System and method for symbolic logic-based evaluation and composite descriptor generation

Abstract

A computer-implemented method and system for symbolic logic-based evaluation are disclosed. Input data (e.g., responses, sensor feeds, or financial indicators) are normalized into symbolic parameters (pᵢ) through processor-executed routines. A clause engine evaluates Boolean expressions and records outcomes in a clause-truth registry. Weighted aggregation generates a composite descriptor (θ), with redundancy reduced through Karnaugh-map or equivalent logical simplification. Visualization modules—including θ gauges, flow diagrams, heatmaps, and logic networks—produce human- and machine-readable outputs that can interface with compliance APIs. Technical effects include reduced computational complexity, lower latency through incremental recomputation, and compliance-grade traceability. The invention is domain-agnostic, with applicability to education, healthcare (limited to monitoring and decision-support functions), finance, industrial IoT, cybersecurity, and enterprise analytics. Healthcare embodiments explicitly exclude diagnosis or treatment. All operations are processor-executed, producing compliance-linked outputs that extend beyond abstract algorithms, mental acts, or mere presentation of information.

Description

[0001] INTERNATIONAL APPLICATION UNDER THE PATENT COOPERATION

[0002] TREATY (PCT)

[0003] TITLE OF THE INVENTION:

[0004] SYSTEM AND METHOD FOR SYMBOLIC LOGIC-BASED EVALUATION AND

[0005] COMPOSITE DESCRIPTOR GENERATION

[0006] APPLICANT(S):

[0007] Name: VASANTHARAJAN GANESAN

[0008] Nationality: INDIAN

[0009] Address: 30, Ground Floor, Srinivasa Pillai Street, West Mambalam,

[0010] Chennai, Tamil Nadu, 600033, India

[0011] INVENTOR(S):

[0012] Name: VASANTHARAJAN GANESAN

[0013] Nationality: INDIAN

[0014] Address: 30, Ground Floor, Srinivasa Pillai Street, West Mambalam,

[0015] Chennai, Tamil Nadu, 600033, India

[0016] PRIORITY DATA:

[0017] Country Code: IN

[0018] Indian Patent Application No. 202541082807

[0019] Filing Date: 01 September 2025

[0020] Office: IPO (India)

[0021] INTERNATIONAL FILING DATE:

[0022] [To be filled by Receiving Office]

[0023] INTERNATIONAL APPLICATION NUMBER:

[0024] [Assigned later by WIPO] I. TECHNICAL FIELD

[0025] The present invention relates to computer-implemented systems for automated data evaluation and symbolic logic-based assessment. In particular, it concerns a method, system, and computer-readable medium for transforming diverse forms of input data — such as structured responses, sensor measurements, financial indicators, log files, or real-time streams — into symbolic parameters, evaluating Boolean clause relationships among those parameters, and generating a composite descriptor (θ) that represents the functional state of a subject or system.

[0026] The invention provides concrete technical improvements in computing, including clause-truth registry management, adaptive threshold recalibration, Karnaugh- style logical simplification, and incremental real-time computation. These processor-executed operations achieve measurable technical effects of reduced computational complexity, improved responsiveness, transparent auditability, and traceability suitable for compliance environments.

[0027] The framework is domain-agnostic and can be deployed across multiple sectors, including education, healthcare (restricted to monitoring and decision-support functions only), finance, industrial operations, Internet-of-Things ecosystems, cybersecurity, workplace analytics, and enterprise platforms.

[0028] II. BACKGROUND ART

[0029] Existing evaluation and scoring frameworks in domains such as education, healthcare monitoring, financial risk analysis, and industrial diagnostics predominantly rely on fixed- threshold rules, statistical aggregation, or opaque machine-learning models. Each of these approaches has inherent limitations. Rule-based and statistical systems lack adaptability when underlying baselines shift, resulting in rigid outcomes that fail to reflect evolving contexts. Machine-learning systems, although adaptive, often function as “black boxes,” requiring substantial computational resources while generating outputs that lack transparency, traceability, and auditability.

[0030] Conventional systems also struggle to represent interdependent or conditional logic among variables. For example, in patient monitoring, alerts are typically triggered by isolated threshold breaches without contextual correlation. Financial risk tools may depend on static indices that fail to capture interrelated market dynamics. In education and workplace analytics, summative scores obscure clause-level interactions, thereby reducing interpretability and limiting actionable insights. These shortcomings highlight the need for a processor-executed evaluation framework that combines adaptability, computational efficiency, and transparency. Specifically, a system is required that can:

[0031] • Recompute results incrementally to minimize redundancy and latency;

[0032] • Represent logical conditions transparently through clause-truth registries suitable for compliance-grade auditing;

[0033] • Adapt thresholds dynamically to reflect population baselines or changing data distributions; and

[0034] • Generate machine-readable outputs directly linked to internal logic for automation and regulatory integration.

[0035] The present invention addresses these deficiencies by introducing a symbolic logic-based evaluation system that unites clause-level interpretability with measurable technical improvements in computational efficiency and compliance-ready traceability.

[0036] III. DISCLOSURE OF INVENTION

[0037] The present invention provides a computer-implemented method, system, and computer- readable medium for symbolic logic-based evaluation and composite descriptor generation.

[0038] In one aspect, digitized input data are transformed into symbolic parameters (pi, i = l ...n) through processor-executed normalization routines. These parameters are evaluated by a clause engine that applies Boolean expressions — including conjunction (AND), disjunction (OR), negation (NOT), and threshold comparisons — to determine clause-level truth values. The outcomes are stored in a clause-truth registry, implemented as an indexed and queryable structure that ensures transparency, traceability, and compliance support.

[0039] A descriptor generator aggregates clause outcomes, optionally with assigned weights, to produce one or more composite descriptors (θ). Logical simplification techniques, such as Karnaugh-map consolidation or equivalent reductions, optimize computation by eliminating redundancy.

[0040] Visualization and reporting modules generate both human-readable and machine-readable outputs — including θ gauges, heatmaps, flow diagrams, and clause-parameter networks. These outputs are dynamically linked to the clause-truth registry, ensuring that results remain interpretable, auditable, and suitable for compliance-grade monitoring and automated system integration.

[0041] The architecture further supports adaptive threshold recalibration based on statistical baselines, as well as incremental recomputation of only affected clauses when new inputs are received. This reduces latency and improves efficiency compared to batch-based approaches.

[0042] The invention achieves measurable technical effects, including reduced computational complexity, real-time adaptability, compliance-ready traceability, and scalable deployment across heterogeneous computing environments. It is domain-agnostic, with potential applications in education, healthcare (limited to monitoring and decision-support functions), finance, industrial loT, cybersecurity, and enterprise analytics.

[0043] IV. DETAILED DESCRIPTION OF THE INVENTION

[0044] The invention will now be described with reference to illustrative embodiments. These examples are non-limiting and provided solely to enable a person skilled in the art. Specific parameter mappings, clause sets, and weighting strategies are considered implementation- dependent and are not limiting to the disclosure. All operations described herein are processor- executed and extend beyond mental acts or abstract methods.

[0045] 1. Symbolic Parameters

[0046] Digitized input data are received through an input interface (101) and transformed into symbolic parameters (102) by processor-executed normalization routines.

[0047] • Parameters (pi, i = 1 . . . n) may represent behavioral traits, physiological signals, financial indicators, sensor measurements, or other domain-specific constructs.

[0048] • Each parameter is mapped into a bounded range (e.g., 0-100) or equivalent normalized domain to enable consistent logical evaluation.

[0049] • The mapping from raw inputs to symbolic parameters is domain-dependent and regarded as an implementation detail.

[0050] These symbolic parameters (102) form the foundational variables for clause evaluation. As illustrated in FIG. 1, raw inputs are normalized into symbolic representations.

[0051] 2. Clause Evaluation Engine A clause evaluation engine (103) applies Boolean expressions (201) over symbolic parameters.

[0052] • Operators include conjunction (AND), disjunction (OR), negation (NOT), and threshold comparisons.

[0053] • Each clause evaluates to a binary truth value (TRUE / FALSE).

[0054] Clause outcomes are stored in a clause-truth registry (104), implemented as an indexed, queryable structure.

[0055] • The registry ensures traceability of each clause result.

[0056] • When input parameters change, the engine supports incremental recomputation, updating only the affected clauses, thereby reducing latency compared to batch re- evaluation.

[0057] 3. Composite Descriptor (0)

[0058] A descriptor generator (105) produces a composite descriptor (θ) summarizing the evaluated state of the system.

[0059] • θ is computed as an aggregation of clause outcomes, optionally weighted.

[0060] • A normalization function maps the weighted sum into a defined scale (e.g., 0-1, percentage, or category band).

[0061] Logical simplification techniques, such as Karnaugh-map consolidation (202) or equivalent reductions, may be applied to eliminate redundant clauses. This optimization improves processor efficiency and reduces memory usage.

[0062] 4. Clause-Truth Registry and Logic Mapping

[0063] The clause-truth registry (104) serves as the authoritative record of clause outcomes.

[0064] • Each entry contains the clause identifier, evaluation result, and metadata such as timestamp and source.

[0065] • Registry entries may be visualized as Karnaugh-style layouts (204), where related or overlapping clauses are grouped (203) for efficiency.

[0066] • Simplified clause results are written back into the registry, ensuring that each θ descriptor can be audited back to its logical components. This provides a transparent link between input parameters, clause evaluations, and the composite descriptor.

[0067] 5. Visualization and Reporting Modules

[0068] A visualization module (106) generates outputs that are both human-interpretable and machine- readable, such as:

[0069] • θ gauges (301) with pointer (302) showing the current composite descriptor within defined status bands.

[0070] • Contribution charts (303) illustrating the influence of individual clauses or parameters.

[0071] • Heatmaps (304) highlighting parameter contributions across domains.

[0072] • Flow networks (305) tracing dependencies from raw inputs → θ parameters (102) → θ clauses (201) → θ descriptor.

[0073] These outputs are dynamically linked to the clause-truth registry (104), ensuring compliance- grade traceability and enabling automated system integration.

[0074] 6. Output Rendering

[0075] Outputs may be:

[0076] • Displayed through user interfaces or dashboards,

[0077] • Transmitted via APIs for integration with external systems, or

[0078] • Exported as machine-readable logs for compliance auditing.

[0079] Unlike conventional dashboards, these outputs remain tied to the clause-truth registry, ensuring traceability and automation.

[0080] 7. Computer-Implemented System

[0081] The invention may be realized in a computing system comprising:

[0082] • At least one processor and memory storing executable instructions,

[0083] • Input interfaces (e.g., APIs, GUIs, or sensor feeds). Execution of stored instructions implements the clause evaluation engine (103), the descriptor generator (105), and visualization module (106). The system may be deployed in standalone servers, cloud-hosted environments, or distributed loT frameworks. Optional secure modules (e.g., TPMs, encrypted pipelines) can enforce compliance and access control in regulated environments.

[0084] 8. Theta Flow Network

[0085] The theta flow network (305) depicts relationships between symbolic parameters, evaluated clauses, and the composite descriptor θ.

[0086] • It is a representational graph generated by software routines.

[0087] • No specialized hardware is required.

[0088] • The flow network enhances transparency by showing cause-effect pathways in real time.

[0089] 9. Adaptive Thresholds

[0090] Clause thresholds may be recalibrated dynamically to reflect statistical baselines or population means.

[0091] • Example: A fixed rule “p1> 60” may adapt to “p1> μ,” where p is the moving average of recent inputs.

[0092] • Such recalibration ensures fairness and adaptability.

[0093] These recalibrations are processor-executed and cannot be feasibly performed as mental acts, consistent with PCT Article 27(5).

[0094] 10. Computer-Readable Medium

[0095] The invention may also be embodied as a computer-readable medium storing instructions that, when executed by one or more processors, implement:

[0096] • Parameter mapping,

[0097] Clause evaluation,

[0098] Registry management, Descriptor computation, and

[0099] • Visualization rendering.

[0100] This ensures portability across platforms and deployment environments.

[0101] 11. Visualization Variants

[0102] Outputs may take multiple forms, including:

[0103] • Interactive dashboards,

[0104] • Static diagrams, or

[0105] • Textual compliance logs.

[0106] These formats are illustrative only, and the invention is not restricted to any single visualization type.

[0107] 12. Domain-Specific Deployment

[0108] The architecture is domain-agnostic and configurable for:

[0109] • Education (academic profiling, cognitive assessment),

[0110] • Healthcare (monitoring physiological indicators for decision-support only; excludes diagnosis or treatment),

[0111] • Finance (risk scoring, anomaly detection),

[0112] • Industry (loT monitoring, predictive maintenance),

[0113] • Cybersecurity (event correlation, anomaly alerts).

[0114] All healthcare embodiments are expressly limited to monitoring and decision-support functions only.

[0115] 13. Real-Time Clause Evaluation

[0116] Clause evaluations and θ computations may be performed incrementally in real time.

[0117] • Each new data point triggers recomputation of dependent clauses only.

[0118] • θ is updated immediately, enabling continuous monitoring for domains such as psychometric evaluation, risk analysis, or equipment health monitoring. This incremental process lowers latency and reduces computational overhead.

[0119] V BRIEF DESCRIPTION OF THE DRAWINGS

[0120] • FIG. 1 illustrates the processor-executed system architecture. Input data are acquired via an input interface (101), converted into symbolic parameters (102), evaluated by a clause engine (103), stored in a clause-truth registry (104), aggregated by a θ descriptor generator (105), and rendered by a visualization module (106).

[0121] • FIG. 2 shows a Karnaugh-map-style visualization (202) of Boolean clauses (201), with simplification groupings (203) feeding into the clause-truth registry (104) for efficient aggregation.

[0122] • FIG. 3 depicts processor-generated outputs, including a θ gauge (301) with pointer (302), contribution charts (303), heatmaps (304), and a flow network (305) tracing symbolic parameters (102) through clause evaluations (201) to the composite descriptor θ. These outputs are machine-generated and directly tied to clause-truth registries and θ computations, ensuring traceability and compliance readiness.

[0123] Note: The drawings are submitted on separate sheets in compliance with PCT Rule 7 and Rules 11.13-11.14. Each figure is labeled and referenced in the description. Each sheet is numbered consecutively and contains the applicant’s name. Required margins (minimum 2 cm top / left and 2 cm bottom / right; preferred 4 cm top / left and 3 cm bottom / right) have been observed. The figures are illustrative and non-limiting; alternative visualization formats remain within the scope of the invention.

[0124] VI. TECHNICAL EFFECT AND ADVANTAGES OF THE INVENTION

[0125] The disclosed invention achieves multiple technical effects and advantages over conventional threshold systems, statistical aggregation methods, and opaque machine-learning models:

[0126] 1. Real-Time Incremental Clause Evaluation

[0127] • Only affected clauses are recomputed when inputs change, reducing computational complexity compared to repeated O(n c) batch runs.

[0128] • Enables low-latency operation in streaming and real-time environments.

[0129] 2. Efficiency Through Logical Simplification • Karnaugh-map style consolidation and equivalent reductions minimize redundancy.

[0130] • Lowers the number of active clauses, processor load, and memory requirements.

[0131] 3. Enhanced Transparency and Traceability

[0132] • Clause-truth registries maintain indexed, queryable records of all evaluations.

[0133] • Each composite descriptor ( θ) is directly traceable to contributing parameters, improving explainability, debugging, and compliance verification.

[0134] 4. Adaptive Threshold Recalibration

[0135] • Processor-executed recalibration adjusts thresholds dynamically to statistical baselines (e.g., mean, percentile, distribution shift).

[0136] • Provides fairness, adaptability, and resilience to population drift — functions infeasible for unaided human cognition.

[0137] 5. Interpretable Multi-Layered Visualization

[0138] • Outputs include θ gauges, contribution charts, heatmaps, and flow networks.

[0139] • Unlike mere dashboards or static presentations of information, outputs remain machine- linked to compliance APIs and automation triggers.

[0140] 6. Scalable Multi-Domain Deployment

[0141] • Architecture is configurable for education, healthcare (decision-support only), finance, industrial loT, cybersecurity, and enterprise analytics.

[0142] • Scales efficiently through parallel or distributed clause evaluation.

[0143] 7. Security and Compliance Integration

[0144] • Supports encrypted data pipelines, audit trails, and role-based access control.

[0145] • Enables deployment in regulated environments requiring compliance-grade traceability.

[0146] Summary of Technical Advantage

[0147] The invention provides a processor-implemented symbolic evaluation system that reduces computational complexity, supports real-time responsiveness, enhances interpretability, and produces compliance-ready outputs. These technical effects extend beyond abstract algorithms, mental acts, or mere presentations of information, thereby satisfying the requirements of technical contribution and patent-eligible subject matter under PCT Articles 5-7 and Article 33(4)

[0148] VII. INDUSTRIAL APPLICABILITY

[0149] The invention is industrially applicable across domains that require automated, reliable, and interpretable evaluation of complex data. Its processor-executed design enables deployment in real-world environments where efficiency, compliance, and scalability are critical.

[0150] • Education: Integration into e-learning platforms for real-time academic or behavioral profiling.

[0151] • Healthcare: Monitoring of physiological indicators for decision-support only, expressly excluding diagnosis or treatment of humans or animals.

[0152] • Finance: Continuous risk assessment, fraud detection, and anomaly monitoring with auditable outputs.

[0153] • Industrial loT : Embedding into sensor-driven monitoring frameworks for predictive maintenance, safety control, and operational efficiency.

[0154] • Cybersecurity: Correlation of event logs and anomaly detection for real-time alerts.

[0155] • Enterprise Analytics: Deployment in workforce monitoring, compliance dashboards, and digital transformation systems.

[0156] The system is compatible with standalone servers, cloud services, and distributed loT environments. Its modular architecture supports scaling to large populations, heterogeneous data streams, and regulated sectors.

[0157] Accordingly, the invention satisfies the requirement of industrial applicability under PCT Article 33(4), demonstrating practical utility and readiness for deployment across multiple industries.

[0158] VIII. NOTES ON PROPRIETARY ELEMENTS

[0159] The embodiments and examples provided herein are sufficient for enablement and practice of the invention in accordance with PCT Articles 5-7 and Rules 5-11. Certain implementation details — such as parameter mappings, weight assignments, optimization heuristics, and production-level clause sets — are considered proprietary and are intentionally withheld.

[0160] • Enablement Sufficiency:

[0161] Illustrative symbolic parameters (pi), Boolean clause structures, and composite descriptor computations (θ) have been disclosed in sufficient detail to allow a person skilled in the art to reproduce a working embodiment.

[0162] • Withheld Elements:

[0163] Exact numeric weightings, domain-specific mappings, and optimization pipelines used in industrial deployment are not disclosed. These details are not required for enablement and remain proprietary trade secrets.

[0164] • Scope of Protection:

[0165] The claims define the inventive concept broadly in terms of symbolic parameterization, clause evaluation, incremental recomputation, logical simplification, and compliance- linked outputs. The absence of proprietary details does not limit the scope of the claims.

[0166] • Healthcare Disclaimer:

[0167] All healthcare-related embodiments are expressly limited to monitoring and decision- support functions only, and exclude any diagnosis or treatment of humans or animals.

[0168] Accordingly, the disclosure balances statutory enablement requirements with the protection of confidential know-how, ensuring that competitors cannot replicate production-level systems while the invention remains fully patentable and enforceable.

[0169] APPENDICES

[0170] APPENDIX A: SYMBOLIC PARAMETER ARCHITECTURE (Illustrative, Non- Limiting Example)

[0171] This Appendix provides a symbolic, illustrative representation of parameter abstraction as applied in the invention. It is included solely to satisfy the enablement requirements of PCT Articles 5-7 and Rules 5-11. No proprietary parameter mappings, domain-specific constructs, or production implementations are disclosed.

[0172] Parameter Abstraction

[0173] • Input data are abstracted into symbolic parameters (pi, i = 1 . . .n).

[0174] • Example: Parameter A and Parameter B may denote generic constructs derived from normalized raw inputs.

[0175] • Parameters C-Z (or additional pi) may denote further constructs, attributes, or conditional factors depending on the embodiment.

[0176] Confidentiality and Compliance Note

[0177] The precise mapping of raw inputs to symbolic parameters is domain-dependent and may remain proprietary. Disclosure of symbolic placeholders is sufficient for statutory enablement while preserving flexibility across education, finance, healthcare, industrial monitoring, cybersecurity, and other domains. All parameter transformations are processor-executed, producing technical effects beyond abstract mental acts, mathematical methods, or algorithms per se.

[0178] Healthcare Disclaimer

[0179] All healthcare-related embodiments are expressly limited to monitoring and decision-support functions only, and explicitly exclude any diagnosis or treatment of humans or animals.

[0180] Scope

[0181] This disclosure is illustrative and non-limiting. The full scope of the invention is defined exclusively by the appended claims. APPENDIX B: ILLUSTRATIVE OUTPUT FORMATS (Illustrative, Non-Limiting Examples)

[0182] The following illustrative output formats are provided solely to satisfy enablement requirements under PCT Articles 5-7 and Rules 5-11. They demonstrate symbolic ways in which composite descriptors and clause contributions may be represented. Proprietary numeric ranges, parameter mappings, and clause sets are intentionally withheld. These embodiments are illustrative and non-limiting.

[0183] 1. Theta Gauge (Conceptual Example)

[0184] • Computed θ descriptor = θ* (symbolic value).

[0185] • Status classification bands may be abstract (e.g., Low, Medium, High).

[0186] • The gauge output provides a machine-generated indication of θ within these bands.

[0187] • Both human-readable and machine-readable forms may be supported, enabling automated triggers and compliance audits.

[0188] 2. Clause Contribution Summary (Symbolic Table)

[0189] (Illustrative only: clauses evaluate to TRUE or FALSE, and if TRUE, contribute weighted values to θ. Actual clause sets, thresholds, and weight values are proprietary and not disclosed.)

[0190] 3. Heatmap-Style View (Conceptual)

[0191] (Symbolic only: contribution strengths may be visualized via colors, gradients, or intensity. Labels and presentation formats are domain-dependent and non-limiting.)

[0192] 4. Flow Network Trace (Symbolic Example)

[0193] • Input data is normalized into symbolic parameters.

[0194] • Parameters are evaluated against processor-executed thresholds.

[0195] • Clause outcomes are recorded in the clause-truth registry.

[0196] • θ descriptor is recomputed based on contributing clauses.

[0197] (This illustrates the symbolic cause-effect chain; actual numeric values, mappings, and clause sets remain proprietary and undisclosed.)

[0198] Confidentiality and Compliance Note

[0199] The foregoing output formats are illustrative and non-limiting. They demonstrate how symbolic parameters and composite descriptors may be processor-generated and displayed in human- and machine-readable forms. Actual numeric ranges, identifiers, weight assignments, and parameter mappings are proprietary and intentionally withheld, as they are unnecessary for enablement and do not limit the claims.

[0200] All healthcare-related embodiments are expressly limited to monitoring and decision-support functions only, excluding any diagnosis or treatment of humans or animals. These disclosures ensure compliance with PCT Articles 5-7 and Rules 5-11, while avoiding exclusions under PCT Article 27(5) and Rule 39.1(v) APPENDIX C: ENABLED EMBODIMENT (Illustrative, Non-Limiting Examples)

[0201] The following disclosure provides simplified illustrative embodiments solely to satisfy enablement requirements under PCT Articles 5-7 and Rules 5-11. These embodiments are symbolic in nature and do not disclose proprietary parameter mappings, clause structures, or weighting strategies. They demonstrate that a person skilled in the art could reproduce a basic embodiment, without restricting the broader scope of the invention.

[0202] 1. Parameter Definition (Illustrative Example)

[0203] Parameters such as Parameter A and Parameter B may represent constructs derived from input data (e.g., survey responses, sensor values, or transaction metrics). Each parameter may be normalized into a defined range (e.g., 0-100) to enable consistency across heterogeneous sources.

[0204] • Example: A raw score of 80 out of 120 may be normalized to 67 on a 0-100 scale.

[0205] • The specific mapping functions are domain-dependent and intentionally withheld, allowing the invention to be flexibly applied across education, healthcare, finance, industrial monitoring, and other domains.

[0206] 2. Adaptive Thresholding (Illustrative Example)

[0207] Static thresholds (e.g., “A > 60”) may be replaced with adaptive thresholds based on statistical baselines.

[0208] • Example: “ may be recalibrated to “A > μ,” where p represents a mean, moving average, or percentile derived from population distributions.

[0209] • Thresholds may evolve dynamically as new data are ingested, enabling adaptive recalibration in real-time environments.

[0210] 3. Clause Evaluation (Illustrative Example)

[0211] Clauses are Boolean expressions defined over parameters.

[0212] • Example: AND (NOT B).

[0213] • If Parameter A exceeds its adaptive threshold and Parameter B does not, the clause evaluates to TRUE. All clause outcomes are stored in the clause-truth registry, implemented as an indexed, queryable structure for immutability, traceability, and auditability.

[0214] 4. Composite 0 Computation (Illustrative Example)

[0215] A composite descriptor θ may be generated by aggregating clause outcomes with weights: θ = g ( Σ wi x ti ) where ti are clause outcomes (TRUE / FALSE), wi are clause weights, and is a normalization function mapping the aggregated result into a defined scale (e.g., [0-1], [0-100]).

[0216] • Example: min-max scaling may be applied to normalize results.

[0217] • Precise weights and scaling functions are proprietary and intentionally withheld.

[0218] 5. Real-Time Updates (Illustrative Example)

[0219] Clause outcomes may be recomputed incrementally as new data are received.

[0220] • Updates to a parameter trigger recomputation only of dependent clauses.

[0221] • θ is refreshed automatically.

[0222] • This reduces latency, avoids redundant recomputation, and enables continuous monitoring in domains such as psychometrics, healthcare monitoring, fraud detection, and industrial loT.

[0223] 6. Illustrative Outputs

[0224] Processor-generated outputs may include, without limitation:

[0225] • A θ gauge showing descriptor values within categorical bands (e.g., Low, Medium, High).

[0226] • Clause contribution summary tables showing parameter influence.

[0227] • Heatmap-style views representing parameter contributions.

[0228] • Flow diagrams or parameter-clause networks tracing how inputs contribute to θ.

[0229] Outputs may be provided in both human-readable and machine-readable forms, enabling automated compliance verification and regulatory audits. 7. Illustrative Algorithms (Non-Limiting)

[0230] To demonstrate enablement, the following well-known algorithms may be applied in clause weighting, thresholding, or minimization:

[0231] • Exponential Weighted Moving Average (EWMA): wt = a × xt + (1 - α) * wt-1, where α ∈ (0,1).

[0232] • Bayesian Updating (simplified): Posterior = (Likelihood x Prior) / Evidence.

[0233] • Kalman Filter (simplified): Updated state = Previous state + Gain x (Observation - Prediction).

[0234] • CUSUM (Cumulative Sum Detection): St = max(0, St-1 + xt - k).

[0235] • Logical Minimization (example): Clauses simplify to A using reduction techniques.

[0236] These examples are illustrative only. Equivalent statistical, probabilistic, or logical methods may be substituted without departing from the scope of the invention.

[0237] 8. Domain Illustrations

[0238] • Psychometrics: If Attention > 60 AND NOT Fatigue → θ TRUE (decision-support index).

[0239] • Healthcare: If Heart Rate > mean + 2σ → θ TRUE (monitoring flag only; not diagnosi s / treatment) .

[0240] • Finance: If Price Change > 3σ → T θRUE (risk flag).

[0241] • loT: If Vibration > threshold AND Temperature > 80°C → TRUE (maintenance alert).

[0242] Disclaimer

[0243] The foregoing embodiments are provided for illustrative enablement only. They represent simplified, non-limiting examples that demonstrate symbolic parameterization, clause evaluation, descriptor generation, and processor-generated outputs. Actual industrial or commercial implementations may employ alternative algorithms, parameterizations, or optimization strategies. All healthcare-related embodiments are expressly limited to monitoring and decision-support functions only, and exclude any diagnosis or treatment of humans or animals.

[0244] These disclosures are intended solely to enable a person skilled in the art to reproduce a basic embodiment for compliance with PCT Articles 5-7 and Rules 5-11, without restricting or limiting the broader scope of the claims.

[0245] APPENDIX D: PRIOR ART DISCUSSION AND NOVELTY DISTINCTIONS

[0246] This appendix is provided for illustrative discussion only. It is non-limiting, not exhaustive, and intended solely to contextualize novelty, inventive step, and technical advancement of the invention in accordance with PCT Articles 5-7 and Article 33(1). The examples and references cited are representative and do not restrict the scope of the claims, which alone define the invention. To the best of the Applicant’s knowledge, no prior system combines symbolic logic clause evaluations with a clause-truth registry, weighted composite descriptor generation (θ), Karnaugh-style logical simplification, and compliance-linked outputs in the manner disclosed herein.

[0247] D.l Identified Prior Art References (Representative Examples)

[0248] • [PA1] Traditional Summative Scoring Tools (e g., Big Five Personality Software,

[0249] Standard Cognitive Profiling Tools).

[0250] Limitation: Simple linear aggregation, no symbolic parameters, no Boolean logic, no clause registry, no θ-style composite.

[0251] • [PA2] Fixed-Rubric Typology Systems (e g., MBTI® and analogues). Limitation: Dichotomous categorisation, no symbolic logic, no weighted clause evaluation.

[0252] • [PA3] Healthcare Monitoring Systems (e.g., US 2015 / 0186815 A1 “System for

[0253] Cognitive Assessment”).

[0254] Limitation: Numeric / ML scores only, no symbolic clause framework, no transparent clause registry.

[0255] • [PA4] Industrial loT / Sensor Diagnostics (e g., EP 2 233 440 A1 “Evaluation of

[0256] Mental State” and related equipment monitoring).

[0257] Limitation: Pattern / statistical thresholds only; lacks Boolean clause mapping, θ computation, or interpretability.

[0258] • [PA5] Financial Risk Scoring Tools (e.g., WO 2020 / 187654 A1 Adaptive Educational

[0259] / Risk Testing).

[0260] Limitation: Adaptive ML-based risk prediction, no symbolic clause evaluation, no clause registry, no θ descriptor.

[0261] • [PA6] AI-Based Educational / Decision Systems (e g., US 11,000,501 “AI-Based

[0262] Educational Feedback System”).

[0263] Limitation: Black-box ML outputs, no symbolic parameters, no interpretable clause logging, no θ composite. • [PA7] Visualization Dashboards for Analytics (e g., US 2017 / 0250369 A1 “System for Visualizing Psychometric Assessments”).

[0264] Limitation: Graphical dashboards over conventional metrics, no symbolic logic pathways, no Karnaugh simplification.

[0265] D.2 Technical Gaps in Prior Art

[0266] • No Symbolic Parameter Abstraction: Prior tools sum raw scores or apply ML directly; none introduce domain-agnostic symbolic parameters (pi).

[0267] • No Boolean Clause-Based Reasoning: Existing systems lack explicit Boolean clauses capturing conditional logic.

[0268] • No Clause-Truth Registry: No prior art maintains persistent clause outcomes for traceability and auditability.

[0269] • No Composite Descriptor (0): Scores are statistical only; none aggregate weighted clause truth values into θ.

[0270] • Limited Interpretability: Conventional dashboards lack Karnaugh maps, flow diagrams, or clause contribution charts.

[0271] • No Real-Time Adaptivity: Thresholds remain fixed; prior systems lack incremental recomputation and adaptive recalibration.

[0272] • Hybrid Symbolic-Statistical Integration Not Taught: Known tools use either rules or ML, not a fused symbolic-numeric framework.

[0273] D.3 Distinctive Features of the Present Invention

[0274] D.4 Non-Obviousness Considerations

[0275] The claimed system achieves synergistic integration of symbolic abstraction, Boolean clauses, clause-truth registry, weighted aggregation, adaptive thresholds, real-time evaluation, and visualization tied to logic. Even combining prior systems would not disclose clause registries, weighted θ computation, or Karnaugh optimization. Thus, the framework is non-obvious under PCT Article 33(1).

[0276] D.5 Technical Effects Achieved

[0277] • Reduced Computation: Incremental recomputation and Karnaugh simplification minimize processor cycles (Claims 10-11).

[0278] • Enhanced Transparency: Clause-truth registry + visualizations ensure traceability (Claims 1, 5-8).

[0279] • Adaptability: Dynamic thresholds improve fairness and responsiveness (Claim 9).

[0280] • Cross-Domain Deployment: Symbolic architecture allows multi-sector application (Claims 12, 19, 23, 25).

[0281] • Improved Trust: Gauges and contribution charts enable user understanding (Claims 7, D.6 Conclusion

[0282] The invention is distinguished from prior art by introducing symbolic parameter abstraction, Boolean clause evaluation, a clause-truth registry, a composite descriptor (θ), multi-layered visualization, and adaptive real-time operation. These features yield measurable technical effects including improved efficiency, transparency, adaptability, and scalability. Healthcare embodiments are expressly limited to monitoring and decision-support functions, while diagnosis and treatment are excluded. Accordingly, the invention demonstrates novelty, inventive step, and technical eligibility under international patent standards, and is not excluded under PCT Article 27(5).

Claims

AMENDED CLAIMS received by the International Bureau on 06 March 2026 (06.03.2026)

1. [Amended] A computer-implemented method for symbolic logic -based evaluation and composite descriptor generation, the method executed on one or more processors and comprising: (i) acquiring digitized input data from external sources via an input interface; (ii) transforming the input data into symbolic parameters p_i (i = 1 to n) by processor- executed normalization routines mapping the input data to a bounded range; (iii) incrementally evaluating Boolean logic clauses defined over the symbolic parameters using a clause evaluation engine, wherein a dependency mechanism identifies parameter-clause relationships and recomputes only clauses directly affected by a parameter change; (iv) storing clause outcomes in a clause-truth registry implemented as an indexed and queryable data structure, each registry entry containing a clause identifier, an evaluation result, and metadata including timestamps and data sources; (v) generating a composite descriptor θ by weighted aggregation of registry-stored clause outcomes following logical simplification routines including consolidation of redundant clauses; (vi) producing machine-interpretable outputs including at least one of a logic flow graph, a gauge with threshold-linked triggers, a clause-parameter flow network, or a parameter contribution heatmap, each dynamically linked to the clause-truth registry; wherein the clause- truth registry enables the composite descriptor (θ) to be audited back to its logical components.

2. The method of Claim 1, wherein Boolean operators comprise threshold comparison, conjunction (AND), disjunction (OR), or negation (NOT), each operator executed by processor routines for high-speed symbolic evaluation.

3. The method of Claim 1, wherein at least one clause includes a negation of a symbolic parameter, the negation executed by processor instructions that automatically recompute dependent clause outcomes.

4. The method of Claim 1, wherein computing θ comprises dynamically adjusting clause weights through processor-executed routines that update weighting factors in response to monitored performance metrics, thereby enhancing robustness and consistency.

5. The method of Claim 1, wherein the clause-truth registry is implemented as a queryable indexed structure configured to generate audit trails and compliance records.

6. The method of Claim 1, wherein generating a logic flow diagram further comprises automated consolidation of redundant clause paths by a simplification engine, thereby reducing graph complexity and memory usage.

7. The method of Claim 1, wherein generating a θ gauge further comprises embedding threshold-trigger mechanisms that initiate recalibration or external control actions upon boundary crossing.

8. The method of Claim 6, wherein the logic flow diagram further comprises a clause-parameter flow network dynamically linked to the registry, wherein each connection is machine-interpretable and queryable for automated analysis.

9. The method of Claim 1, wherein Boolean clause thresholds are recalibrated by processor-executed routines in response to evolving input trends, enabling real-time adjustment of evaluation sensitivity while maintaining fairness.

10. The method of Claim 1, wherein redundant logical conditions are eliminated using processor-implemented simplification routines, thereby optimizing throughput and memory footprint.

11. The method of Claim 1, wherein steps (iii)-(v) are performed incrementally in real time, refreshing θ without recomputing unaffected clauses.

12. The method of Claim 1, wherein integration with domain- specific data sources comprises preprocessing pipelines that normalize heterogeneous data from education, healthcare, finance, or loT platforms, said normalization executed by processor routines.

13. [Amended] A computer-implemented evaluation system comprising: (i) at least one processor and memory storing instructions; (ii) an input interface configured to normalize incoming data into symbolic parameters p_i; (iii) a clause evaluation engine configured to incrementally evaluate Boolean logic clauses using a dependency mechanism that identifies parameter-clause relationships and recomputes only clauses directly affected by parameter change; (iv) a clause-truth registry implemented as an indexed and queryable data structure, each registry entry containing a clause identifier, an evaluation result, and metadata; (v) a descriptor generator configured to compute a composite descriptor θ by weighted aggregation of registry-stored clause outcomes following logical simplification; (vi) a visualization module configured to produce outputs dynamically linkedto the clause-truth registry; wherein the clause-truth registry enables the composite descriptor (θ) to be audited back to its logical components.

14. [Amended] A computer-readable medium storing processor-executable instructions which, when executed by one or more processors, cause a system to: normalize input data into symbolic parameters; incrementally evaluate Boolean logic clauses; record outcomes in a clause-truth registry; generate a composite descriptor 0 via weighted aggregation with logical simplification; and produce machine- interpretable outputs linked to the registry; wherein execution achieves technical effects of reduced computational complexity, adaptive evaluation, and transparent auditability.

15. The method of Claim 1, wherein symbolic parameters are derived from digitized response data, including but not limited to psychometric questionnaires, and wherein the clause evaluation engine executes automated consistency checks, the embodiment being limited to decision- support only and excluding diagnosis or treatment.

16. The method of Claim 1, wherein symbolic parameters correspond to healthcare-related sensor data or records, and θ represents a processor- computed index for monitoring or decision- support, the embodiment being expressly limited to non-diagnostic and non-therapeutic applications.

17. The method of Claim 1, wherein symbolic parameters are derived from financial indicators, and Boolean clauses incorporate anomaly-detection rules such that θ represents a risk score traceable to clause logic.

18. The method of Claim 1, wherein symbolic parameters are derived from industrial loT sensors, and θ represents predictive maintenance status or equipment indicators computed automatically by processors.

19. The method of Claim 1, wherein dashboards generated for users are dynamically linked to the clause-truth registry and expose machine- readable APIs configured for compliance audits.

20. The method of Claim 1, wherein θ triggers automated recalibration routines or external alerts when predefined thresholds are crossed.

21. The method of Claim 1, wherein θ is computed incrementally from streaming data to provide continuous monitoring with low-latency updates.

22. The method of Claim 1, wherein the clause-truth registry stores metadata including timestamps, data-source identifiers, and domain tags, thereby enabling compliance reporting and forensic audits.

23. The system of Claim 13, wherein dashboards generated by the visualization module are parameterized with access-control metadata, thereby enforcing role -based segregation in multi-tenant environments.

24. The system of Claim 13, wherein the descriptor generator computes multiple θ descriptors in parallel from overlapping subsets of clauses, thereby enabling multi-dimensional evaluation without recomputing shared conditions.

25. The medium of Claim 14, wherein the instructions further cause the system to establish secure pipelines with schema normalization, encryption, and latency optimization for API integration with external systems.

26. The system of Claim 13, further comprising secure hardware or trusted execution environments (TPM / TEE) with redundancy and watchdog modules, thereby enhancing reliability and compliance readiness.

27. A deployment method comprising integrating the evaluation system of Claim 13 with external enterprise or loT platforms through secure APIs and encrypted data pipelines, wherein the integration enables automated compliance reporting and machine-actionable feedback, the integration being processor-executed and not practicable as a manual process.[0001][0002]Statement under Article 19(1)[0003]The amendments to claims 1 , 13 and 14 clarify the architectural relationship between incremental clause evaluation, the dependency mechanism identifying parameter- clause relationships, and the clause-truth registry used for composite descriptor generation.[0004]In particular, the amended claims clarify that Boolean logic clauses defined over symbolic parameters are evaluated using a dependency mechanism that identifies parameter-clause relationships and recomputes only clauses directly affected by a parameter change. The amended claims further clarify that outcomes of clause evaluations are stored in a clause-truth registry implemented as an indexed and queryable data structure, and that a composite descriptor (6) is generated by weighted aggregation of registry-stored clause outcomes following logical simplification.[0005]These amendments emphasize the structural integration between incremental clause evaluation, registry-based storage of clause outcomes, and generation of machine- interpretable outputs dynamically linked to the registry. The amendments also clarify that the composite descriptor (6) can be reconstructed and audited from registry-stored clause outcomes and their associated metadata.[0006]The amendments are fully supported by the disclosure of the application as originally filed and introduce no new matter. Claims 2-12 and 15-27 remain unchanged.

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