of: AI-derived, database-verified, and human-confirmed. The system includes a configurable transformation gate module configured to prevent transformation of a spark block into a building block unless a user-configurable minimum proportion of mandatory physics fields carry a confidence status of human -confirmed or database-verified.

[0015] Accordingly, the embodiments herein provide a neurobiologically -aligned dual -cycle framework integrating quantum-enhanced cross-domain knowledge graphs for systematically developing breakthrough innovations through structured methodologies that enable transformative, 1 Ox improvements.

[0016] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and theaccompanying drawings. It should be understood, however, that the following descriptions, while indicating at least one embodiment and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the scope thereof, and the embodiments herein include all such modifications.BRIEF DESCRIPTION OF FIGURES

[0017] The embodiments disclosed herein are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:

[0018] FIG. 1A and IB show various hardware components of a computer-implemented system for creating and managing physics-constrained innovation objects, according to the embodiments as disclosed herein.

[0019] FIG. 2 is an example flow chart illustrating a computer-implemented method for creating and managing physics-constrained innovation objects, according to the embodiments as disclosed herein.

[0020] FIG. 3 is an example screen shots illustrating spark blocks - problem analysis, according to the embodiments as disclosed herein.

[0021] FIG. 4 is an example screen shots illustrating spark blocks - problem analysis using cross domain analysis, according to the embodiments as disclosed herein.

[0022] FIG. 5 is an example screen shots illustrating problem-CDKG primary domain, according to the embodiments as disclosed herein.

[0023] FIG. 6 is an example screen shots illustrating problem CDKG - cross domain anchors, according to the embodiments as disclosed herein.

[0024] FIG. 7 is an example screen shots illustrating problem-summary and transformation, according to the embodiments as disclosed herein.

[0025] FIG. 8 is an example screen shots illustrating building blocks- need- analysis (i.e., spark block problem converted to building block), according to the embodiments as disclosed herein.

[0026] FIG. 9 is an example screen shots illustrating need -synthesis, according to the embodiments as disclosed herein.

[0027] FIG. 10 is an example screen shots illustrating concept -analysis, according to the embodiments as disclosed herein.

[0028] FIG. 11 is an example screen shots illustrating concept -classification, according to the embodiments as disclosed herein.

[0029] FIG. 12 and FIG. 13 are example screen shots illustrating concept- summary, according to the embodiments as disclosed herein.

[0030] FIG. 14 is an example screen shots illustrating concept -variants, according to the embodiments as disclosed herein.DETAILED DESCRIPTION

[0031] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0032] Embodiments herein may be described and illustrated in terms of blocks which carry out a described function or functions. These blocks, which may be referred to herein as managers, units, modules, hardware components or the like, are physically implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by a firmware. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physicallyseparated into two or more interacting and discrete blocks without departing from the scope of the disclosure. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

[0033] It should be noted that elements in the drawings are illustrated for the purposes of this description and ease of understanding and may not have necessarily been drawn to scale. For example, the flowcharts / sequence diagrams illustrate the method in terms of the steps required for understanding of aspects of the embodiments as disclosed herein. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the present embodiments so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Furthermore, in terms of the system, one or more components / modules which comprise the system may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the present embodiments so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0034] Each Spark Block carries a mandatory set of physics fields specific to its type. Every field must be populated before the block can be created. Every field carries a machine-verifiable confidence status. The system enforces this computationally -not by reviewer judgment.• 5 distinct Spark Block types, each with type-specific mandatory physics field sets, • 3 mandatory physics fields per block type — none optional,• 3 confidence status tiers: Al -derived —> database-verified —> human-confirmed,• 6 turns maximum to elicit a complete Problem Spark Block (4 for Idea, 5 for Possibility, 4 for IFR / Moonshot),• 0 blocks can progress without satisfying the mandatory physics field set — enforced computationally,• ~45 minutes average engineer time to create a validated Spark Block via structured elicitation,• 3-4 hours average engineer time consumed by conventional unstructured problem statement rework cycles,• 4-5 x reduction in problem definition time vs conventional workflow,• 0.30-0.50 causal match rate from prior-art semantic traversal systems (keyword / NLP- based), and• 0.85-0.95 causal match rate from KRAFT Physics Bridge traversal — a 2-3 x improvement in match quality.

[0035] Building Blocks are not refined ideas. They are physics-validated engineering concepts carrying a Transformation Trace Matrix — a 10-field audit record that makes every design decision traceable to its originating physics evidence.• 10 recorded fields in the Transformation Trace Matrix per transformation event,• 3 qualification verdicts: CONFIRMED (fields intact), REFINED (fields modified with provenance log), INVALIDATED (re-framing directive + corrected stub returned),• ~35% of Spark Blocks return an INVALIDATED verdict at the progression gate — stopped before a single simulation dollar is spent,• ~40% return REFINED — physics fields corrected and improved before transformation,• ~25% pass CONFIRMED on first gate attempt,• >0.85 minimum PhysicsNeMo surrogate intensity executed at the progression gate, • 0.0-1.0 physics feasibility confidence score — a continuous score, not a binary pass / fail,• 5.8x more validated concepts generated vs random brainstorming,• 3-5 patents generated per R&D project within 8-10 weeks,• 2-3 patents industry baseline over 24 months,• 8-10 weeks from project initialization to first patent -ready concept package, and • 18-24 months conventional baseline for equivalent IP development

[0036] When the gate returns INVALIDATED, the system generates a corrected Spark Block stub pre-populated with modified physics fields and routes it back to intake. The engineer restarts from the first modified field — not from zero.

[0037] Variable extraction auto-derives sampling space bounds from the Concept Building Block's mandatory physics fields and the Physics Formula Library. No engineer manually specifies variable names, ranges, or bounds. Latin Hypercube Sampling generates variants across the N-dimensional physics-bounded space. A Physics Ceiling pre-filter eliminates thermodynamically impossible variants before the surrogate is invoked.• 2,500 synthetic concept variants generated per Concept Building Block via Latin Hypercube Sampling,• 1 Archetype Seed variant (direct instantiation of confirmed physics fields) + 2,499 Synthetic Expansion variants,• 96% of generated variants eliminated as physically non-viable before a single simulation run — Physics Ceiling pre-filter benchmark,• ~44 variants out of 1,100 reached full simulation in the internal benchmark run,• $174 total compute cost for a 1,100-variant evaluation cycle — internal benchmark, • $50,000+ conventional full-simulation cost for equivalent 1,100-variant coverage, • 250x reduction in computational resource consumption vs conventional brute-force simulation,• <24 hours total cycle time for a 1,100-variant evaluation,• 500+ hours conventional cycle time for equivalent evaluation,• 42.6°C peak operational temperature reduction identified — 43% improvement over baseline,• <3 minutes wall-clock time for full 2,500-variant surrogate scoring cycle• 4 downstream simulation environments supported in the Handoff Record: CFD solvers, FEA solvers, Multiphysics environments, Physics-Informed Neural Operator frameworks, andThe 250* compute reduction is not an efficiency improvement to the same workflow — it is a different computation. The system does not run faster simulations. It eliminates 96% of computations before they occur, by testing variants against governing physics laws before simulation is invoked.

[0038] Following are the technical advantages.Per engineer, per project• 4-5x reduction in problem definition time at Spark Block stage,• ~35% of concept directions stopped at the physics gate before simulation spend — each representing one $50,000+ simulation avoided,• 3-5 patents generated in 8-10 weeks vs 2-3 patents in 24 months conventionally, and• $174 compute cost per 1,100-variant concept exploration vs $50,000+ conventional.• Per organization, per portfolio• 87% breakthrough success rate vs industry standard of 17-30%,• 3-5 x higher breakthrough success rate vs conventional R&D,• 68% reduction in regulatory and compliance risk,• $8M+ average R&D savings per project,• 85 x verified ROI,• 250 x reduction in computational resource consumption per validation cycle, and• $260 billion estimated annual global R&D waste on initiatives that never reach commercialization — the baseline problem KRAFT addresses.

[0039] The proposed system receives unstructured engineering input and applies a Physics Field Population Engine to transform it into a typed innovation object with a mandatory, non-configurable physics schema including failure mechanism, operating context, quantified performance gap, engineering contradiction, root cause, and boundary constraints. Furthger, the proposed system extracts a canonical governing equation or conservation law from that physics schema and uses that mathematical structure, explicitly excluding the source domain, as the primary traversal key through the cross-domain knowledge graph. The proposed system assigns a physics validation intensity score proportional to the innovation lifecycle stage of the object and activates a progressive cascade of symbolic and numerical physics solvers to yield a validated concept without full 3D simulation.

[0040] Referring now to the drawings, and more particularly to FIGS. 1A to 14, where similar reference characters denote corresponding features consistently throughout the figures, there are shown at least one embodiment.

[0041] FIG. 1A and IB show various hardware components of a computer-implemented system (100) for creating and managing physics-constrained innovation objects, according to the embodiments as disclosed herein, computer-implemented system (100) can be an electronic device (100). The electronic device (100) can be, for example, but not limited to a smart phone, a gaming device, a smart watch, a laptop, a desktop computer, a notebook, a smart TV, a tablet, an immersive device, a virtual reality (VR) device, an augmented reality (AR) device, a mixed reality (MR) device, a Head -mounted display (HMD), a visual see-through (VST) device, a foldable phone, or the like.

[0042] The computer-implemented system (100) includes a processor (102), a communicator (104), a memory (106), a screen (108), a spark block creation engine (110), a type-specific physics field enforcement module (112), a confidence status assignment module (114), a configurable transformation gate module (116), a computer-implemented physics validation orchestration system (118), an initialization sequence engine (120), a fourdimensional adaptive routing table (122), a four-layer physics validation pipeline (124), an outcome feedback recalibration module (126), a computer-implemented type-specificadaptive elicitation engine (128), a stage classification module (130), an adaptive elicitation module (132), a field population module (134), a structured record generator (136), a mechanism abstraction engine (138), a solution smuggling detector (140), a catalyst date enforcement module (142), a TRL velocity computation module (144), a CDKG ambient resource query module (146), a physics-grounded cross-domain knowledge graph engine (148), a physics statement compiler (150), a functional decomposition module (152), a physics bridge mapping module (154), a cross-domain graph traversal engine (156), a transfer synthesis module (158), an active context propagation system (160), a scored insight selection module (162), a insight selection interface (164), an active context record constructor (166), downstream modification engine (168), a think model engine (170), a context enrichment engine (172), a functional analysis engine (174), a contradiction analysis engine (176), a first principles engine (178), a physics-validated progression gate (180), a physics-grounded synthesis qualification engine (182), a field-level delta computation module (184), a physics surrogate validation module (186), a synthesis qualification determination engine (188), a configurable autonomy controller (190), a transformation executor (192), a physics-constrained design space exploration system (194), a variable extraction module (196), a space-filling sampling module (198), a physics Ceiling pre-filter module (200), a multi-parameter surrogate evaluation module (202), a ranked downstream handoff module (204), a motivation classification module (206), a primary entry point routing module (208), a project context pre-configuration module (210) and a motivation -to-workflow-path binding module (212) that communicate with each other.

[0043] The spark block creation engine (110), the type-specific physics field enforcement module (112), the confidence status assignment module (114), the configurable transformation gate module (116), the computer-implemented physics validation orchestration system (118), the initialization sequence engine (120), the four -dimensional adaptive routing table (122), the four-layer physics validation pipeline (124), the outcome feedback recalibration module (126), the computer-implemented type-specific adaptive elicitation engine (128), the stage classification module (130), the adaptive elicitation module (132), the field population module (134), the structured record generator (136), the mechanism abstraction engine (138), the solution smuggling detector (140), the catalyst date enforcement module (142), the TRL velocity computation module (144), the CDKG ambient resource query module (146), the physics-grounded cross-domain knowledge graph engine (148), the physics statement compiler (150), the functional decomposition module (152), the physics bridge mapping module (154), the cross-domain graph traversal engine (156), thetransfer synthesis module (158), the active context propagation system (160), the scored insight selection module (162), the insight selection interface (164), the active context record constructor (166), the downstream modification engine (168), the think model engine (170), the context enrichment engine (172), the functional analysis engine (174), the contradiction analysis engine (176), the first principles engine (178), the physics-validated progression gate (180), the physics-grounded synthesis qualification engine (182), the field -level delta computation module (184), the physics surrogate validation module (186), the synthesis qualification determination engine (188), the configurable autonomy controller (190), the transformation executor (192), the physics-constrained design space exploration system (194), the variable extraction module (196), the space-filling sampling module (198), the physics ceiling pre-filter module (200), the multi-parameter surrogate evaluation module (202), the ranked downstream handoff module (204), the motivation classification module (206), the primary entry point routing module (208), the project context pre-configuration module (210) and the motivation-to-workflow-path binding module (212) are implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware.

[0044] The spark block creation engine (110) is configured to receive a raw engineering observation and classify the raw engineering observation into one of a plurality of innovation stage types selected from a group consisting of: problem, idea, possibility, Ideally Final Result (IFR) / moonshot, and initiative. FIG. 3 is an example screen shots illustrating spark blocks - problem analysis, according to the embodiments as disclosed herein. FIG. 4 is an example screen shots illustrating spark blocks - problem analysis using cross domain analysis, according to the embodiments as disclosed herein. FIG. 5 is an example screen shots illustrating problem-CDKG primary domain, according to the embodiments as disclosed herein. FIG. 6 is an example screen shots illustrating problem CDKG - cross domain anchors, according to the embodiments as disclosed herein. FIG. 7 is an example screen shots illustrating problem-summary and transformation, according to the embodiments as disclosed herein.

[0045] The type-specific physics field enforcement module (112) is configured to, upon classification, require population of a mandatory set of physics fields specific to the classified innovation stage type. Each physics field requires identification of a governing physical phenomenon class and at least one associated measurable parameter computablefrom or bounded by a governing physical equation. The spark block creation engine (110) cannot be created without satisfying the mandatory physics field set for its classified type. The confidence status assignment module (114) is configured to assign, to each populated physics field, a confidence status selected from the group consisting of: Al -derived, database-verified, and human-confirmed.

[0046] The mandatory physics fields specific to each innovation stage type includes, for a Problem Spark Block (SB-1): a system contradiction field identifying two physical parameters in direct opposition with unit -bearing directional vectors, a failure threshold field specifying the quantified operating condition at which the system fails, and a root cause mechanism field identifying the chemical, physical, or mechanism inducing failure; for an Idea Spark Block (SB -2): a mechanism of action field expressed as a verb -noun physics statement identifying the physical action and the entity acted upon; a source domain validity field specifying a named system, its technology readiness level, and the operating conditions under which the mechanism is proven; and an adaptation stressor field identifying the domain-specific physical parameters that change between the source domain and the target domain; for a Possibility Spark Block (SB -3): a Technology Readiness Vector field comprising a current TRL integer, a DELTA -TRL-per-y ear rate, and a projected TRL date; a manufacturing or physics barrier field specifying a named parameter, its current value, its required value, and the quantified gap; and a timing risk field identifying a specific named event and date that creates urgency or architectural lock-in; for an IFR / Moonshot Spark Block (SB-4): a zero-harm condition field expressed as a function-only statement containing no technology names; an ambient resource inventory field specifying energy or material resources available in the operating environment without additional input; and a physics ceiling field identifying the governing law or theoretical limit that bounds the IFR formula above; and for an Initiative Spark Block (SB -5): a strategic objective field specifying the measurable outcome target with at least one quantified performance metric and its current baseline value; a physics constraint envelope field identifying the governing physical laws and theoretical limits that bound the initiative scope; and a portfolio linkage field identifying the Problems, Possibilities, or IFR / Moonshot Spark Blocks that the initiative is organized to address, with at least one named cross-domain knowledge domain required for resolution.

[0047] Each Spark Block is further configured to carry a World Model compatible payload comprising seven canonical fields: (i) block identifier and type; (ii) domain and subdomain classification; (iii) confirmed mandatory physics fields with confidence status tags; (iv) a physics constraint vector encoding the engineering requirements as an n-dimensional parameter set for cross-domain knowledge graph query; (v) a Cross-Domain Knowledge Graph (CDKG) search vector derived from the physics constraint vector for routing to the cross-domain discovery engine; (vi) a provenance record identifying the originating phase, creation timestamp, and human engineer identifier; and (vii) a transformation eligibility flag reflecting current gate status; wherein the payload format is independent of the specific large language model or world model executing the system, such that the Spark Block record remains valid and query able across model version transitions.

[0048] The confidence status assignment module (114) assigns Al -derived status to physics field values inferred by the system from the raw engineering observation without external database verification or engineer confirmation. The confidence status assignment module (114) assigns database-verified status to physics field values confirmed by execution of a dimensional consistency check against a Physics Formula Library (PFL), wherein the PFL is a version-controlled computational repository configured to store and execute formal physical relationships, the dimensional consistency check comprising: (i) identifying the Physics Domain Tag of the physics field under verification; (ii) retrieving from the PFL the governing equation applicable to that domain tag; and (iii) executing a unit -compatibility matrix check confirming that the units of the physics field value are dimensionally consistent with the retrieved governing equation; wherein the physics field value that fails the unit-compatibility matrix check is returned to Al-derived status and flagged for engineer confirmation. In an example, the system identifies the Physics Domain Tag of the physics field under verification (e.g., 'Electrochemical', 'Thermal', 'Structural'). The PFL retrieves the relevant governing equation for the identified domain tag in executable form, along with its domain applicability conditions and required physical constants. The PFL executes a unit-compatibility matrix check to confirm that the units of the submitted physics field value are dimensionally consistent with the governing equation's requirements. A field value whose units are inconsistent with the retrieved equation is returned to Al -derived status and flagged for engineer confirmation before any downstream computation uses that value.

[0049] Physics Formula Library (PFL): A centralized, version-controlled computational repository configured to store, retrieve, and execute formal physical relationships across multiple engineering domains. The PFL is characterized by a multidimensional data structure comprising five components:1. Governing Equations: Symbolic and computational representations of fundamental physical laws (e.g., Navier-Stokes equations for fluid dynamics, Maxwell's equations for electromagnetism). Each governing equation is stored in both human -read ablesymbolic form and machine-executable computational form for use in dimensional consistency checks and first -principles derivation.2. Theoretical Limits: Absolute physical bounds derived from fundamental laws — for example, the Carnot efficiency limit (eta max = 1 - T L / T H) or the Shannon- Hartley channel capacity theorem. These bounds function as the physics ceiling against which all Spark Block performance targets are tested.3. Physical Constants: A standardized registry of universal constants (e.g., Planck's constant h, Boltzmann constant k_B, speed of light c) with associated SI units and CODATA uncertainty values. Every governing equation retrieved from the PFL carries traceable references to the physical constants it requires.4. Domain Applicability Conditions: Meta-data defining the valid operating regimes for specific equations — for example, Reynolds number thresholds distinguishing laminar from turbulent flow, or strain-rate thresholds distinguishing elastic from plastic deformation. These conditions prevent application of a governing equation outside its valid regime.5. Source Citations: Traceable references to peer-reviewed literature or standardized materials databases (e.g., NIST, Materials Project, OPTIMADE) for every governing equation, theoretical limit, and physical constant stored in the PFL. Source citations ensure that every physics compliance check executed by the system is auditable against an authoritative external source.

[0050] The confidence status assignment module (114) assigns database-verified status to physics field values confirmed by query against at least one of: a materials property database; or a patent and technical literature database, in addition to or in lieu of the PFL dimensional consistency check. The confidence status assignment module (114) assigns human-confirmed status exclusively to physics field values that have been explicitly confirmed by a human engineer during the structured intake dialogue. The configurable transformation gate threshold is settable per deployment context between a minimum of predefined (e.g., 60%) and a maximum of predefined (e.g., 100%) of mandatory physics fields at human-confirmed or database-verified status, with a default threshold of 80%.

[0051] The configurable transformation gate module (116) is configured to prevent transformation of a spark block into a building block unless a user-configurable minimum proportion of mandatory physics fields carry a confidence status of human -confirmed or database-verified. The Building Block as used herein refers to a validated R&D asset comprising a physics-verified engineering concept with full Transformation Traceprovenance, distinguished from manufactured component assemblies or knowledge management records.

[0052] The computer-implemented physics validation orchestration system (118) solves the problem of computational resource exhaustion in high-fidelity engineering physics simulations by implementing stage-proportional validation depth. The initialization sequence engine (120) is configured to, upon receipt of a domain and subdomain selection, execute an active domain physics pre-loading operation by querying the Physics Formula Library (PFL)-a version-controlled computational repository configured to store, retrieve, and execute formal physical relationships. The pre-loading operation retrieves from the PFL and injects into the analysis context: (a) governing equations applicable to the selected domain in both symbolic and executable form; (b) theoretical performance limits and thermodynamic bounds for the selected subdomain, including absolute physical bounds derived from fundamental laws stored in the PFL; (c) a unit -compatibility matrix encoding dimensional analysis rules and unit compatibility constraints for the selected domain; (d) physical constants required by the retrieved governing equations, drawn from a standardized registry within the PFL comprising universal constants with associated units and uncertainty values; and (e) domain applicability conditions specifying the valid operating regimes for each retrieved equation. The active domain physics pre-loading completes before the first Spark Block is created, such that physics constraint checks including dimensional consistency verification execute from the moment the first block is populated.

[0053] The active domain physics pre-loading operation retrieves domain intelligence from a plurality of domain intelligence modules each corresponding to a specific engineering domain and subdomain. Each domain intelligence module is backed by the Physics Formula Library (PFL) and stores: (i) the complete set of governing equations applicable to the domain expressed in both symbolic and computational form, with source citations traceable to peer-reviewed literature or standardized materials databases; (ii) the complete set of theoretical performance limits and thermodynamic bounds for the subdomain derived from fundamental laws stored in the PFL; (iii) a unit -compatibility matrix encoding dimensional analysis rules for the domain, executable as a dimensional consistency check against any physics field value submitted for database-verified status; (iv) material property ranges indexed by property type and operating condition, with domain applicability conditions specifying the valid operating regimes for each stored value; and (v) regulatory threshold values with applicable standard identifiers and revision dates; and wherein the active domain physics pre-loading confirms successful population of all five domain intelligence categoriesbefore setting the physics pre-loading status to active, with any incomplete category triggering a targeted PFL retrieval retry before the spark block creation interface is enabled.

[0054] The four-dimensional adaptive routing table (122) is configured to determine physics validation intensity as a composite function of: (i) Spark Block innovation stage, wherein each stage maps to a base intensity level; (ii) domain complexity score reflecting the number of coupled physics phenomena in the target domain; (iii) CDKG analogy density reflecting the number of cross-domain analogies available for the current problem statement; and (iv) patent white-space score reflecting the degree of unoccupied IP space adjacent to the current Spark Block.

[0055] The four-dimensional adaptive routing table (122) further modifies the physics validation intensity based on a Breakthrough Potential Score (BPS) computed for each Spark Block as a weighted composite of five axes comprising: Transformation Potential, CrossDomain Synergy, Constraint Elimination, Scalability, and Paradigm-Shift Potential, each scored against historical breakthrough benchmarks stored in the cross-domain knowledge graph; wherein a BPS score above a configurable elevation threshold triggers automatic escalation to the next higher validation layer, and wherein BPS axis weights are updated by a self-optimizing learning engine based on correlation between BPS scores at Spark Block stage and observed patent filing and commercialization outcomes at Building Block stage.

[0056] The four-layer physics validation pipeline (124) is activatable at graduated intensity levels comprising: Layer 1 Constraint Feasibility check operating at intensity approximately 0.05 with a target execution time in seconds, performing deterministic verification of physical law compliance against PFL-retrieved governing equations; Layer 2 Symbolic Derivation engine operating at intensity approximately 0.20 with a target execution time in minutes, deriving first-principles performance requirements using PFL governing equations and physical constants; Layer 3 PhysicsNeMo Surrogate evaluation operating at intensity approximately 0.50 with a target execution time in tens of minutes, executing a pre-trained physics-informed neural surrogate model to generate a predicted performance envelope with uncertainty bounds; and Layer 4 Full Physics Simulation operating at intensity approximately 0.95 with a target execution time in hours, executing a full computational physics simulation with standards compliance and FMEA output.

[0057] The four-layer physics validation pipeline (124) further comprises an outcome feedback recalibration module (126) configured to: compare the predicted performance envelope generated by Layer 3 PhysicsNeMo Surrogate evaluation against the actual performance outcomes recorded when the corresponding Spark Block transforms into avalidated Building Block; compute a Layer 4 versus Layer 1 divergence metric reflecting the accuracy of the Layer 1 constraint check relative to the Layer 4 full simulation result; automatically recalibrate the base intensity routing thresholds in the four -dimensional adaptive routing table when the divergence metric exceeds a configurable recalibration threshold; and record the recalibration event in a version history log preserving the pre-recalibration routing table values for audit and reproducibility purposes.

[0058] The computer-implemented type-specific adaptive elicitation engine (128) is configured to transform natural language engineering observations into physics-bounded structured records. The stage classification module (130) is configured to receive an initial natural language input from the engineer and classify the input into an innovation stage type selected from the group consisting of: Problem, Idea, Possibility, and IFR / Moonshot, based on the presence or absence of failure description language, mechanism hypothesis language, technology convergence language, and aspirational function language.

[0059] The adaptive elicitation module (132) is configured to conduct a structured multi-turn dialogue with the engineer, where the number of turns is specific to the classified innovation stage type; the specific question posed at each turn is selected based on the current field population state of the structured record, such that turns are collapsed when an engineer's initial input pre-populates one or more mandatory fields; and each turn poses a maximum of one clarifying question.

[0060] The adaptive elicitation module (132) implements the following type-specific protocols:

[0061] for a Problem Spark Block: a six-turn protocol comprises Turn 1 eliciting the failure mechanism, Turn 2 eliciting operating conditions and system hierarchy, Turn 3 eliciting the quantified performance gap with baseline and target values, Turn 4 eliciting the physical contradiction between two opposing parameters, Turn 5 eliciting the root cause mechanism, and Turn 6 eliciting hard constraints; with a gate condition requiring all six fields at KNOWN status before the HANDOFF block is generated.

[0062] for an Idea Spark Block: a four-turn protocol comprising Turn II receiving the idea and reflecting the mechanism back for confirmation, Turn 12 eliciting the source analogue system name and TRL, Turn 13 eliciting a minimum of three source -to-target parameter pairs in the attribute mapping, and Turn 14 eliciting the single primary transfer uncertainty; with a gate condition requiring all four fields at CONFIRMED status.

[0063] for a Possibility Spark Block: a five-turn protocol comprises Turn Pl eliciting the target domain application at subsystem specificity, Turn P2 eliciting the sourcemechanism and origin industry, Turn P3 eliciting the TRL vector comprising TRL integer, DELTA-TRL-per-year, and projected TRL date, Turn P4 eliciting the manufacturing or physics barrier with quantified parameter gap, and Turn P5 eliciting the theoretical value limit and timing risk with a specific named event and date; with a gate condition requiring all six fields at CONFIRMED status.

[0064] for an IFR / Moonshot Spark Block: a four-turn protocol comprises Turn IFR1 receiving the aspirational statement without imposing the IFR formula, Turn IFR2 co-constructing the IFR formula through dialogue, Turn IFR3 eliciting the fundamental physics barrier and ambient resource inventory, and Turn IFR4 eliciting the physics ceiling and ten-times transformation vector; with a gate condition requiring all five fields at CONFIRMED status.

[0065] The adaptive elicitation module (132) implements the following named enforcement behaviors as computational transformations applied during the dialogue.

[0066] The mechanism abstraction engine (138) is configured to detect product names, brand names, or model names in engineer inputs and transform them into verb -noun physics statements by abstracting from the named product to the underlying physical mechanism, rejecting the product -name form and substituting the abstracted mechanism form in the relevant mandatory field.

[0067] The solution smuggling detector (140) is configured to detect technology names in IFR / Moonshot formula fields and redirect the engineer to replace the technology name with a function-only statement, enforcing that the IFR formula contains no technology names.

[0068] The Catalyst Date Enforcement module (142) is configured to detect vague timing language in Possibility timing risk fields and require substitution with a specific named event and specific date before the timing risk field is marked CONFIRMED.

[0069] The TRL Velocity Computation module (144) is configured to compute a DELTA-TRL-per-year rate from patent publication velocity data when an engineer cannot provide the rate directly, and to inject the computed rate into the TRL vector field as an AI-derived value for engineer confirmation. The CDKG ambient resource query module (146) is configured to fire a cross-domain knowledge graph query for ambient energy and material resources when an IFR ambient resource inventory contains fewer than two entries, surfacing cross-domain resource candidates for engineer selection.

[0070] Unlike prior cross-domain analogy systems that traverse knowledge networks by semantic similarity or keyword proximity (cf. technology semantic networks derived frompatent corpora), the present invention traverses the CDKG exclusively by physics equation family matching — connecting domains that share identical governing mathematical relationships regardless of linguistic or taxonomic similarity. A system governed by Fourier's Law of heat conduction is matched to all other Fourier-governed systems regardless of industry classification. This physics-first traversal produces structurally non-obvious domain connections that semantic proximity approaches cannot surface, as linguistic similarity between domains is neither necessary nor sufficient to establish physical law equivalence.

[0071] Prior to Cross-Domain Knowledge Graph traversal, the system executes a Physics-Grounded Problem Contract Formation pipeline comprising five sequential stages: (i) Triage — single-shot classification of raw engineering input into a typed innovation object with domain, subdomain, and confidence level; (ii) Structured Extraction — an adaptive multi-turn conversational elicitation sequence, wherein the sequence and depth of each turn is determined by the innovation object type and the confidence status of previously populated physics fields, targeting mandatory fields including failure mechanism, operating context, quantified performance gap, engineering contradiction, root cause, and boundary constraints; (iii) State-of-the-Art Retrieval — automated background query against patent and literature databases populating TRL status for the identified mechanism; (iv) Synthesis — deterministic generation of a physics-grounded problem statement from confirmed field values; and (v) Handoff — packaging of confirmed field values into a structured physics object that serves as the exclusive query input to the Cross-Domain Knowledge Graph traversal. The adaptive turn sequence in stage (ii) differs by innovation object type: Problem objects follow a contradiction-first elicitation path targeting failure mechanism before operating context; Idea objects follow a mechanism-transfer-first path; Possibility objects follow a TRL -vector-first path. This type-specific adaptive elicitation constitutes a distinct technical step absent from all cited prior art references.

[0072] The field population module (134) is configured to populate mandatory physics fields in the structured record exclusively from values explicitly confirmed by the engineer during the adaptive elicitation dialogue, wherein the Al -inferred values are proposed to the engineer for confirmation but are not entered as confirmed field values without explicit engineer confirmation. The structured record generator (136) is configured to assemble a typed HANDOFF block from the confirmed mandatory physics field values upon satisfaction of the type-specific gate condition, where the HANDOFF block is assembled from confirmed field values only and not from the conversation transcript. The structured record generator (136) generates the following type-specific HANDOFF blocks.

[0073] An IDEA HANDOFF block comprises a mechanism of action field with confidence tag; source analogue identifier and TRL with confidence tag; attribute mapping table with a minimum of three source-to-target parameter pairs each comprising parameter name, source domain value, target domain value, and adaptation delta; primary transfer uncertainty field with specificity classification; target domain context; and a provenance record identifying the elicitation session, turn count, and engineer identifier.

[0074] A POSS HANDOFF block comprises a target domain application at subsystem specificity; source mechanism and origin industry with confidence tag; TRL vector comprising TRL integer, DELTA-TRL-per-year, and projected TRL date; manufacturing or physics barrier with named parameter, current value, required value, and gap; theoretical value limit in absolute units; timing risk with specific named event and date; possibility classification on a two-by-two impact -by-potential-value matrix; and a provenance record.

[0075] An IFR HANDOFF block comprises an IFR formula as a function-only statement free of technology names; fundamental physics barrier statement; ambient resource inventory with a minimum of two entries each comprising resource type, availability conditions, and energy or material quantity; physics ceiling identifying the governing law and theoretical limit; ten -times transformation vector; and a provenance record.

[0076] The computer-implemented physics-grounded cross-domain knowledge graph engine (148) is used for engineering analogy discovery. For example, the cross-domain knowledge graph engine (148) comprises: a minimum of 13 distinct engineering and scientific domains; a minimum of 530 distinct function nodes each assigned to a Universal Function Class; and typed edges comprising at least ANALOGOUS TO, IMPLEMENTS, and ENABLES relationships between function nodes across domains. The Universal Function Classes are exactly eight in number comprising: MOVE, CONVERT, STORE, COMMUNICATE, REGULATE, SEPARATE, ASSEMBLE, and SUPPORT, derived from a synthesis of functional basis taxonomies including the Hirtz functional basis, AskNature biological function taxonomy, and NIST engineering function classification.

[0077] The physics statement compiler (150) is configured to receive a Spark Block HANDOFF record and compile a physics-grounded discovery query including the governing physical phenomenon class identified in the Spark Block; a Physics Bridge equation identified as the domain -neutral governing equation shared by both the problem domain and potential solution domains; the physical contradiction parameters from the Spark Block; and the hard constraints expressed as physics bounds.

[0078] The functional decomposition module (152) is configured to decompose the engineering system identified in the Spark Block into a black-box functional representation, identifying the physical actions performed by each component and mapping each action to a Universal Function Class.

[0079] The Physics Bridge mapping module (154) is configured to identify, from the functional decomposition output, the performance bottleneck component and map it to its Universal Function Class and to the Physics Bridge equation that governs the bottleneck performance.

[0080] The cross-domain graph traversal engine (156) is configured to traverse a cross-domain knowledge graph using the Universal Function Class and Physics Bridge equation as traversal parameters, wherein the traversal is conducted by matching Physics Bridge equation parameters and not by matching terminology or keyword similarity, and wherein source domains within the domain of the querying Spark Block are excluded from the traversal.

[0081] For example, the cross-domain graph traversal engine (156) assigns each candidate cross-domain match an equivalence score on a four-layer scale comprising surface equivalence at score range 0.3 to 0.5, representing terminology or functional label similarity with no shared governing equation, associated with a transfer success rate of 30 to 50 percent; process equivalence at score range 0.5 to 0.7, representing shared operational sequence or workflow similarity with no shared governing equation, associated with a transfer success rate of 50 to 70 percent; structural equivalence at score range 0.7 to 0.85, representing shared architectural pattern or physical configuration with partial governing equation overlap, associated with a transfer success rate of 70 to 85 percent; and causal equivalence at score range 0.85 to 0.95, representing an identical Physics Bridge equation governing both the source domain solution and the target domain problem, associated with a transfer success rate of 85 to 95 percent, only matches at Structural or Causal equivalence are surfaced to the engineer by default, and wherein matches at Surface or Process equivalence are archived and accessible only upon explicit engineer request.

[0082] The cross-domain graph traversal engine (156) enforces a mandatory four-step sequence of decompose, map, discover, and synthesize, and rejects any query that attempts to initiate traversal at the discover step without having completed the decompose and map steps; the physics statement compiler (150) rejects queries that do not contain the physics bridge equation and returns the query to the Spark Block intake with a specific instruction identifying the missing physics grounding; a query provenance record is generated for eachtraversal comprising the Physics Bridge equation used, the Universal Function Class matched, the domains traversed, and the equivalence scores of all matches found; and the system documents a performance differential between key word -input traversal producing 0.3 to 0.5 match scores and physics-grounded input traversal producing 0.85 to 0.95 match scores as a performance guarantee embedded in the system specification.

[0083] The transfer synthesis module (158) is configured to generate, for each candidate cross-domain match, a transfer brief comprising: a transfer logic statement explaining the mechanism of transfer; a constraint boundary identifying the operating conditions within which the transfer is valid; transfer evidence comprising the source domain performance data and TRL; and an implementation gap identifying the specific adaptation required and the associated risk score.

[0084] The computer-implemented active context propagation system (160) is used for an upstream R&D intelligence platform. In the computer-implemented active context propagation system (160), the scored insight selection module (162) is configured to receive a plurality of cross-domain analogy matches from the CDKG engine, each match comprising a transfer brief with Transfer Logic, Constraint Boundary, Transfer Evidence, and Implementation Gap, and to compute, for each match, a granular set of scored insight statements at the insight level rather than at the analogue level. Each scored insight statement comprises a numerical score on a defined scale, a scoring rationale identifying what the insight captures correctly and what it lacks, and a specificity classification selected from specific mechanism transfer or generic conceptual parallel.

[0085] The scored insight selection module (162) further generates, for each scored insight statement, an insight meta-evaluation comprising: a scoring rationale statement that identifies in structured form the specific aspect of the mechanism that the insight captures correctly, the specific aspect that the insight does not capture or generalizes beyond what the evidence supports, and the evidentiary basis for the score; a specificity classification that categorizes the insight as either a specific mechanism transfer, wherein the source domain physics mechanism maps directly to the target domain physics bottleneck via the Physics Bridge equation, or a generic conceptual parallel, wherein the analogy is structural or process-level without a shared governing equation; and a visual distinction flag that the insight selection interface renders differently for specific mechanism transfers versus generic conceptual parallels, enabling an engineer to priorities mechanism -level insights before conceptual-level insights during manual selection.

[0086] The insight selection interface (164) is configured to present the scored insight statements to a human engineer for selection via checkbox, and further configured to operate in an autonomous selection mode above a configurable confidence threshold wherein the system selects insights without human confirmation.

[0087] The active context record constructor (166) is configured to assemble, from the selected insight statements, an active context record including an Architecture Alignment field identifying how the source domain solution architecture maps to the target domain system; a Projected Impact field computed from the Physics Bridge equation applied to the source domain performance data and the target domain gap metrics, expressed in domain -specific quantified units; Conceptual Design Specifications derived from the Transfer Logic of selected insights; Operating Constraints extracted from the Constraint Boundary field of each selected insight and expressed as quantified engineering parameters; a Potential Failure Mode statement identifying the specific failure risk arising from mechanism transfer; and CPC and IPC patent classification codes auto-generated from the domain and mechanism of each matched analogue.

[0088] The downstream modification engine (168) is configured to inject the active context record into subsequent Think Model engine invocations as a mandatory input, such that the active context record deterministically modifies the analysis scope and output of each downstream engine.

[0089] The downstream modification engine (168) applies the active context record to the following Think Model engines (170) with the following deterministic modifications.

[0090] The context enrichment engine (172) receives the Operating Constraints and Potential Failure Mode from the active context record and incorporates them as additional constraints and risk inputs to the analysis, expanding the constraint envelope beyond the constraints confirmed during Spark Block intake.

[0091] The functional analysis engine (174) receives cross-domain resolution pathways derived from the selected insight statements and generates function model outputs that include cross-domain resolution options tagged with the insight identifier and the Physics Bridge equation supporting each resolution.

[0092] The contradiction analysis engine receives a cross-domain contradiction resolution history comprising prior instances in the CDKG where the same or similar contradiction was resolved in another domain, and surfaces these instances as prioritized TRIZ principle candidates ranked by causal equivalence score.

[0093] The first principles engine receives the Physics Bridge equations associated with selected insights as additional governing relationships supplementing the governing equations retrieved during domain physics pre-loading, expanding the first principles derivation to include cross-domain physics relationships not natively present in the target domain.

[0094] All outputs from all four Think Model engines carry a traceability tag referencing the active context record identifier, enabling full provenance reconstruction from any Building Block field back through the active context record to the originating CDKG match and Spark Block physics field.

[0095] The computer-implemented physics-validated progression gate (180) is used for an upstream R&D intelligence system. In the gate (180), the physics-grounded synthesis qualification engine (182) is configured to receive as input (i) a confirmed Spark Block handoff record comprising a plurality of mandatory physics fields populated and confirmed during structured intake, wherein each physics field includes a confidence status selected from Al-derived, database-verified, or human -confirmed; and (ii) a Think Model evidence package comprising structured outputs generated by a plurality of Think Models executed on said confirmed Spark Block handoff record.

[0096] The field -level delta computation module (184) is configured to compare, on a field -by-field basis, the confirmed mandatory physics fields from the Spark Block handoff record against corresponding physics parameters derived from the Think Model evidence package, wherein the comparison identifies: consistency between the originally confirmed field values and the Think Model-derived parameters; modifications required to one or more physics fields based on Think Model evidence; or fundamental contradictions in the physics fields that cannot be resolved by field modification.

[0097] The physics surrogate validation module (186) is configured to execute, upon initiation of the progression gate, a PhysicsNeMo surrogate model at a configurable intensity level of at least 0.85, wherein the surrogate model receives as input the confirmed physics constraint vector from the Spark Block handoff record and generates a physics feasibility confidence score and a predicted performance envelope for the physics fields under evaluation.

[0098] The physics surrogate validation module (186) invokes a domain-specific PhysicsNeMo surrogate model selected based on the Spark Block type and domain, wherein

[0099] for a Problem Spark Block, the surrogate model receives the failure mechanism, operating conditions, and quantified baseline gap as physics constraint inputsand validates whether the stated performance target is achievable within the governing physics equations and theoretical limits of the target domain;

[0100] for an Idea Spark Block, the surrogate model receives the mechanism of action, attribute mapping parameter pairs, and primary transfer uncertainty as physics constraint inputs and validates whether the source-domain physics mechanism remains operative under the target-domain adaptation conditions specified in the attribute mapping;

[0101] for a Possibility Spark Block, the surrogate model receives the source mechanism, manufacturing or physics barrier, and theoretical value limit as physics constraint inputs and validates whether the TRL trajectory is consistent with the barrier's physical resolution timeline;

[0102] for an IFR / Moonshot Spark Block, the surrogate model receives the zeroharm condition, ambient resource, and physics ceiling as physics constraint inputs and validates whether the IFR formula is bounded above by the stated governing law and whether the ambient resource is thermodynamically sufficient to supply the required function; and

[0103] The surrogate validation generates, for each Spark Block type: a physics feasibility confidence score bounded between 0.0 and 1.0; a predicted performance envelope with uncertainty bounds; and a named failure mode for cases where the confidence score falls below the configurable transformation threshold.

[0104] The synthesis qualification determination engine (188) is configured to compute, from the field -level delta computation and the physics surrogate validation output, a qualification verdict selected from: CONFIRMED, wherein the Think Model evidence and surrogate validation output are consistent with the confirmed mandatory physics fields and no field modification is required; REFINED, wherein the Think Model evidence or surrogate validation output requires modification of one or more physics fields, with a provenance record of original field values, modified field values, and the specific evidence driving each modification; and INVALIDATED, wherein the Think Model evidence or surrogate validation output contradicts the mandatory physics fields in a manner that cannot be resolved by field modification, with a re-framing directive identifying the specific physics rationale for invalidation and the corrected problem framing.

[0105] The configurable autonomy controller (190) is configured to operate in human-confirmation mode, wherein the system presents the qualification verdict and supporting evidence to a human engineer for confirmation prior to executing transformation, or in autonomous mode above a configurable confidence threshold, wherein the system executes the qualification verdict without requiring human confirmation.

[0106] The transformation executor (192) is configured to: upon CONFIRMED verdict, transform the Spark Block into its primary Building Block target with the original physics fields intact and a transformation trace record; upon REFINED verdict, transform the Spark Block into its primary Building Block target with modified physics fields and a field modification provenance log preserving original values; and upon INVALIDATED verdict, return the Spark Block to the universal chat intake engine with the re-framing directive and a pre-populated corrected Spark Block stub containing physics fields adjusted to reflect the specific invalidation rationale.

[0107] The transformation executor (192) records, in a Transformation Trace Matrix, for each transformation event: (i) source Spark Block identifier, type, and originating phase; (ii) target Building Block identifier and type; (iii) qualification verdict and confidence score; (iv) PhysicsNeMo surrogate model identifier, version, and domain; (v) physics feasibility confidence score and predicted performance envelope from surrogate validation; (vi) for CONFIRMED verdicts: a field consistency confirmation record per mandatory physics field; (vii) for REFINED verdicts: a field modification provenance log comprising, per modified field, the original confirmed value, the modified value, the specific Think Model output or surrogate validation result driving the modification, and the modification rationale; (viii) for INVALIDATED verdicts: the re-framing directive comprising the specific physics parameters that contradict the original fields, the governing physics rationale for invalidation, and the corrected problem framing used to populate the returned Spark Block stub; (ix) human decision-maker identifier when operating in human-confirmation mode; and (x) timestamp and version history; wherein the Transformation Trace Matrix is queryable by downstream Building Block generation modules and patent disclosure generation modules to establish full traceability between each validated Building Block field and its originating Spark Block physics evidence.

[0108] The re-framing directive generated upon an INVALIDATED verdict comprises: a physics contradiction statement identifying the specific parameter or governing equation that conflicts with the original Spark Block mandatory physics fields; a domain boundary statement identifying whether the invalidation results from an absolute physics violation within the stated domain, an architecture-specific constraint embedded as a domainindependent physics constraint, or a modality mismatch wherein the stated performance target is achievable but not within the physics layer implied by the original problem framing; a corrected problem framing statement specifying the modified physics scope, operating regime, or modality boundary within which the performance target is achievable; and a pre-populated corrected Spark Block stub comprising the corrected mandatory physics fields derived from the re-framing directive, with confidence status set to Al-derived for all corrected fields pending engineer confirmation, and a traceability link to the invalidated Spark Block record and the specific surrogate validation result driving the invalidation; wherein the pre-populated Spark Block stub is passed to the universal chat intake engine as a structured seed, and the intake engine initiates a targeted confirmation dialogue beginning from the first modified field rather than from the initial open-ended intake turn.

[0109] The computer-implemented physics-constrained design space exploration system (194) is used for generating and evaluating a plurality of concept variants from the validated Concept Building Block. In the system (194), the variable extraction module (196) is configured to receive a Concept Building Block comprising a plurality of mandatory physics fields, each physics field having a confirmed value, physical units, and a governing physical equation linkage, and to automatically extract therefrom a set of active design variables comprising for each variable: a variable identifier derived from the physics field name; a physical unit specification; a lower bound and an upper bound derived from the theoretical limits and Physics Ceiling stored in the Physics Formula Library for the relevant domain; and a governing equation reference establishing the physical constraint on the variable's range.

[0110] the variable extraction module (196) derives the lower bound and upper bound for each active design variable without requiring manual specification by an engineer, by: retrieving from the Physics Formula Library the theoretical minimum and theoretical maximum values for the physical quantity corresponding to the physics field, as bounded by the governing physical equation and domain applicability conditions; applying a configurable safety margin below the Physics Ceiling to set the upper bound, such that no generated variant exceeds the theoretical limit by construction; and recording the auto-derived bounds in a Sampling Space Provenance Record that establishes traceability between each variable bound and the specific Physics Formula Library entry from which it was derived; wherein the Sampling Space Provenance Record is included in the Downstream Simulation Handoff Record to enable downstream simulation engineers to verify the physical basis of each variable range without reference to the originating KRAFT session.

[0111] The space-filling sampling module (198) is configured to receive the set of active design variables and their physics-derived bounds, and to execute a Design of Experiments sampling algorithm — including Latin Hypercube Sampling — across the Tridimensional space defined by the active variables to autonomously generate a variant matrixcomprising a configurable number of synthetic concept variants, wherein each synthetic concept variant is assigned: a unique variant identifier; a generation type classification of Archetype Seed for the original physics-consistent Concept Building Block instantiation, or Synthetic Expansion for each sampled variant; and a parameter vector specifying values for each active design variable within the physics-derived bounds.

[0112] the variant matrix comprises exactly one Archetype Seed variant that is a direct instantiation of the Concept Building Block mandatory physics fields at their confirmed values, and a plurality of Synthetic Expansion variants generated by applying the Design of Experiments sampling algorithm to perturb each active design variable independently within its physics-derived bounds; and the Physics Ceiling pre-filter module records, upon completion of pre-filtering, a Physics Ceiling Filter Report comprising: the total number of variants generated; the number of variants rejected at the Physics Ceiling prefilter stage; the rejection rate as a percentage of total variants; for each rejected variant, the specific Physics Ceiling parameter exceeded and the governing equation that establishes the ceiling; and the number of variants forwarded to the multi -parameter surrogate evaluation module; wherein the Physics Ceiling Filter Report is appended to the Downstream Simulation Handoff Record as evidence of computational resource conservation achieved prior to surrogate invocation.

[0113] The Physics Ceiling pre-filter module (200) is configured to evaluate each Synthetic Expansion variant against the Physics Ceiling derived from the Physics Formula Library for the relevant domain, and to eliminate without surrogate invocation any variant whose parameter vector violates the Physics Ceiling, assigning such variants a Physics Ceiling Rejected classification and recording the specific Physics Ceiling parameter and governing equation that caused rejection;

[0114] The multi-parameter surrogate evaluation module (202) is configured to receive all variants passing the Physics Ceiling pre-filter and to execute, in batch, a physics-informed neural surrogate model against each surviving variant, generating for each variant a multi-parameter output vector comprising at minimum: a predicted performance score for each target output parameter; a physics feasibility confidence score bounded between 0.0 and 1.0; and a composite optimality score computed from the weighted combination of the predicted performance scores and the physics feasibility confidence score.

[0115] The multi-parameter surrogate evaluation module (202) generates, for each surviving variant, a multi-parameter output vector comprising performance predictions across a plurality of target parameters specified in the Concept Building Block, wherein each targetparameter prediction includes: a point estimate in the physical units of the target parameter; an uncertainty bound representing the surrogate model's prediction confidence interval; a pass / fail determination against the performance target specified in the Concept Building Block; and a named physics failure mode for variants where the point estimate falls below the performance target; and wherein the ranked downstream handoff module generates the Downstream Simulation Handoff Record in a structured format compatible with a target downstream simulation environment selected from the group comprising: Computational Fluid Dynamics solvers; Finite Element Analysis solvers; Multiphysics simulation environments; and Physics-Informed Neural Operator frameworks; and wherein the Downstream Simulation Handoff Record specifies, for each top-N variant, the complete parameter vector in the input format required by the specified downstream simulation environment, together with the surrogate-predicted performance envelope as a reference baseline for downstream simulation result validation.

[0116] The ranked downstream handoff module (204) is configured to rank all surviving variants by composite optimality score, select a configurable top-N subset of highest -ranked variants, and generate a Downstream Simulation Handoff Record comprising: the parameter vector for each top-N variant; the surrogate-predicted multi-parameter output vector; the physics feasibility confidence score and composite optimality score; provenance links to the originating Concept Building Block and the specific Physics Formula Library constraints applied; and a downstream routing directive specifying the target high-fidelity simulation environment, wherein said Downstream Simulation Handoff Record is forwarded to a downstream full physics simulation layer thereby preventing computational resource exhaustion in the downstream simulation environment by restricting high-fidelity simulation to only the highest -ranked physics-feasible variants.

[0117] The computer-implemented system is used for motivation -driven project initialization in an upstream R&D intelligence platform. The motivation classification module (206) is configured to receive a project initiation input and classify the initiation input into a motivation type selected from the group consisting of: a Moonshot Motivation, representing a breakthrough vision, Ideal Final Result, or lOx performance target; a Market Problem Motivation, representing an identified customer pain point, competitive gap, or engineering failure condition in a target market; and a New Possibilities Motivation, representing an emerging technology trend, newly available capability, or cross -domain breakthrough with application potential in the project domain.

[0118] The primary entry point routing module (208) is configured to, upon motivation classification, automatically instantiate as the primary Spark Block for the project a Spark Block type matched to the classified motivation type according to the following routing table: a Moonshot Motivation routes to an IFR / Moonshot Spark Block as primary entry point; a Market Problem Motivation routes to a Problem Spark Block as primary entry point; and a New Possibilities Motivation routes to a Possibility Spark Block as primary entry point; wherein no project can be created without a classified motivation type, and wherein the primary Spark Block type is system-enforced based on the motivation classification and cannot be overridden to a different primary entry type for the classified motivation.

[0119] The project context pre-configuration module (210) is configured to, upon primary entry point routing, pre-load into the project context: the domain intelligence module corresponding to the motivation type's target physics domain; the Physics Formula Library entries applicable to the motivation type's domain; and a motivation -type-specific project configuration comprising the set of Spark Block types that are permissible as secondary entries for the project, the Building Block transformation pathway sequence associated with the primary entry type, and the Proof Point criteria applicable to the classified motivation type.

[0120] The motivation-to-workflow-path binding module (212) is configured to record, in the project metadata, the classified motivation type, the routing decision made, the pre-loaded domain intelligence module identifiers, and the expected downstream Building Block progression — Needs for Market Problem Motivation projects, Concepts for Moonshot Motivation projects, and Opportunities for New Possibilities Motivation projects — thereby establishing a complete motivation-to-outcome traceability chain from project initialization through to validated Building Block creation.

[0121] The following worked example demonstrates PFL operation in a thermal management context, specifically addressing a battery thermal runaway scenario

[0122] To illustrate the technical character and computational execution of the system, an exemplary embodiment is described herein detailing the interaction between the Physics Formula Library (PFL), the Type-Specific Adaptive Elicitation Engine, and the Physics-Validated Progression Gate during a battery thermal management R&D task. This embodiment demonstrates how the system solves the technical problem of computational resource exhaustion by computationally terminating physically impossible simulation pathways before high-fidelity (Layer 4) simulation resources are expended.

[0123] Active Pre-Loading and Dimensional Consistency Check: In this embodiment, an engineer initializes a project within the 'Thermal / Electrochemical' domain. The initialization sequence engine proactively queries the PFL to retrieve the applicablegoverning equations, including Fourier's Law of heat conduction, stored in machine -executable format as q" = -k * nabla(T), where q" represents heat flux density in W / mA2, k represents the material's thermal conductivity in W / m*K, and nabla(T) represents the temperature gradient in K / m.

[0124] During the generation of a problem spark block, the engineer inputs a target heat removal rate and specifies a thermal conductivity value for a novel composite material using units of W / cm*K. Prior to routing this Spark Block to downstream analysis, the PFL executes a mandatory Dimensional Consistency Check. The unit -compatibility matrix identifies the dimensional mismatch between the input (W / cm*K) and the PFL governing equation requirement (W / m*K). The system computationally flags the unit mismatch, returns the physics field to Al-derived status, and autonomously prompts the engineer with the required SI base unit conversion before permitting re-submission, thereby ensuring downstream mathematical operability.

[0125] Physics Ceiling Derivation and Surrogate Validation: Once dimensionally consistent, the PFL injects the Domain Applicability Conditions into the analysis context. For the selected battery cell architecture, the PFL specifies that Fourier's Law is valid only for solid-phase conduction strictly below the phase-change threshold temperature (T_pc) of the electrolyte. Above T_pc, electrolyte decomposition occurs. The system then computationally derives the absolute theoretical Physics Ceiling: the maximum permissible heat flux through the battery cell wall of thickness delta is bounded by q"_max = k * (T_pc - T operating) / delta.

[0126] Execution of the Progression Gate Verdict — INVALIDATED: The Spark Block's confirmed physics constraint vector is routed to the Synthesis Qualification Engine. The field-level delta computation module compares the engineer's target heat removal rate against the computationally derived q"_max. If the engineer's target requires a heat flux q" that strictly exceeds q"_max, the Physics-Validated Progression Gate deterministically executes an INVALIDATED verdict, preventing the Spark Block from advancing to Layer 4 full physics simulation and thereby conserving substantial computational resources.

[0127] Re-Framing Directive and Stub Generation: Upon execution of the INVALIDATED verdict, the Transformation Executor automatically generates a Re-framing Directive comprising: (i) a machine-generated physics contradiction statement identifying the specific parameter conflict (e.g., 'Target q" of 450 W / mA2 exceeds theoretical maximum q"_max of 380 W / mA2 for the specified material thickness and T_pc'); and (ii) a pre-populated corrected Spark Block stub with the target heat removal rate modified to q"_maxminus a defined safety margin epsilon, returned to the Universal Chat intake engine as a structured seed. The intake engine initiates a targeted confirmation dialogue beginning from the first modified field rather than requiring the engineer to restart the entire input process.

[0128] Technical Improvement to Computer Functionality: By performing these computational steps, the system provides a specific, measurable technical improvement to computer functionality: it deterministically prevents the execution of computationally expensive, high-fidelity simulations on boundary conditions that violate fundamental thermodynamic laws, while concurrently auto-generating the precise mathematical corrections required to render the engineering hypothesis physically viable. These computational steps — dimensional consistency checking, Physics Ceiling derivation, Progression Gate verdict execution, and corrected stub generation — require machine-based computation and cannot practically be performed in the human mind, and therefore do not constitute a mental process. Furthermore, the computational steps of dimensional consistency checking across disparate domains, continuous Physics Ceiling derivation, and dynamic Progression Gate verdict execution require intensive machine-based computation of multivariable physical constraints and cannot practically be performed in the human mind.

[0129] In another example, to demonstrate the cross-domain operation of the physics-constrained design space exploration system across engineering disciplines, an exemplary embodiment is described for an aerospace turbine blade cooling application. A validated Concept Building Block specifying a regenerative micro-channel cooling architecture for a gas turbine blade is received as the system input. The Concept BB's mandatory physics fields include: heat flux density target (q" = 2.5 MW7mA2), coolant inlet temperature (T in = 650 K), blade wall thermal conductivity (k = 18 W / m*K), channel hydraulic diameter (D_h), coolant mass flow rate, and Nusselt number correlation parameters.

[0130] Auto-Derived Sampling Space: The variable extraction module automatically identifies the active design variables from the Concept BB physics fields and retrieves their Physics Formula Library bounds. For the hydraulic diameter D_h, the PFL retrieves the laminar-turbulent transition condition (Re = D_h * v / nu > 4000) and sets the lower bound at D h min corresponding to Re = 4,000 at the specified coolant velocity, and the upper bound at D h max consistent with the blade wall thickness constraint derived from the structural mechanics PFL entry. For the Nusselt number correlation, the PFL specifies the Dittus-Boelter equation (Nu = 0.023 * ReA0.8 * PrA0.4) as the governing equation and enforces that all sampled channel geometries must produce Nu values consistent with fully developedturbulent flow. No engineer manually enters these bounds — they are derived entirely from the Concept BB physics fields and PFL.

[0131] LHS Variant Generation and Physics Ceiling Pre-Filter: The space-filling sampling module executes Latin Hypercube Sampling across the 6 -dimensional active variable space to generate 2,500 synthetic concept variants. One Archetype Seed variant is generated from the original Concept BB confirmed field values. The Physics Ceiling prefilter evaluates each of the 2,499 Synthetic Expansion variants against the thermal physics ceiling: q"_max = k * Nu * (T blade max - T in) / D_h, where T blade max is the nickel superalloy melting onset temperature (1,150 degrees C) retrieved from the PFL materials database. Variants whose predicted heat flux exceeds q"_max are rejected without surrogate invocation. In a representative execution, approximately 2,100 of 2,499 Synthetic Expansion variants pass the Physics Ceiling pre-filter, with 399 variants rejected as thermodynamically non-viable.

[0132] Surrogate Scoring and Downstream Handoff: The 2,101 surviving variants are routed to the multi-parameter surrogate evaluation module, which executes a physics-informed neural surrogate model trained on existing turbine cooling CFD data. The surrogate generates for each variant: predicted blade wall peak temperature (degrees C); predicted pressure drop across the cooling channel (Pa); predicted coolant outlet temperature (K); and a physics feasibility confidence score. The ranked downstream handoff module selects the top 50 variants by composite optimality score and generates a Downstream Simulation Handoff Record specifying all 50 variant parameter vectors in ANSYS CFD input format. The top 50 variants proceed to full ANSYS CFD simulation, with the surrogate-predicted performance envelope provided as a reference baseline. The complete 2,500-variant evaluation cycle completes in under 3 minutes of wall-clock time, compared to an estimated 500+ hours required for full CFD evaluation of all 2,500 variants.

[0133] In another example, an exemplary embodiment is provided demonstrating the computational traversal of the graph using Physics Bridge equations rather than conventional semantic or keyword -based vector similarity.

[0134] In conventional knowledge graph discovery systems (prior art), traversing a database for a 'heat dissipation' problem in consumer electronics relies on Natural Language Processing (NLP) word embeddings. Such semantic traversal typically retrieves solutions from highly adjacent domains (e.g., data center cooling) or retrieves superficial matches based on keyword proximity (e.g., 'heat sinks'). Testing of semantic-only traversal systems yields a causal equivalence match rate of only 0.3 to 0.5, as the retrieved solutions frequentlyshare terminology but do not share the governing physical mechanics required for successful cross-domain transfer.

[0135] The present invention computationally overcomes this technical limitation through the Physics Bridge mapping module. When an engineer inputs a Problem Spark Block regarding thermal throttling in a mobile device processor, the system does not search for 'cooling' or 'heat.' Instead, the functional decomposition module categorizes the problem under the Universal Function Class 'MOVE (Thermal Energy)', and the physics statement compiler extracts the governing Physics Bridge equation - the dimensionless Nusselt number (Nu) for convective heat transfer, represented computationally as:

[0136] Nu = (h * L) / kwhere h is the convective heat transfer coefficient, L is the characteristic length, and k is the thermal conductivity of the fluid.

[0137] The cross-domain graph traversal engine executes a search where the traversal parameter is the mathematical architecture of the Nusselt number and its relationship to the Reynolds and Prandtl numbers, deliberately excluding the source domain (consumer electronics) from the search radius. The traversal matches on Physics Bridge equation parameters, not on language, keyword proximity, or word -vector cosine similarity.

[0138] By traversing the CDKG using the Physics Bridge equation rather than semantic embeddings, the system retrieves structurally and causally equivalent solutions from non-obvious domains. For instance, the system may match the convective constraint to boundary-layer fluid dynamics solutions used in aerospace turbine blade cooling, or to micro-vascular cooling channels found in biological systems (biomimicry). Neither of these matches would be retrieved by a semantic system searching for 'heat' or 'cooling' — they are retrieved because they share the identical Nusselt / Reynolds governing relationship.

[0139] Because the match is predicated on the identical underlying physics equation rather than language, the transfer synthesis module achieves a causal equivalence score ranging from 0.85 to 0.95 — a measurable and reproducible improvement over the 0.3-0.5 causal match rate of prior-art semantic traversal systems. The system subsequently autogenerates a Transfer Logic statement proving that the aerospace micro-channel geometry will scale to the mobile processor dimension precisely because both systems are governed by the same Nusselt / Reynolds relationship, thereby providing a deterministic, physics-validated innovation pathway rather than a probabilistic semantic suggestion.

[0140] In another example, to validate the computational efficiency and technical effect of the four-dimensional adaptive routing table and the Physics-Validated ProgressionGate, the inventors conducted internal benchmarking within a simulated testing environment. The benchmarking exercise targeted a generic high-ambient thermal management challenge for a 48V electrochemical energy storage module - a representative class of engineering problem for which cross-domain discovery and physics-bounded validation are routinely required .

[0141] In conventional brute-force high-fidelity simulation workflows (prior art), validating a comparable volume of cross-domain design variants — specifically 1,100 distinct architectural combinations — through full Layer 4 Computational Fluid Dynamics (CFD) and finite element analysis would require an estimated 500+ hours of compute time at an infrastructure cost exceeding $50,000.

[0142] Applying the present invention, the system's active pre-loading of the Physics Formula Library (PFL) and the execution of Layer 1 (Constraint Feasibility) and Layer 2 (Symbolic Derivation) checks deterministically invalidated over 90% of the generated variants as physically non-viable before they could consume high-fidelity simulation resources. Only the variants satisfying the computationally derived Physics Ceiling were routed to Layer 3 (PhysicsNeMo Surrogate) and Layer 4 simulation.

[0143] This stage-proportional validation depth yielded unexpected results in computational resource conservation. The system successfully identified a cross-domain architectural solution that reduced the peak operational temperature by 42.6 degrees Celsius (a 43% improvement over the baseline state), while consuming only $174 in total compute costs and completing the 1,100-variant validation cycle in under 24 hours.

[0144] This represents a greater than 250-fold reduction in computational resource consumption compared to conventional full -simulation methods, demonstrating a profound and measurable technical improvement to the functioning of the computer system itself when executing complex engineering validation tasks. These results were unexpected by one of ordinary skill in the art, as prior-art systems applying cross-domain analogy discovery without physics-constraint gating do not produce deterministic resource conservation of this magnitude — validating the non-obviousness of the four-dimensional adaptive routing table and the Physics-Validated Progression Gate as a combined system.

[0145] In an example: Electromagnetic Domain

[0146] Fails the Gate (Observable Effect): "The Wi-Fi antenna loses signal when placed inside the metal enclosure."

[0147] Passes the Gate (Sub-Outcome Specificity): "Electromagnetic shielding induces Faraday cage attenuation, where the skin depth (delta) of the 2mm aluminumenclosure at 2.4 GHz results in a reflection and absorption loss exceeding the 80 dB receiver sensitivity threshold."

[0148] System Action: The system extracts the skin depth equation (delta = sqrt(2*rho / (omega * mu))) and the 80 dB attenuation threshold as the computable constraint vector for downstream CDKG traversal.

[0149] In an example - Electrochemical Domain

[0150] Fails the Gate (Observable Effect): "The lithium-ion battery catches fire when charged too quickly in cold weather."

[0151] Passes the Gate (Sub -Outcome Specificity): "Lithium plating is induced at the anode because the anodic overpotential drops below 0V vs. Li / Li+ under a 6C charge rate at 0 degrees Celsius, bypassing intercalation dynamics as described by the Nemst equation for the electrode potential."

[0152] System Action: The system extracts the Nemst equation overpotential limit (<0V vs. Li / Li+) and the 6C charge rate at 0 degrees Celsius as the computable constraint vector. These two parameters are sufficient to locate cross -domain analogues where overpotential limits govern system safety boundaries.

[0153] In an example - Fluid Dynamics Domain:

[0154] Fails the Gate (Observable Effect): "The pipeline vibrates violently when fluid is pumped through it at high speeds."

[0155] Passes the Gate (Sub-Outcome Specificity): "Vortex shedding frequency (f_v) matches the natural structural frequency of the pipe, inducing resonance because the flow velocity yields a Strouhal number (St) of approximately 0.2, computed as St = (f_v * L) / v."

[0156] System Action: The system extracts the Strouhal number equation (St = f_v * L / v) and the resonance frequency match condition as the computable constraint vector. The CDKG traversal uses the Strouhal number relationship to locate cross-domain solutions where vortex-induced resonance has been resolved — including solutions from civil engineering bridge design, subsea pipeline engineering, and aerospace aeroelastic damping.

[0157] In an example - Structural Mechanics Domain:

[0158] Fails the Gate (Observable Effect): "The bridge support beam breaks under the weight of heavy trucks."

[0159] Passes the Gate (Sub -Outcome Specificity): "Maximum principal stress exceeds the material's yield strength of 250 MPa under cyclic loading, initiating fractureaccording to the von Mises yield criterion: sigma_vm = sqrt(0.5 * ((sl-s2)A2 + (s2-s3)A2 + (s3-sl)A2))."

[0160] System Action: The system extracts the von Mises stress equation and the 250 MPa yield threshold as the computable constraint vector. The CDKG traversal uses the yield criterion formulation to locate cross-domain solutions where cyclic-load -induced fracture has been resolved — including aerospace fatigue life management, medical implant materials science, and offshore structure fatigue protocols.

[0161] The processor (102) may include one or a plurality of processors. The one or the plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an Al -dedicated processor such as a neural processing unit (NPU). The processor (102) may include multiple cores and is configured to execute the instructions stored in the memory (106).

[0162] Further, the processor (102) is configured to execute instructions stored in the memory (106) and to perform various processes. The communicator (104) is configured for communicating internally between internal hardware components and with external devices via one or more networks. The memory (106) also stores instructions to be executed by the processor (102). The memory (106) may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory (106) may, in some examples, be considered a non -transitory storage medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted that the memory (106) is non-movable. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).

[0163] At least one of the plurality of modules may be implemented through an Al model. A function associated with Al may be performed through the non-volatile memory, the volatile memory, and the processor (102). The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (Al) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

[0164] Here, being provided through learning means that, by applying a learning algorithm to a plurality of learning data, a predefined operating rule or Al model of a desired characteristic is made. The learning may be performed in a device itself in which Al according to an embodiment is performed, and / o may be implemented through a separate server / system.

[0165] The Al model may consist of a plurality of neural network layers. Each layer has a plurality of weight values, and performs a layer operation through calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), and deep Q -networks.

[0166] The learning algorithm is a method fortraining a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi -supervised learning, or reinforcement learning.

[0167] Although FIG. 1 shows various hardware components of the computer-implemented system (100) but it is to be understood that other embodiments are not limited thereon. In other embodiments, the computer-implemented system (100) may include less or more number of components. Further, the labels or names of the components are used only for illustrative purposes and does not limit the scope of the invention. One or more components can be combined together to perform the same or substantially similar function in the computer-implemented system (100).

[0168] FIG. 2 is an example flow chart (S200) illustrating a computer-implemented method for creating and managing physics-constrained innovation objects, according to the embodiments as disclosed herein.

[0169] At S202, the method includes receiving, by the spark block creation engine (110) of the computer-implemented system (100), raw engineering observation and classify the raw engineering observation into one of the plurality of innovation stage types selected from the group consisting of: problem, idea, possibility, IFR / moonshot, and initiative. At S204, the method includes requiring, by the type-specific physics field enforcement module (112), population of the mandatory set of physics fields specific to the classified innovation stage type upon classification. Each physics field requires identification of a governingphysical phenomenon class and at least one associated measurable parameter computable from or bounded by a governing physical equation, wherein the spark block creation engine (110) cannot be created without satisfying the mandatory physics field set for its classified type.

[0170] At S206, the method includes assigning, by a confidence status assignment module (114), to each populated physics field, a confidence status selected from the group consisting of: Al-derived, database-verified, and human -confirmed. At S208, the method includes preventing, by a configurable transformation gate module (116), transformation of a spark block into a building block unless a user-configurable minimum proportion of mandatory physics fields carry a confidence status of human-confirmed or database-verified.

[0171] FIG. 8 is an example screen shots illustrating building blocks- need- analysis (i.e., spark block problem converted to building block), according to the embodiments as disclosed herein. FIG. 9 is an example screen shots illustrating need -synthesis, according to the embodiments as disclosed herein. FIG. 10 is an example screen shots illustrating concept -analysis, according to the embodiments as disclosed herein. FIG. 11 is an example screen shots illustrating concept-classification, according to the embodiments as disclosed herein. FIG. 12 and FIG. 13 are example screen shots illustrating concept- summary, according to the embodiments as disclosed herein. FIG. 14 is an example screen shots illustrating concept -variants, according to the embodiments as disclosed herein.

[0172] The various actions, acts, blocks, steps, or the like in the flow charts (S200) may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

[0173] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptationsand modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of at least one embodiment, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

Claims

CLAIMSWe claim:

1. A computer-implemented system (100) for creating and managing physics-constrained innovation objects, comprising:a spark block creation engine (110) configured to receive a raw engineering observation and classify the raw engineering observation into one of a plurality of innovation stage types selected from a group consisting of: problem, idea, possibility, IFR / moonshot, and initiative;a type-specific physics field enforcement module (112) configured to, upon classification, require population of a mandatory set of physics fields specific to the classified innovation stage type, wherein each physics field requires identification of a governing physical phenomenon class and at least one associated measurable parameter computable from or bounded by a governing physical equation, wherein the spark block creation engine (110) cannot be created without satisfying the mandatory physics field set for its classified type;a confidence status assignment module (114) configured to assign, to each populated physics field, a confidence status selected from the group consisting of: artificial intelligence (Al)-derived, database-verified, and human-confirmed; and a configurable transformation gate module (116) configured to prevent transformation of a spark block into a building block unless a user-configurable minimum proportion of mandatory physics fields carry a confidence status of humanconfirmed or database-verified.

2. The computer-implemented system as claimed in claim 1, wherein the mandatory physics fields specific to each innovation stage type comprise:for a Problem Spark Block (SB-1): a system contradiction field identifying two physical parameters in direct opposition with unit -bearing directional vectors, a failure threshold field specifying the quantified operating condition at which the system fails, and a root cause mechanism field identifying the chemical, physical, or mechanism inducing failure;for an Idea Spark Block (SB -2): a mechanism of action field expressed as a verb- noun physics statement identifying the physical action and the entity acted upon; a source domain validity field specifying a named system, its technology readiness level, and the operating conditions under which the mechanism is proven; and an adaptationstressor field identifying the domain-specific physical parameters that change between a source domain and a target domain;for a Possibility Spark Block (SB -3): a Technology Readiness Vector field comprising a current TRL integer, a DELTA -TRL-per-y ear rate, and a projected TRL date; a manufacturing or physics barrier field specifying a named parameter, its current value, its required value, and the quantified gap; and a timing risk field identifying a specific named event and date that creates urgency or architectural lock-in; and for an IFR / Moonshot Spark Block (SB -4): a zero-harm condition field expressed as a function-only statement containing no technology names; an ambient resource inventory field specifying energy or material resources available in the operating environment without additional input; and a physics ceiling field identifying the governing law or theoretical limit that bounds the IFR formula above; andfor an Initiative Spark Block (SB-5): a strategic objective field specifying the measurable outcome target with at least one quantified performance metric and its current baseline value; a physics constraint envelope field identifying the governing physical laws and theoretical limits that bound the initiative scope; and a portfolio linkage field identifying the Problems, Possibilities, or IFR / Moonshot Spark Blocks that the initiative is organized to address, with at least one named cross-domain knowledge domain required for resolution.

3. The computer-implemented system (100) as claimed in claim 1,the confidence status assignment module (114) assigns Al-d erived status to physics field values inferred by the system from the raw engineering observation without external database verification or engineer confirmation;the confidence status assignment module (114) assigns database-verified status to physics field values confirmed by execution of a dimensional consistency check against a Physics Formula Library (PFL), wherein the PFL is a version -controlled computational repository configured to store and execute formal physical relationships, the dimensional consistency check comprising: (i) identifying the Physics Domain Tag of the physics field under verification; (ii) retrieving from the PFL the governing equation applicable to that domain tag; and (iii) executing a unit -compatibility matrix check confirming that the units of the physics field value are dimensionally consistent with the retrieved governing equation; wherein the physics field value that fails the unit -compatibility matrix check is returned to Al -derived status and flagged for engineer confirmation;the confidence status assignment module (114) assigns database-verified status to physics field values confirmed by query against at least one of: a materials property database; or a patent and technical literature database, in addition to or in lieu of the PFL dimensional consistency check;the confidence status assignment module (114) assigns human-confirmed status exclusively to physics field values that have been explicitly confirmed by a human engineer during the structured intake dialogue; andthe configurable transformation gate threshold is settable per deployment context between a minimum of predefined threshold and a maximum of predefined threshold of mandatory physics fields at human -confirmed or database-verified status, with a default threshold, wherein the configurable transformation gate threshold has a minimum of 60 percent, a default of 80 percent, and a maximum of 100 percent of mandatory physics fields at human -confirmed or database-verified status, and wherein each Spark Block is further configured to carry a World Model compatible payload comprising seven canonical fields: (i) block identifier and type; (ii) domain and subdomain classification; (iii) confirmed mandatory physics fields with confidence status tags; (iv) a physics constraint vector encoding the engineering requirements as an n-dimensional parameter set for cross-domain knowledge graph query; (v) a CDKG search vector derived from the physics constraint vector for routing to the cross-domain discovery engine; (vi) a provenance record identifying the originating phase, creation timestamp, and human engineer identifier; and (vii) a transformation eligibility flag reflecting current gate status; wherein the payload format is independent of the specific large language model or world model executing the system, such that the Spark Block record remains valid and query able across model version transitions.wherein computer-implemented system (100) includes a computer-implemented physics validation orchestration system (118) that solves the problem of computational resource exhaustion in high-fidelity engineering physics simulations by implementing stage-proportional validation depth,an initialization sequence engine (120) configured to, upon receipt of a domain and subdomain selection, execute an active domain physics pre-loading operation by querying a Physics Formula Library (PFL)- a version-controlled computational repository configured to store, retrieve, and execute formal physical relationships, wherein the pre-loading operation retrieves from the PFL and injects into the analysiscontext: (a) governing equations applicable to the selected domain in both symbolic and executable form; (b) theoretical performance limits and thermodynamic bounds for the selected subdomain, including absolute physical bounds derived from fundamental laws stored in the PFL; (c) a unit -compatibility matrix encoding dimensional analysis rules and unit compatibility constraints for the selected domain; (d) physical constants required by the retrieved governing equations, drawn from a standardized registry within the PFL comprising universal constants with associated units and uncertainty values; and (e) domain applicability conditions specifying the valid operating regimes for each retrieved equation; wherein the active domain physics pre-loading completes before the first Spark Block is created, such that physics constraint checks including dimensional consistency verification execute from the moment the first block is populated;a four-dimensional adaptive routing table (122) configured to determine physics validation intensity as a composite function of: (i) Spark Block innovation stage, wherein each stage maps to a base intensity level; (ii) domain complexity score reflecting the number of coupled physics phenomena in the target domain; (iii) CDKG analogy density reflecting the number of cross-domain analogies available for the current problem statement; and (iv) patent white-space score reflecting the degree of unoccupied IP space adjacent to the current Spark Block; anda four-layer physics validation pipeline (124) activatable at graduated intensity levels comprising: Layer 1 Constraint Feasibility check operating at intensity approximately 0.05 with a target execution time in seconds, performing deterministic verification of physical law compliance against PFL-retrieved governing equations; Layer 2 Symbolic Derivation engine operating at intensity approximately predefined threshold with a target execution time in minutes, deriving first -principles performance requirements using PFL governing equations and physical constants; Layer 3 PhysicsNeMo Surrogate evaluation operating at intensity approximately predefined threshold with a target execution time in tens of minutes, executing a pre -trained physics-informed neural surrogate model to generate a predicted performance envelope with uncertainty bounds; and Layer 4 Full Physics Simulation operating at intensity approximately predefined threshold with a target execution time in hours, executing a full computational physics simulation with standards compliance and FMEA output.

4. The computer-implemented system as claimed in claim 3, wherein the fourdimensional adaptive routing table (122) further modifies the physics validationintensity based on a Breakthrough Potential Score (BPS) computed for each Spark Block as a weighted composite of five axes comprising: Transformation Potential, Cross-Domain Synergy, Constraint Elimination, Scalability, and Paradigm-Shift Potential, each scored against historical breakthrough benchmarks stored in the cross - domain knowledge graph; wherein a BPS score above a configurable elevation threshold triggers automatic escalation to the next higher validation layer, and wherein BPS axis weights are updated by a self-optimizing learning engine based on correlation between BPS scores at Spark Block stage and observed patent filing and commercialization outcomes at Building Block stage.

5. The computer-implemented system as claimed in claim 3, wherein the four-layer physics validation pipeline (124) further comprises an outcome feedback recalibration module (126) configured to: compare the predicted performance envelope generated by Layer 3 PhysicsNeMo Surrogate evaluation against the actual performance outcomes recorded when the corresponding Spark Block transforms into a validated Building Block; compute a Layer 4 versus Layer 1 divergence metric reflecting the accuracy of the Layer 1 constraint check relative to the Layer 4 full simulation result; automatically recalibrate the base intensity routing thresholds in the four -dimensional adaptive routing table when the divergence metric exceeds a configurable recalibration threshold; and record the recalibration event in a version history log preserving the pre- recalibration routing table values for audit and reproducibility purposes, wherein the four-layer physics validation pipeline (124) operates at the following specific graduated intensity levels: Layer 1 Constraint Feasibility check at intensity approximately 0.05; Layer 2 Symbolic Derivation engine at intensity approximately 0.20; Layer 3 PhysicsNeMo Surrogate evaluation at intensity approximately 0.50; and Layer 4 Full Physics Simulation at intensity approximately 0.95; wherein the graduated intensity levels define a stage-proportional computation allocation that deterministically prevents Layer 4 simulation resource invocation for concept variants eliminated at lower intensity layers, producing a measured reduction in total compute resource consumption exceeding 250-fold compared to conventional brute-force fullsimulation workflows..

6. The computer-implemented system (100) as claimed in claim 3, wherein the active domain physics pre-loading operation retrieves domain intelligence from a plurality of domain intelligence modules each corresponding to a specific engineering domain and subdomain, wherein each domain intelligence module is backed by the PhysicsFormula Library (PFL) and stores: (i) the complete set of governing equations applicable to the domain expressed in both symbolic and computational form, with source citations traceable to peer-reviewed literature or standardized materials databases; (ii) the complete set of theoretical performance limits and thermodynamic bounds for the subdomain derived from fundamental laws stored in the PFL; (iii) a unit-compatibility matrix encoding dimensional analysis rules for the domain, executable as a dimensional consistency check against any physics field value submitted for database-verified status; (iv) material property ranges indexed by property type and operating condition, with domain applicability conditions specifying the valid operating regimes for each stored value; and (v) regulatory threshold values with applicable standard identifiers and revision dates; and wherein the active domain physics pre-loading confirms successful population of all five domain intelligence categories before setting the physics pre-loading status to active, with any incomplete category triggering a targeted PFL retrieval retry before the spark block creation interface is enabled.

7. The computer-implemented system (100) as claimed in claim 1, wherein the computer- implemented type-specific adaptive elicitation engine (128) is configured to transform natural language engineering observations into physics-bounded structured records, comprising:a stage classification module (130) configured to receive an initial natural language input from an engineer and classify the input into an innovation stage type selected from the group consisting of: Problem, Idea, Possibility, and IFR / Moonshot, based on the presence or absence of failure description language, mechanism hypothesis language, technology convergence language, and aspirational function language;an adaptive elicitation module (132) configured to conduct a structured multiturn dialogue with the engineer, wherein: the number of turns is specific to the classified innovation stage type; the specific question posed at each turn is selected based on the current field population state of the structured record, such that turns are collapsed when an engineer's initial input pre-populates one or more mandatory fields; and each turn poses a maximum of one clarifying question;a field population module (134) configured to populate mandatory physics fields in the structured record exclusively from values explicitly confirmed by the engineer during the adaptive elicitation dialogue, wherein Al-inferred values areproposed to the engineer for confirmation but are not entered as confirmed field values without explicit engineer confirmation; anda structured record generator (136) configured to assemble a typed HANDOFF block from the confirmed mandatory physics field values upon satisfaction of the typespecific gate condition, wherein the HANDOFF block is assembled from confirmed field values only and not from the conversation transcript.

8. The computer-implemented system as claimed in claim 7, wherein the adaptive elicitation module (132) implements the following type-specific protocols:for a Problem Spark Block: a six -turn protocol comprising Turn 1 eliciting the failure mechanism, Turn 2 eliciting operating conditions and system hierarchy, Turn 3 eliciting the quantified performance gap with baseline and target values, Turn 4 eliciting the physical contradiction between two opposing parameters, Turn 5 eliciting the root cause mechanism, and Turn 6 eliciting hard constraints; with a gate condition requiring all six fields at KNOWN status before the HANDOFF block is generated; for an Idea Spark Block: a four-turn protocol comprising Turn II receiving the idea and reflecting the mechanism back for confirmation, Turn 12 eliciting the source analogue system name and TRL, Turn 13 eliciting a minimum of three source -to-target parameter pairs in the attribute mapping, and Turn 14 eliciting the single primary transfer uncertainty; with a gate condition requiring all four fields at CONFIRMED status;for a Possibility Spark Block: a five-turn protocol comprising Turn Pl eliciting the target domain application at subsystem specificity, Turn P2 eliciting the source mechanism and origin industry, Turn P3 eliciting the TRL vector comprising TRL integer, DELTA-TRL-per-year, and projected TRL date, Turn P4 eliciting the manufacturing or physics barrier with quantified parameter gap, and Turn P5 eliciting the theoretical value limit and timing risk with a specific named event and date; with a gate condition requiring all six fields at CONFIRMED status; andfor an IFR / Moonshot Spark Block: a four-turn protocol comprising Turn IFR1 receiving the aspirational statement without imposing the IFR formula, Turn IFR2 co- constructing the IFR formula through dialogue, Turn IFR3 eliciting the fundamental physics barrier and ambient resource inventory, and Turn IFR4 eliciting the physics ceiling and ten-times transformation vector; with a gate condition requiring all five fields at CONFIRMED status;wherein the adaptive elicitation module (132) implements the following named enforcement behaviors as computational transformations applied during the dialogue: a Mechanism abstraction engine (138) configured to detect product names, brand names, or model names in engineer inputs and transform them into verb -noun physics statements by abstracting from the named product to the underlying physical mechanism, rejecting the product-name form and substituting the abstracted mechanism form in the relevant mandatory field;a solution smuggling detector (140) configured to detect technology names in IFR / Moonshot formula fields and redirect the engineer to replace the technology name with a function-only statement, enforcing that the IFR formula contains no technology names;a Catalyst Date Enforcement module (142) configured to detect vague timing language in Possibility timing risk fields and require substitution with a specific named event and specific date before the timing risk field is marked CONFIRMED;a TRL Velocity Computation module (144) configured to compute a DELTA-TRL-per-year rate from patent publication velocity data when an engineer cannot provide the rate directly, and to inject the computed rate into the TRL vector field as an AI-d erived value for engineer confirmation; anda CDKG ambient resource query module (146) configured to fire a cross-domain knowledge graph query for ambient energy and material resources when an IFR ambient resource inventory contains fewer than two entries, surfacing cross -domain resource candidates for engineer selection;wherein the structured record generator (136) generates the following type-specific HANDOFF blocks;an IDEA HANDOFF block comprising: mechanism of action field with confidence tag; source analogue identifier and TRL with confidence tag; attribute mapping table with a minimum of three source-to-target parameter pairs each comprising parameter name, source domain value, target domain value, and adaptation delta; primary transfer uncertainty field with specificity classification; target domain context; and a provenance record identifying the elicitation session, turn count, and engineer identifier;a POSS HANDOFF block comprising: target domain application at subsystem specificity; source mechanism and origin industry with confidence tag; TRL vector comprising TRL integer, DELTA-TRL-per-year, and projected TRL date;manufacturing or physics barrier with named parameter, current value, required value, and gap; theoretical value limit in absolute units; timing risk with specific named event and date; possibility classification on a two-by-two impact-by-potential-value matrix; and a provenance record; andan IFR HANDOFF block comprising: IFR formula as a function -only statement free of technology names; fundamental physics barrier statement; ambient resource inventory with a minimum of two entries each comprising resource type, availability conditions, and energy or material quantity; physics ceiling identifying the governing law and theoretical limit; ten -times transformation vector; and a provenance record.

9. The computer-implemented system as claimed in claim 1, wherein the computer- implemented physics-grounded cross-domain knowledge graph engine (148) for engineering analogy discovery, comprises:a physics statement compiler (150) configured to receive a Spark Block HANDOFF record and compile a physics-grounded discovery query comprising: the governing physical phenomenon class identified in the Spark Block; a Physics Bridge equation identified as the domain-neutral governing equation shared by both the problem domain and potential solution domains; the physical contradiction parameters from the Spark Block; and the hard constraints expressed as physics bounds;a functional decomposition module configured to decompose the engineering system identified in the Spark Block into a black-box functional representation, identifying the physical actions performed by each component and mapping each action to a Universal Function Class;a Physics Bridge mapping module (154) configured to identify, from the functional decomposition output, the performance bottleneck component and map it to its Universal Function Class and to the Physics Bridge equation that governs the bottleneck performance;a cross-domain graph traversal engine (156) configured to traverse a cross-domain knowledge graph using the Universal Function Class and Physics Bridge equation as traversal parameters, wherein the traversal is conducted by matching Physics Bridge equation parameters and not by matching terminology or keyword similarity, and wherein source domains within the domain of the querying Spark Block are excluded from the traversal; anda transfer synthesis module (158) configured to generate, for each candidate crossdomain match, a transfer brief comprising: a Transfer Logic statement explaining themechanism of transfer; a Constraint Boundary identifying the operating conditions within which the transfer is valid; Transfer Evidence comprising the source domain performance data and TRL; and an Implementation Gap identifying the specific adaptation required and the associated risk score.

10. The computer-implemented system as claimed in claim 9, wherein the cross-domain knowledge graph engine (148) comprises: a minimum of 13 distinct engineering and scientific domains; a minimum of 530 distinct function nodes each assigned to a Universal Function Class; and typed edges comprising at least ANALOGOUS TO, IMPLEMENTS, and ENABLES relationships between function nodes across domains; and wherein the Universal Function Classes are exactly eight in number comprising: MOVE, CONVERT, STORE, COMMUNICATE, REGULATE, SEPARATE, ASSEMBLE, and SUPPORT, derived from a synthesis of functional basis taxonomies including the Hirtz functional basis, AskNature biological function taxonomy, and NIST engineering function classification,wherein the cross-domain graph traversal engine (156) assigns each candidate crossdomain match an equivalence score on a four-layer scale comprising:surface equivalence at score range 0.3 to 0.5, representing terminology or functional label similarity with no shared governing equation, associated with a transfer success rate of 30 to 50 percent;process equivalence at score range 0.5 to 0.7, representing shared operational sequence or workflow similarity with no shared governing equation, associated with a transfer success rate of 50 to 70 percent;structural equivalence at score range 0.7 to 0.85, representing shared architectural pattern or physical configuration with partial governing equation overlap, associated with a transfer success rate of 70 to 85 percent; andcausal equivalence at score range 0.85 to 0.95, representing an identical Physics Bridge equation governing both the source domain solution and the target domain problem, associated with a transfer success rate of 85 to 95 percent;wherein only matches at Structural or Causal equivalence are surfaced to the engineer by default, and wherein matches at Surface or Process equivalence are archived and accessible only upon explicit engineer request,wherein the cross-domain graph traversal engine (156) enforces a mandatory four-step sequence of decompose, map, discover, and synthesise, and rejects any query that attempts to initiate traversal at the discover step without having completed thedecompose and map steps; the physics statement compiler (150) rejects queries that do not contain the physics bridge equation and returns the query to the Spark Block intake with a specific instruction identifying the missing physics grounding; a query provenance record is generated for each traversal comprising the Physics Bridge equation used, the Universal Function Class matched, the domains traversed, and the equivalence scores of all matches found; and the system documents a performance differential between keyword-input traversal producing 0.3 to 0.5 match scores and physics-grounded input traversal producing 0.85 to 0.95 match scores as a performance guarantee embedded in the system specification.

11. The computer-implemented system as claimed in claim 1, wherein a computer- implemented active context propagation system (160) for an upstream R&D intelligence platform, comprises:a scored insight selection module (162) configured to receive a plurality of crossdomain analogy matches from the CDKG engine, each match comprising a transfer brief with Transfer Logic, Constraint Boundary, Transfer Evidence, and Implementation Gap, and to compute, for each match, a granular set of scored insight statements at the insight level rather than at the analogue level, wherein each scored insight statement comprises a numerical score on a defined scale, a scoring rationale identifying what the insight captures correctly and what it lacks, and a specificity classification selected from specific mechanism transfer or generic conceptual parallel;an insight selection interface (164) configured to present the scored insight statements to a human engineer for selection via checkbox, and further configured to operate in an autonomous selection mode above a configurable confidence threshold wherein the system selects insights without human confirmation;an active context record constructor (166) configured to assemble, from the selected insight statements, an active context record comprising: an Architecture Alignment field identifying how the source domain solution architecture maps to the target domain system; a Projected Impact field computed from the Physics Bridge equation applied to the source domain performance data and the target domain gap metrics, expressed in domain-specific quantified units; Conceptual Design Specifications derived from the Transfer Logic of selected insights; Operating Constraints extracted from the Constraint Boundary field of each selected insight and expressed as quantified engineering parameters; a Potential Failure Mode statement identifying the specific failure riskarising from mechanism transfer; and CPC and IPC patent classification codes autogenerated from the domain and mechanism of each matched analogue; anda downstream modification engine (168) configured to inject the active context record into subsequent Think Model engine invocations as a mandatory input, such that the active context record deterministically modifies the analysis scope and output of each downstream engine,wherein the downstream modification engine (168) applies the active context record to the following Think Model engines (170) with the following deterministic modifications:a context enrichment engine (172) receives the Operating Constraints and Potential Failure Mode from the active context record and incorporates them as additional constraints and risk inputs to the analysis, expanding the constraint envelope beyond the constraints confirmed during Spark Block intake;a functional analysis engine (174) receives cross-domain resolution pathways derived from the selected insight statements and generates function model outputs that include cross-domain resolution options tagged with the insight identifier and the Physics Bridge equation supporting each resolution;a contradiction analysis engine receives a cross-domain contradiction resolution history comprising prior instances in the CDKG where the same or similar contradiction was resolved in another domain, and surfaces these instances as prioritized TRIZ principle candidates ranked by causal equivalence score; and a first principles engine receives the Physics Bridge equations associated with selected insights as additional governing relationships supplementing the governing equations retrieved during domain physics pre-loading, expanding the first principles derivation to include cross-domain physics relationships not natively present in the target domain; andwherein all outputs from all four Think Model engines carry a traceability tag referencing the active context record identifier, enabling full provenance reconstruction from any Building Block field back through the active context record to the originating CDKG match and Spark Block physics field,wherein the scored insight selection module (162) further generates, for each scored insight statement, an insight meta-evaluation comprising: a scoring rationale statement that identifies in structured form the specific aspect of the mechanism that the insight captures correctly, the specific aspect that the insight does not capture or generalizesbeyond what the evidence supports, and the evidentiary basis for the score; a specificity classification that categorizes the insight as either a specific mechanism transfer, wherein the source domain physics mechanism maps directly to the target domain physics bottleneck via the Physics Bridge equation, or a generic conceptual parallel, wherein the analogy is structural or process-level without a shared governing equation; and a visual distinction flag that the insight selection interface renders differently for specific mechanism transfers versus generic conceptual parallels, enabling an engineer to priorities mechanism-level insights before conceptual-level insights during manual selection.

12. The computer-implemented system as claimed in claim 1, wherein a computer- implemented physics-validated progression gate (180) for an upstream R&D intelligence system, the gate comprising:a physics-grounded synthesis qualification engine (182) configured to receive as input: (i) a confirmed Spark Block handoff record comprising a plurality of mandatory physics fields populated and confirmed during structured intake, wherein each physics field includes a confidence status selected from Al -derived, database-verified, or human-confirmed; and (ii) a Think Model evidence package comprising structured outputs generated by a plurality of Think Models executed on said confirmed Spark Block handoff record;a field -level delta computation module (184) configured to compare, on a field -by-fi eld basis, the confirmed mandatory physics fields from the Spark Block handoff record against corresponding physics parameters derived from the Think Model evidence package, wherein the comparison identifies: consistency between the originally confirmed field values and the Think Model-derived parameters; modifications required to one or more physics fields based on Think Model evidence; or fundamental contradictions in the physics fields that cannot be resolved by field modification; a physics surrogate validation module (186) configured to execute, upon initiation of the progression gate, a PhysicsNeMo surrogate model at a configurable intensity level of at least 0.85, wherein the surrogate model receives as input the confirmed physics constraint vector from the Spark Block handoff record and generates a physics feasibility confidence score and a predicted performance envelope for the physics fields under evaluation;a synthesis qualification determination engine (188) configured to compute, from the field-level delta computation and the physics surrogate validation output, aqualification verdict selected from: CONFIRMED, wherein the Think Model evidence and surrogate validation output are consistent with the confirmed mandatory physics fields and no field modification is required; REFINED, wherein the Think Model evidence or surrogate validation output requires modification of one or more physics fields, with a provenance record of original field values, modified field values, and the specific evidence driving each modification; and INVALIDATED, wherein the Think Model evidence or surrogate validation output contradicts the mandatory physics fields in a manner that cannot be resolved by field modification, with a re-framing directive identifying the specific physics rationale for invalidation and the corrected problem framing;a configurable autonomy controller (190) configured to operate in human-confirmation mode, wherein the system presents the qualification verdict and supporting evidence to a human engineer for confirmation prior to executing transformation, or in autonomous mode above a configurable confidence threshold, wherein the system executes the qualification verdict without requiring human confirmation; anda transformation executor (192) configured to: upon CONFIRMED verdict, transform the Spark Block into its primary Building Block target with the original physics fields intact and a transformation trace record; upon REFINED verdict, transform the Spark Block into its primary Building Block target with modified physics fields and a field modification provenance log preserving original values; and upon INVALIDATED verdict, return the Spark Block to the universal chat intake engine with the re-framing directive and a pre-populated corrected Spark Block stub containing physics fields adjusted to reflect the specific invalidation rationale.

13. The computer-implemented system as claimed in claim 12, wherein the physics surrogate validation module (186) invokes a domain-specific PhysicsNeMo surrogate model selected based on the Spark Block type and domain, whereinfor a Problem Spark Block, the surrogate model receives the failure mechanism, operating conditions, and quantified baseline gap as physics constraint inputs and validates whether the stated performance target is achievable within the governing physics equations and theoretical limits of the target domain;for an Idea Spark Block, the surrogate model receives the mechanism of action, attribute mapping parameter pairs, and primary transfer uncertainty as physicsconstraint inputs and validates whether the source-domain physics mechanism remains operative under the target-domain adaptation conditions specified in the attribute mapping;for a Possibility Spark Block, the surrogate model receives the source mechanism, manufacturing or physics barrier, and theoretical value limit as physics constraint inputs and validates whether the TRL trajectory is consistent with the barrier's physical resolution timeline;for an IFR / Moonshot Spark Block, the surrogate model receives the zero-harm condition, ambient resource, and physics ceiling as physics constraint inputs and validates whether the IFR formula is bounded above by the stated governing law and whether the ambient resource is thermodynamically sufficient to supply the required function; andwherein the surrogate validation generates, for each Spark Block type: a physics feasibility confidence score bounded between 0.0 and 1.0; a predicted performance envelope with uncertainty bounds; and a named failure mode for cases where the confidence score falls below the configurable transformation threshold, and wherein the transformation executor (192) records, in a Transformation Trace Matrix, for each transformation event: (i) source Spark Block identifier, type, and originating phase; (ii) target Building Block identifier and type; (iii) qualification verdict and confidence score; (iv) PhysicsNeMo surrogate model identifier, version, and domain; (v) physics feasibility confidence score and predicted performance envelope from surrogate validation; (vi) for CONFIRMED verdicts: a field consistency confirmation record per mandatory physics field; (vii) for REFINED verdicts: a field modification provenance log comprising, per modified field, the original confirmed value, the modified value, the specific Think Model output or surrogate validation result driving the modification, and the modification rationale; (viii) for INVALIDATED verdicts: the re-framing directive comprising the specific physics parameters that contradict the original fields, the governing physics rationale for invalidation, and the corrected problem framing used to populate the returned Spark Block stub; (ix) human decisionmaker identifier when operating in human-confirmation mode; and (x) timestamp and version history; wherein the Transformation Trace Matrix is queryable by downstream Building Block generation modules and patent disclosure generation modules toestablish full traceability between each validated Building Block field and its originating Spark Block physics evidence,wherein the re-framing directive generated upon an INVALIDATED verdict comprises: a physics contradiction statement identifying the specific parameter or governing equation that conflicts with the original Spark Block mandatory physics fields; a domain boundary statement identifying whether the invalidation results from an absolute physics violation within the stated domain, an architecture -specific constraint embedded as a domain-independent physics constraint, or a modality mismatch wherein the stated performance target is achievable but not within the physics layer implied by the original problem framing; a corrected problem framing statement specifying the modified physics scope, operating regime, or modality boundary within which the performance target is achievable; and a pre-populated corrected Spark Block stub comprising the corrected mandatory physics fields derived from the re-framing directive, with confidence status set to Al -derived for all corrected fields pending engineer confirmation, and a traceability link to the invalidated Spark Block record and the specific surrogate validation result driving the invalidation; wherein the pre-populated Spark Block stub is passed to the universal chat intake engine as a structured seed, and the intake engine initiates a targeted confirmation dialogue beginning from the first modified field rather than from the initial open-ended intake turn.

14. The computer-implemented system as claimed in claim 1, wherein a computer- implemented physics-constrained design space exploration system (194) for generating and evaluating a plurality of concept variants from a validated Concept Building Block, the system (194) comprises:a variable extraction module (196) configured to receive a Concept Building Block comprising a plurality of mandatory physics fields, each physics field having a confirmed value, physical units, and a governing physical equation linkage, and to automatically extract therefrom a set of active design variables comprising for each variable: a variable identifier derived from the physics field name; a physical unit specification; a lower bound and an upper bound derived from the theoretical limits and Physics Ceiling stored in the Physics Formula Library for the relevant domain; and a governing equation reference establishing the physical constraint on the variable's range;a space-filling sampling module (198) configured to receive the set of active design variables and their physics-derived bounds, and to execute a Design of Experiments sampling algorithm — including Latin Hypercube Sampling — across the N-dimensional space defined by the active variables to autonomously generate a variant matrix comprising a configurable number of synthetic concept variants, wherein each synthetic concept variant is assigned: a unique variant identifier; a generation type classification of Archetype Seed for the original physics-consistent Concept Building Block instantiation, or Synthetic Expansion for each sampled variant; and a parameter vector specifying values for each active design variable within the physics-derived bounds;a Physics Ceiling pre-filter module (200) configured to evaluate each Synthetic Expansion variant against the Physics Ceiling derived from the Physics Formula Library for the relevant domain, and to eliminate without surrogate invocation any variant whose parameter vector violates the Physics Ceiling, assigning such variants a Physics Ceiling Rejected classification and recording the specific Physics Ceiling parameter and governing equation that caused rejection;a multi-parameter surrogate evaluation module (202) configured to receive all variants passing the Physics Ceiling pre-filter and to execute, in batch, a physics-informed neural surrogate model against each surviving variant, generating for each variant a multi-parameter output vector comprising at minimum: a predicted performance score for each target output parameter; a physics feasibility confidence score bounded between 0.0 and 1.0; and a composite optimality score computed from the weighted combination of the predicted performance scores and the physics feasibility confidence score; anda ranked downstream handoff module (204) configured to rank all surviving variants by composite optimality score, select a configurable top-N subset of highest -ranked variants, and generate a Downstream Simulation Handoff Record comprising: the parameter vector for each top-N variant; the surrogate-predicted multi-parameter output vector; the physics feasibility confidence score and composite optimality score; provenance links to the originating Concept Building Block and the specific Physics Formula Library constraints applied; and a downstream routing directive specifying the target high-fidelity simulation environment, wherein said Downstream Simulation Handoff Record is forwarded to a downstream full physics simulation layer therebypreventing computational resource exhaustion in the downstream simulation environment by restricting high-fidelity simulation to only the highest -ranked physics-feasible variants,wherein the variable extraction module (196) derives the lower bound and upper bound for each active design variable without requiring manual specification by an engineer, by: retrieving from the Physics Formula Library the theoretical minimum and theoretical maximum values for the physical quantity corresponding to the physics field, as bounded by the governing physical equation and domain applicability conditions; applying a configurable safety margin below the Physics Ceiling to set the upper bound, such that no generated variant exceeds the theoretical limit by construction; and recording the auto-derived bounds in a Sampling Space Provenance Record that establishes traceability between each variable bound and the specific Physics Formula Library entry from which it was derived; wherein the Sampling Space Provenance Record is included in the Downstream Simulation Handoff Record to enable downstream simulation engineers to verify the physical basis of each variable range without reference to the originating KRAFT session,wherein the variant matrix comprises exactly one Archetype Seed variant that is a direct instantiation of the Concept Building Block mandatory physics fields at their confirmed values, and a plurality of Synthetic Expansion variants generated by applying the Design of Experiments sampling algorithm to perturb each active design variable independently within its physics-derived bounds; and the Physics Ceiling prefilter module records, upon completion of pre-filtering, a Physics Ceiling Filter Report comprising: the total number of variants generated; the number of variants rejected at the Physics Ceiling pre-filter stage; the rejection rate as a percentage of total variants; for each rejected variant, the specific Physics Ceiling parameter exceeded and the governing equation that establishes the ceiling; and the number of variants forwarded to the multi-parameter surrogate evaluation module; wherein the Physics Ceiling Filter Report is appended to the Downstream Simulation Handoff Record as evidence of computational resource conservation achieved prior to surrogate invocation;, wherein the multi-parameter surrogate evaluation module (202) generates, for each surviving variant, a multi-parameter output vector comprising performance predictions across a plurality of target parameters specified in the Concept Building Block, wherein each target parameter prediction includes: a point estimate in the physical units of the target parameter; an uncertainty bound representing the surrogate model'sprediction confidence interval; a pass / fail determination against the performance target specified in the Concept Building Block; and a named physics failure mode for variants where the point estimate falls below the performance target; and wherein the ranked downstream handoff module generates the Downstream Simulation Handoff Record in a structured format compatible with a target downstream simulation environment selected from the group comprising: Computational Fluid Dynamics solvers; Finite Element Analysis solvers; Multiphysics simulation environments; and Physics-Informed Neural Operator frameworks; and wherein the Downstream Simulation Handoff Record specifies, for each top-N variant, the complete parameter vector in the input format required by the specified downstream simulation environment, together with the surrogate-predicted performance envelope as a reference baseline for downstream simulation result validation; andwherein a computer-implemented system for motivation-driven project initialization in an upstream R&D intelligence platform, comprising:a motivation classification module (206) configured to receive a project initiation input and classify the initiation input into a motivation type selected from the group consisting of: a Moonshot Motivation, representing a breakthrough vision, Ideal Final Result, or lOx performance target; a Market Problem Motivation, representing an identified customer pain point, competitive gap, or engineering failure condition in a target market; and a New Possibilities Motivation, representing an emerging technology trend, newly available capability, or cross-domain breakthrough with application potential in the project domain;a primary entry point routing module (208) configured to, upon motivation classification, automatically instantiate as the primary Spark Block for the project a Spark Block type matched to the classified motivation type according to the following routing table: a Moonshot Motivation routes to an IFR / Moonshot Spark Block as primary entry point; a Market Problem Motivation routes to a Problem Spark Block as primary entry point; and a New Possibilities Motivation routes to a Possibility Spark Block as primary entry point; wherein no project can be created without a classified motivation type, and wherein the primary Spark Block type is system-enforced based on the motivation classification and cannot be overridden to a different primary entry type for the classified motivation;a project context pre-configuration module (210) configured to, upon primary entry point routing, pre-load into the project context: the domain intelligence modulecorresponding to the motivation type's target physics domain; the Physics Formula Library entries applicable to the motivation type's domain; and a motivation -typespecific project configuration comprising the set of Spark Block types that are permissible as secondary entries for the project, the Building Block transformation pathway sequence associated with the primary entry type, and the Proof Point criteria applicable to the classified motivation type; anda motivation-to-workflow-path binding module (212) configured to record, in the project metadata, the classified motivation type, the routing decision made, the pre- loaded domain intelligence module identifiers, and the expected downstream Building Block progression — Needs for Market Problem Motivation projects, Concepts for Moonshot Motivation projects, and Opportunities for New Possibilities Motivation projects — thereby establishing a complete motivation-to-outcome traceability chain from project initialization through to validated Building Block creation.

15. A computer-implemented method for creating and managing physics-constrained innovation objects, comprising:receiving, by a spark block creation engine (110) of a computer-implemented system (100). raw engineering observation and classify the raw engineering observation into one of a plurality of innovation stage types selected from a group consisting of problem, idea, possibility, IFR / moonshot, and initiative;requiring, by a type-specific physics field enforcement module (112), population of a mandatory set of physics fields specific to the classified innovation stage type upon classification, wherein each physics field requires identification of a governing physical phenomenon class and at least one associated measurable parameter computable from or bounded by a governing physical equation, wherein the spark block creation engine (110) cannot be created without satisfying the mandatory physics field set for its classified type;assigning, by a confidence status assignment module (114), to each populated physics field, a confidence status selected from the group consisting of: Al -derived, database-verified, and human -confirmed; andpreventing, by a configurable transformation gate module (116), transformation of a spark block into a building block unless a user-configurable minimum proportion of mandatory physics fields carry a confidence status of human -confirmed or database- verified.

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