Computer-aided system for multidimensional generative value assessment and applicant selection

Abstract

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

Description

TECHNICAL FIELD

[0001] The invention relates to computer-aided technologies for personnel evaluation and, in particular, a digital system for quantifying the potential of applicants through computer-aided modeling of structured and unstructured data. The invention is situated in the field of intelligent applicant evaluation, behavioral analysis, digital evidence verification, multidimensional classification, and automated decision-making for institutional recruitment, promotion, and leadership identification. BACKGROUND OF THE INVENTION

[0002] Traditional recruitment systems prioritize technical expertise, measurable performance indicators, and past achievements. However, global organizations increasingly require employees who can generate innovation, act as mentors, build collaborative networks, and foster collective development. Conventional systems are inadequately structured to assess generative behavior, intangible social contributions, ethical integrity, and the ability to build a long-term institutional legacy. Consequently, organizations lack computer-aided mechanisms to identify candidates whose value lies not only in their technical performance but also in their ability to foster learning ecosystems, create resilient organizational cultures, and drive institutional transformation.

[0003] Existing screening technologies generally rely on categorical descriptions, narrowly defined rating grids, and static sets of features that fail to capture relational motivations, authenticity, and adaptive behavioral patterns. Manual assessments are prone to bias, lack transparency, and cannot be scaled to large applicant pools requiring standardized analysis. The invention overcomes these limitations by implementing a computational architecture that formalizes generative dimensions, integrates behavioral, evidence-based, and contextual assessments, and generates standardized evaluation results through automated weighting, normalization, and classification.

[0004] Historically, the assessment and selection of talent in institutional settings has been dominated by systems that prioritize measurable technical skills, academic achievements, and standardized performance indicators. This leads to decision-making frameworks heavily focused on quantifiable outcomes and less on the multidimensional value of the individual. Traditional applicant assessment platforms and applicant tracking systems primarily function as data repositories and scoring systems, classifying candidates based on job requirements, keyword matches, or competency assessments. These systems treat applicants as a collection of disaggregated data points, reducing complex human behavior to categorical descriptions that can be matched against a predefined template.While conventional digital recruitment software typically supports structured data collection, resume analysis, competency mapping, and evaluation based on user-defined criteria, it lacks mechanisms for interpreting qualitative aspects of candidate performance such as innovation, mentoring, ethical integrity, and social engagement. Furthermore, existing systems do not assess relationship-building, future-oriented contributions, or a candidate's ability to strengthen organizational networks or create an institutional legacy—all essential indicators of generative performance.The lack of computer architectures capable of modeling human behavior in its relational and transformative dimensions has led to organizational environments that select technically competent but socially isolated individuals, resulting in fragile institutional cultures, low resilience, and minimal collective innovation.

[0005] Machine learning relies on rating systems that attempt to reduce bias and increase efficiency. However, these solutions suffer from fundamental weaknesses inherent in reductionist modeling. Ranking systems used in recruitment optimize predictive accuracy based on historical performance metrics, job profile fit, and risk minimization. However, they are based on datasets that reflect the current workforce, not the desired future state. Consequently, technology-based systems often reproduce entrenched organizational patterns instead of identifying agents of change. Furthermore, AI tools integrated into recruitment software typically operate with classification models trained on historical outcome variables such as promotion rates, length of service, or leadership positions.This leads to the neglect of creative leadership, mentoring, collaboration, and social impact. These approaches are inherently retrospective; they attempt to predict future success based on past performance indicators that may not reflect evolving institutional priorities. The inability of current systems to identify talent capable of creating new value forces organizations to pursue incremental rather than transformative development paths. This limits adaptability and cultural evolution.

[0006] Another structural limitation of existing systems is the lack of integrated, multidimensional assessment architectures capable of mapping interactions between behavioral, social, and institutional variables. Current instruments assess isolated traits or competencies but cannot calculate relational metrics, such as the ability of innovations to influence organizational culture or the potential of mentoring to create long-term institutional sustainability. The lack of computational frameworks for modeling emergent phenomena means that organizations cannot identify individuals who act as catalysts for collective transformation. Because current systems do not consider the institutional context, they cannot align assessment criteria with strategic goals, cultural values, or development priorities.Consequently, selection processes optimize general performance indicators rather than context-specific generative potential.

[0007] Another disadvantage of existing recruitment technologies is their lack of traceability and auditability. Systems based on subjective assessments, manual scoring, or implicit heuristics offer only limited transparency regarding decision-making processes. This lack of a traceable decision logic exposes institutions to legal risks, organizational bias, and the loss of institutional knowledge. If assessments cannot be digitally reconstructed, hiring outcomes cannot be analyzed, optimized, or validated. Traditional evaluation processes thus reinforce opaque, unsystematic decision-making processes instead of establishing transparent, evidence-based organizational management.

[0008] Existing systems ultimately assume linearity and stability in career development, which is incompatible with environments characterized by rapid technological change, cross-functional collaboration, and evolving institutional goals. Candidates who can operate across domains, build networks, and drive adaptive change are often undervalued, while those optimized for narrowly focused technical skills are disproportionately rewarded. This discrepancy not only undermines organizational resilience but also hinders the development of inclusive, ethical, and innovation-driven cultures.

[0009] In summary, while conventional recruitment technologies, psychometric systems, competency frameworks, behavioral interview methods, and AI-based analysis tools assess individual characteristics, retrospective outcomes, or self-assessments, they fail to capture generative potential, institutional impact, relational value, and long-term perspectives. These limitations lead to decisions that favor technical outcomes over collective growth, individual achievements over systemic transformation, and short-term performance over long-term institutional development. This exacerbates the existing gap that the SIGES-based system explicitly aims to close. SUMMARY OF THE INVENTION

[0010] The invention relates to a computer-aided system for multidimensional generative evaluation using an integrated digital architecture. This architecture comprises a data acquisition unit, a feature extraction unit, a weighting calculation unit, a quantitative evaluation unit, an evidence verification unit, a classification unit, a decision generation unit, and a system control unit. The system acquires structured datasets and narrative data, extracts feature vectors, assigns weights to them, calculates generative values, verifies evidence, and determines classification thresholds that represent the maturity level of the generative evaluation. The output results provide traceable digital datasets encoded with classification identifiers, feature summaries, and verification status.

[0011] The invention enables context-dependent adaptation by storing a generative profile definition matrix that reflects institution-specific weightings, dimension hierarchies, verification requirements, and expected behavioral characteristics. It also includes audit-proof calculation logs and status tracking, thereby facilitating compliance with regulatory requirements and the forensic reconstruction of evaluation results. The system supports adaptive, computer-aided modeling of the generative value and delivers digital decision results suitable for automated or manually controlled selection processes.

[0012] The main objective of the present invention is to provide a computer-based system for evaluating candidates based on their multidimensional generative value, rather than limiting assessment to narrowly defined technical competencies or historical performance indicators. The invention aims to introduce a digital architecture that systematically identifies innovation potential, mentoring capacities, collaborative skills, ethical integrity, and projected institutional legacy through computer-aided modeling of structured and unstructured data. A further objective of the invention is to overcome the limitations of existing solutions through transparent, evidence-based evaluation processes in which narrative statements, interview responses, and documented achievements are technically correlated to ensure authenticity, credibility, and contextual relevance.The system operationalizes generative evaluation in such a way that standardized, auditable results are generated, which can be replicated in various institutional contexts without subjective assessment or manual evaluation methods.

[0013] A further objective of the invention is the development of a calculation mechanism that integrates multi-layered dimensional weighting and quantitative evaluation to calculate a consolidated generative score capable of representing complex relational interactions between behavioral, social, technical, and institutional criteria. The invention aims to implement adaptive weighting structures that can be configured to reflect organization-specific priorities, thereby ensuring that the generative selection aligns with the institutional mission, culture, and strategic goals. By enabling dynamic adaptation, the invention is intended to support diverse environments, ranging from educational institutions and non-profit organizations to businesses and research ecosystems.

[0014] The invention aims to provide a system architecture that incorporates automated verification of evidence as a central evaluation function, thereby addressing the widespread lack of validation mechanisms in current applicant assessment technologies. Through metadata verification, content analysis, and document comparison, the system is designed to reduce reliance on unverifiable descriptions and increase the reliability of the evaluation results. This directly addresses the problem of self-report bias and staged responses, which are common in current recruitment and talent analysis systems.

[0015] A further objective of the invention is the generation of classification results that not only represent categories but also contain structured, machine-readable datasets with verification metadata, temporal identifiers, and an audit-proof coding system to ensure long-term transparency and traceability. The invention aims to establish a digital decision-making level that supports institutional governance, policy compliance, and data-driven optimization of talent selection and development processes.

[0016] Furthermore, the invention aims to implement a state-controlled, processor-driven workflow that coordinates data acquisition, feature extraction, weighting, evaluation, verification, and classification through deterministic scheduling and comprehensive event logging. This objective fulfills the requirements for operational reliability, process synchronization, and forensic traceability, thus enabling institutional actors to monitor system performance, evaluate technical behavior, and reconstruct decision-making processes as needed.

[0017] The invention further aims to provide a scalable computing infrastructure capable of evaluating large pools of applicants without compromising data quality, analytical depth, or contextualization. By integrating multidimensional processing functions, the invention enables environments for selecting large numbers of applicants in which complex behavioral analyses can be performed efficiently and without impairing interpretive accuracy.

[0018] Ultimately, the invention aims to fundamentally transform institutional selection processes by integrating generativity, collaboration, ethical responsibility, and long-term value creation into the foundation of computer-aided decision-making. The invention seeks to shift the logic of organizational evaluation from transactional selection to the systematic promotion of individuals who can generate sustainable collective benefits. This should enable the development of effective, ethically grounded, and innovation-oriented institutional ecosystems. BRIEF DESCRIPTION OF THE IMAGE

[0019] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a computer-implemented system for multidimensional generative value assessment and applicant selection.

[0020] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only the specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0021] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0022] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation of it.

[0023] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0024] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0026] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0027] Fig. Figure 1 shows a block diagram of a computer-implemented system for multidimensional generative value assessment and candidate selection. The system 100 comprises a data acquisition unit (102) configured to electronically receive candidate data, including structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews;a feature extraction unit (104) coupled to the data acquisition unit and configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors representing generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit (106) configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements, and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context;a quantitative rating unit (108) configured to calculate a generative rating by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; an evidence verification unit (110) configured to electronically validate referenced digital evidence by performing content extraction, metadata checking, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors;a classification determination unit (112) configured to assign a classification level to a candidate by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower, a middle, and an upper threshold, wherein the classification levels represent different generative maturity states and determine subsequent eligibility for selection; a decision generation unit (114) configured to produce a digital output data set that includes the classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium;and a system control unit (116) that operatively interacts with all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

[0028] In one embodiment, the data acquisition unit (102) comprises a configurable data interface architecture with structured input buffers for academic and professional datasets, as well as adaptive narrative data structures of variable length for unstructured interview responses. This allows the captured information to be stored in a unified digital representation that preserves the applicant-specific semantic context, relational dependencies, and metadata attributes such as provenance, input source category, and document type indicators, thus enabling subsequent computer-assisted processing that maintains the contextual integrity of the generative indicators disclosed by the applicant.

[0029] In one embodiment, the feature extraction unit (104) is configured to encode extracted features as vectorized digital representations that include fields for relational meaning, contextual emphasis, behavioral orientation, and semantic polarity, wherein each representation is stored as a multidimensional fixed-length or dynamically allocated data structure and includes additional data attributes that describe the origin of the extracted feature, the linguistic origin segment, and a category identifier corresponding to a generative dimension associated with innovation, mentoring, ethical consistency, social contribution, resilience, or other generative indicators derived from structured and unstructured inputs disclosed in the application documents.

[0030] In one embodiment, the weighting calculation unit (106) stores the definition matrix of the generative profile in a persistent digital structure containing hierarchical dimension identifiers, mappings between generative indicators and descriptors of organizational relevance, metadata descriptors expressing qualitative importance, and machine-readable references to evidence requirements, such that the stored matrix enables the calculation of weighted representations of extracted generative indicators according to institution-specific importance assignments without changing the structural representation of the underlying feature vectors.

[0031] In one embodiment, the quantitative evaluation unit (108) comprises a multidimensional scoring aggregation architecture configured to calculate composite generative scores by integrating weighted feature representations stored in system memory. The scoring architecture includes data structures representing interdimensional associations, contextual relevance weights, semantic uncertainty indicators, and attribute confidence indicators, such that a consolidated, system-generated generative score reflects complex relational interactions between technical, behavioral, and institutional impact dimensions within the applicant profile.

[0032] In one embodiment, the evidence verification unit (110) comprises a digital validation architecture configured to maintain a machine-readable representation of reference evidence and associated narrative statements, wherein this representation includes metadata fields for source identification, timestamps, author attributes, semantic alignment descriptors, and indicators of confirmation consistency, so that each piece of evidence can be computationally linked to extracted feature vectors to determine the structural agreement or divergence between the specified generative actions and the documented external confirmation.

[0033] In one embodiment, the classification determination unit (112) stores in memory a set of computational classification thresholds, represented as digital parameter limits and linked to lower, medium, and higher generative performance categories. Each limit is defined by a multi-attribute representation that includes context-specific sensitivity indicators, dimensional significance coefficients, and rating confidence attributes, such that the classification of an applicant is determined by comparing a consolidated generative rating score with the limit representations encoded in the system memory.

[0034] In one embodiment, the decision generation unit (114) stores digital representations of applicant evaluations as structured, machine-readable data records that include fields for performance classification identifiers, aggregated generative attribute summaries, evidence verification results, and interpretation metadata, wherein these data records are encoded in a traceable digital format that includes embedded identifiers for chronological sequence, authorization status, and change history, thereby enabling persistent traceability during audits and institutional accountability for evaluation decisions.

[0035] In one embodiment, the system control unit (116) manages a persistent state management representation that includes structured descriptions of operational phases associated with data acquisition, feature extraction, weighting calculation, quantitative evaluation, classification determination, and decision generation. This representation contains unique identifiers for each phase, dependency attributes, completion indicators, and event origin information, thus maintaining system-wide synchronization and fault tolerance throughout the entire computational environment.

[0036] In one embodiment, the system control unit (116) stores computation logs comprising time-related data records that encode operational results, execution status, interactions between units, and system events. Each log entry contains metadata fields that define the temporal sequence, operational context, traceability identifiers, and system state descriptions, thus ensuring digital traceability of the stored logs, enabling retrospective analysis, system optimization, and audit-driven control of the multidimensional applicant evaluation framework.

[0037] The detailed description of the invention is further supported by the information on the system architecture contained in the accompanying documentation. This illustrates the modular structure and the interconnected functional components of the computer-implemented system. Fig.Figure 1 shows the system architecture with a data acquisition unit (102), a feature extraction unit (104), a weighting calculation unit (106), a quantitative evaluation unit (108), an evidence verification unit (110), a classification unit (112), a decision generation unit (114), and a system control unit (116). All these units are implemented as discrete but interoperable computing modules that exchange data via defined interfaces under a coordinated control mechanism.

[0038] In embodiments of the invention, the system architecture is designed to operationalize a comprehensive generative evaluation, such as the SIGES framework (Comprehensive Generative Evaluation and Selection System). This is achieved by modeling dimensions of generative potential and transforming qualitative information provided by applicants into verifiable, traceable, computer-based indicators. SIGES provides a conceptual foundation on which applicants are evaluated not only with regard to their technical competence but also with regard to their proven ability to generate authentic added value, transfer knowledge, lead innovative processes, build networks, and create a sustainable institutional legacy.

[0039] The disclosed system integrates these principles by encoding applicant data into feature vectors aligned with generative dimensions and by applying configurable weighting models that reflect institutional priorities in terms of social value, collective development, and long-term impact.

[0040] In one embodiment, the system is configured to implement a structured process for defining generative profiles, which is technically equivalent to SIGES, with the organization defining a strategic generative profile that represents both technical and generative performance dimensions.

[0041] The strategic generative profile is digitally represented in a profile definition matrix stored in the system memory. Each dimension is coded within this matrix with sub-criteria, specific indicators, verification requirements, and numerical weightings. Dimensions with high institutional relevance, such as tradition, mentoring, innovation, and social engagement, receive higher weightings, while supporting dimensions, such as values, collaborations, and technological infrastructure, receive lower weightings. This stored matrix serves as the basis for weighting by the weighting unit and for quantifying applicant performance according to institution-specific priorities.

[0042] In one embodiment, the data acquisition unit electronically captures candidates' responses to proprietary measurement instruments, such as the generative self-assessment questionnaires described in SIGES. The generative self-assessment mechanism captures expressive behavior through a series of situational questions mapped to generative indicators such as applied creativity, mentoring, shared responsibility, societal impact, institutional legacy, and ethical consistency. Each item is encoded in a machine-readable format containing an indicator identifier, descriptive content, response value, and scale reference. The feature extraction unit applies semantic analysis, question-context matching, and indicator-based classification to transform these responses into structured feature vectors.These are then processed using weighting coefficients, normalization functions, and reliability factors.

[0043] In another embodiment, the system integrates structured interview assessments according to SIGES, whereby interview transcripts and observation protocols are algorithmically evaluated to extract generative indicators relating to knowledge transfer, innovative leadership, resilience, social engagement, authenticity and predicted impact.

[0044] The invention enables the coding of open-ended interview responses into segmented text objects with metadata fields that represent the situational context, narrative depth, indications of mentoring, cooperative behavior, emotional tone, and outcome relevance. These coded representations are processed to generate feature vectors at the dimension level, which are linked to corresponding datasets in the evidence assessment unit. The quantitative assessment unit then calculates partial scores for interview-based indicators using domain-specific weightings, thus making the interview a significant contributor to the aggregated generative assessment result.

[0045] In embodiments, the invention implements digital representations of structured assessment instruments, such as the generative assessment rubric, the concrete evidence sheet, and the authenticity and projection verification described in SIGES. The rubric defines weighted assessment criteria aligned with indicators such as teaching, mentoring, innovative leadership, resilience, social engagement, ethical integrity, and projected legacy, with each criterion assigned a numerical scale. The system converts rubric-based assessment events into numerical feature vectors and aggregates these feature vectors according to the indicator weighting, the reliability of the source, and dimensional coherence.Similarly, documents uploaded by applicants, including project reports, witness statements, institutional acknowledgments, and social products, are collected, classified, and compared with the narrative statements. The generated verification scores modify the confidence and dimensional weighting of the relevant characteristics.

[0046] In one embodiment, the quantitative assessment unit performs an integrated calculation process that combines the weighted scores from the interview guide, assessment grid, evidence sheet, and proof of authenticity according to predefined weights (e.g., 40%, 30%, 20%, and 10%, respectively), resulting in a weighted total score of 100 points (according to SIGES). The calculated total score is normalized and converted into a generative assessment score. This score is then interpreted using threshold models that categorize candidates into performance levels, such as established generative style, advanced generative style, generative style in development, initial development, or insufficient generative evidence.This classification is performed by the classification unit using calculation thresholds stored in memory and results in structured classification outputs that are digitally encoded by the decision unit.

[0047] In addition to calculating the score and classifying the system, the system according to the invention supports structured decision-making that corresponds to the final phase of the SIGES procedure. Here, quantitative results, verification results, and observational data are combined to generate actionable institutional recommendations. The decision generation unit creates a machine-readable decision record containing the applicant's classification level, dimension-level summaries, evidence validation results, and institutional recommendations. These recommendations may include promotion, leadership development, conditional integration, or disqualification. The decision record is digitally signed and stored in non-volatile memory, thus ensuring traceability and regulatory compliance.

[0048] In some implementations, the system also performs consistency checks of the assessments by triangulating self-assessments, interview responses, and documents, as described in SIGES. The system calculates agreement at the measurement level and identifies anomalies where statements lack evidence or inconsistencies occur between the different phases. These anomalies can affect the confidence coefficients, which may reduce the weighting of the dimensions or trigger alerts stored in the decision log. Furthermore, the system supports longitudinal institutional analyses, in which aggregated results across different applicant groups are analyzed to identify recurring patterns in generative potential and institutional alignment.

[0049] By integrating SIGES into a hardware-based computing architecture, the invention establishes a novel digital mechanism for identifying, quantifying, and operationalizing the generative human value within applicant pools. The resulting system transforms qualitative human expressions into structured, verifiable data artifacts and generates context-sensitive classification results that enable evidence-based and socially responsible talent decisions. The system is implemented as a hardware-based computing architecture in which each of the claimed units corresponds to discrete physical processing components, storage modules, and electronic interfaces configured to perform the specified operations. The data acquisition unit consists of hardware circuits, network interface controllers, and input buffers that electronically receive applicant data and store it in machine-readable form.The feature extraction unit, weighting calculation unit, quantitative evaluation unit, evidence verification unit, and classification unit are implemented using dedicated processing hardware, including processors, accelerators, addressable memory, and firmware-encoded instruction sets that perform the necessary computational transformations on the data streams represented in electronic memory. The decision generation unit is realized through digital hardware subsystems configured to synthesize output datasets, apply cryptographic signatures with hardware-based key storage, and store the encoded results on non-volatile storage media such as SSDs, flash memory, or secure hardware modules.A system control unit implemented as a processor-controlled hardware scheduler connects all modules via electrical buses and communication channels, controlling real-time orchestration, state transitions, resource allocation, and event logging in a persistent storage subsystem. This hardware configuration ensures that all functionality is based on physical calculations performed by electronic circuits, rather than on abstract mental processes or intangible administrative constructs. This guarantees that the system is anchored in tangible machine components that operate with electronic data signals.

[0050] The invention can be understood as an integrated computational method that is executed in the units defined in the claims for data acquisition, feature extraction, weighting calculation, quantitative evaluation, verification, classification, decision-making, and system control. Each of these units is implemented as a set of processor-executable instructions and associated data structures stored in one or more digital memories. The method begins with a continuous data acquisition phase in which the data acquisition unit receives heterogeneous inputs from applicants. These include structured academic records, descriptions of professional experience, digital CV files, structured responses to proprietary questionnaires such as generative self-assessments, and unstructured narrative responses from interviews and contextualized situational exercises.The system creates a unified digital representation of these inputs by mapping each incoming item to an internal schema consisting of applicant identifier, data source category, document type, timestamps, and origin attributes. Structured datasets such as degrees, job titles, and certifications are mapped to normalized fields, while narrative data is stored as variable-length strings with additional references to its original capture context, such as a self-assessment questionnaire, an interview question, or an evidence narrative.

[0051] Once the data are captured and stored in this unified representation, the feature extraction unit applies natural language processing pipelines to the unstructured segments. The process performs tokenization, sentence boundary detection, and syntactic analysis to transform text into a sequence of token objects enriched with word class tags, dependency relationships, and semantic role labels. On this representation, a semantic classification process categorizes expressions associated with generative dimensions such as applied creativity, mentoring, networking, social impact, ethical consistency, collaborative resilience, and institutional legacy. These dimensions have been formalized in the Comprehensive Generative Evaluation and Selection System (SIGES) profile definition matrix.For example, if an applicant's description includes continuous guidance of colleagues, transfer of practical knowledge, and monitoring of progress, the process assigns this segment to the mentoring dimension and links it to relevant indicators such as training others and caring for others. The feature extraction unit then creates feature vectors, with each vector containing attributes such as dimension identifier, sub-criterion, linguistic polarity, intensity, contextual emphasis, and relational meaning.

[0052] In parallel, structured data is transformed into feature vectors using deterministic mapping methods. Academic degrees, certifications, and documented professional experience are coded as features for technical knowledge and professional experience, each with level descriptions, area of ​​expertise, and duration. These structured features are linked to the same generative profile definition matrix but primarily contribute to the basic technical dimensions. All feature vectors, regardless of whether they originate from narrative or structured sources, are stored in a multidimensional feature repository indexed by applicant ID, dimension, and evidence link.The method uses this memory to calculate relationships between different dimensions and thus recognizes, for example, that a narrative about innovative project management matches documented project reports or institutional recognitions in the structured dataset.

[0053] The weighting calculation unit processes these stored feature vectors using a generative profile definition matrix, which is managed as a hierarchical digital structure. For each generative dimension, the matrix encodes a set of sub-criteria, indicators, expected level descriptions, evidence requirements, and weighting coefficients that represent the relative importance within the global generative assessment model. In many institutional configurations, dimensions such as tradition, mentoring, innovation, and community engagement are given greater weight than purely technical knowledge. For each feature vector, the procedure consults the matrix to retrieve the associated dimension and indicator definition and calculates a weighted contribution score by combining a raw feature strength derived from semantic intensity or quantitative measurement with the dimension weighting coefficient.Raw feature strength can be obtained, for example, from Likert scale self-assessment responses, from category ratings of interview segments, or from automatically calculated relevance scores resulting from the prominence and specificity of the narrative content.

[0054] To ensure the comparability of features from different sources, the weighting unit performs normalization operations, mapping the raw values ​​to a standardized internal scale. The process also aggregates multiple features of the same indicator and dimension and applies internal logic to balance frequency and depth. This prevents repeated but superficial mentions from obscuring fewer but more meaningful expressions of generative behavior. The unit assigns confidence indicators to each weighted feature, reflecting the reliability of the underlying data source, the clarity of the semantic classification, and the presence or absence of corroborating evidence.The resulting set of weighted and normalized feature vectors thus represents a structured, multidimensional representation of the applicant's generative profile in machine-readable form.

[0055] The quantitative assessment unit receives the weighted feature vectors and performs multidimensional aggregation to calculate a generative score. The process groups features according to dimensions such as innovation, mentoring, tradition, community engagement, values ​​and attitudes, collaborations, and technological infrastructure. A dimension score is calculated for each dimension by summing or otherwise combining the weighted contributions of the respective indicators. Adjustment factors are applied that take into account internal consistency, coverage, and agreement between different sources. For example, if both self-assessments and interview statements show strong and consistent evidence of community engagement, further supported by documented social projects, the process increases the strength of the assessment and stabilizes the dimension score.If, however, the information in the self-assessment is not confirmed by the interview performance or documents, the procedure weakens the contribution of these characteristics using a consistency-based correction factor.

[0056] The quantitative assessment unit models interdimensional relationships by constructing a graph of dimensional interactions. The nodes represent generative dimensions, while the edges represent reinforcing or balancing relationships. For example, innovation and mentoring can be linked as mutually supportive dimensions, while the technological base acts as a supporting dimension that amplifies the impact of innovation if it is sufficiently present. Using this graph, the method calculates an interaction-adjusted generative assessment score by applying cross-dimensional multipliers or constraints. This allows the system to distinguish between an innovative but isolated candidate and an innovative candidate who is simultaneously involved in mentoring and community engagement. The latter exhibits greater generative potential.

[0057] In addition to aggregation at the dimension level, the quantitative assessment unit integrates results from various assessment blocks, such as the generative self-assessment, the structured interview guide, the evidence sheet, and the authenticity and projection protocol (as described in the SIGES process). Each block is assigned a weight that defines its relative influence on the final assessment. The procedure calculates block-specific sub-scores and then combines these, according to the weights, into a global generative assessment score. It also assesses information triangulation by measuring the correlation between the blocks.A high degree of coherence between self-perception, reported experiences, proven results, and observed authenticity leads to a higher confidence score for the overall score, while discrepancies trigger adjustments that reduce the overall rating or flag the profile for further review. The final generative score is then stored in the system memory, assigned to the applicant's identifier, and accompanied by metadata describing how the score was determined, including the key contributing dimensions and indicators.

[0058] The evidence verification unit works in parallel with these calculations to validate the authenticity and relevance of the referenced evidence. For each piece of evidence, such as documents relating to completed projects, case studies, institutional accreditations, or products like guidelines and protocols, the system extracts metadata such as authorship, source, temporal context, and the type of impact described. The system then applies content analysis techniques, such as keyword extraction, semantic similarity measurement, and structural comparison, to link this evidence to specific indicators and dimensions in the feature vectors.

[0059] It checks the consistency between statements in the text sections and the specific information in the supporting documents, such as project scope, community reach, or measurable results. In cases of strong consistency, the document verification unit marks the corresponding generative attributes as externally confirmed, thus increasing their reliability level. If discrepancies or missing documents are detected, the system reduces the reliability associated with these attributes and, if necessary, adds a verification note to the profile. All verification processes are logged with timestamps, source identifiers, and verification results, creating a traceable validation history.

[0060] The classification unit uses the assessment results of generative performance and the associated confidence indicators, assigning them to predefined performance categories. These categories are coded as a series of thresholds representing lower, middle, and upper levels of performance. Each level is characterized by multidimensional boundary descriptions. For example, one level might correspond to an operational or developing generative profile, another to an advanced profile with continuous mentoring and innovation, and a higher level to established generative maturity with demonstrable tradition and institutional perspective—analogous to the higher-level descriptions in the SIGES interpretation matrix. The procedure compares the applicant's normalized assessment score with these thresholds, taking into account dimension coverage and confidence attributes.In borderline cases where the score is close to a threshold, the unit may apply additional conditions, such as requiring a minimum score in core dimensions like tradition or mentoring for the top performance level. This ensures that the classification reflects a comprehensive, rather than an incomplete, generative profile. The result of this process is a classification level that encodes the applicant's generative maturity in a machine-readable label.

[0061] The decision generation unit then creates a digital decision record that summarizes the calculated classification, dimension-level summaries, key indicators, verification results, and recommended organizational actions. The process populates a structured decision template with fields for the candidate ID, the generative classification label, key strengths, areas for development, evidence compliance status, and system-generated recommendations. These recommendations might include, for example, suitability for immediate promotion, participation in leadership development programs, eligibility for conditional hiring, or recommendations for further mentoring prior to final selection. The record is encoded in a digitally signed, tamper-proof format that includes metadata on chronology, reviewer authorization fields, and version attributes.The signature process can use cryptographic operations with keys stored in secure hardware or software containers, ensuring that any changes to the data set can be detected and traced. The decision generation unit stores this encoded data set in non-volatile memory, where it becomes part of the institution's evaluation archive.

[0062] Throughout the entire technical workflow, the system control unit orchestrates state transitions and ensures operational control. It defines a series of processing states for data acquisition, feature extraction, weighting calculation, quantitative evaluation, verification, classification, and decision-making, and maintains a persistent representation of these states with identifiers, dependencies, and completion flags. Upon acquisition of a new applicant record, the control unit initializes a process instance and sequences the execution of the units according to a preconfigured workflow. It handles scheduling, resource allocation, and exception management. For each executed step, the control unit logs an event record containing a timestamp, unit identifier, input and output references, execution status, and any detected anomalies. In the event of an error or inconsistency, such as...For example, in the case of missing verification data or contradictory classification results, the control unit can trigger the re-execution of certain units, initiate a manual review, or adjust the processing parameters according to institutional guidelines.

[0063] By storing these calculation logs, the system enables the retrospective reconstruction of how a specific classification was generated. This supports subsequent analysis, optimization of dimension weighting, and continuous refinement of the matrix used to define the generative profile. Institutions can query the log database to analyze patterns in applicant groups, such as which dimensions most reliably identify high generative profiles or how the results of the verification process correlate with long-term institutional impact. The technical implementation thus not only enables real-time evaluation but also creates a feedback mechanism through which the generative assessment model can evolve and continuously align itself with the institution's strategic goals and observed results.

[0064] In this way, the detailed technique applied by the described units transforms heterogeneous applicant information into a coherent, validated and contextually aligned representation of generative value, thus enabling objective, transparent and comprehensible selection decisions in which innovation, mentoring, social contribution and legacy explicitly take precedence over purely technical indicators.

[0065] The invention is based on the integrated computer-aided modeling of structured and unstructured applicant information, including academic achievements, professional history, interview responses, and supporting documents. Structured data is encoded in digital datasets with metadata describing origin, category, and relevance. Unstructured text input is processed using token-based analysis and semantic segmentation to extract generative indicators such as mentoring behavior, creative leadership, social engagement, ethical integrity, and the future relevance of the institution.

[0066] Feature extraction transforms text-based attributes into multidimensional vector representations, with each vector containing relational meaning, contextual emphasis, semantic polarity, and dimensional category identifiers. The analyzed segments preserve implicit meaning through digitally encoded metadata that describes the linguistic origin and contextual classification. The extracted features are stored in vector memory, enabling the modeling of interactions across multiple dimensions as well as evaluative computations.

[0067] The dimensions are weighted using a generative profile definition matrix, which is stored as a persistent digital structure and represents assigned weights, importance coefficients, verification requirements, and relationships at the dimension level. The matrix reflects generative priority dimensions such as innovation, mentoring, legacy, and social engagement, which were particularly emphasized in SIGES. The system calculates weighted representations without altering the vector structure, thus enabling scalable matrix substitution without reprocessing the source data.

[0068] The quantitative assessment aggregates weighted characteristics using normalization, correction of contextual biases, and conflict resolution procedures. The architecture models interdimensional relationships by calculating associative multipliers, uncertainty coefficients, and confidence indicators. The resulting generative assessment score represents a composite, nonlinear evaluation of performance potential, relationship behavior, and institutional value impact.

[0069] Evidence verification is achieved through electronic correlation of digital documents with alleged behaviors, metadata extraction, and semantic match analysis. Verification techniques link evidence with extracted features to determine the authenticity and validity of generative claims. The evidence is stored with traceable identifiers, timestamps, and relational indices that enable forensic reconstruction.

[0070] Decision generation creates a structured, digitally signed dataset containing classification identifiers, aggregated feature summaries, evidence validation results, and recommended organizational actions. The datasets are encoded in traceable formats that embed chronological sequences, authorization status, and change history to ensure auditability and regulatory compliance.

[0071] System control processes manage state identifiers, event logs, descriptions of the operating context, and dependency structures between units, thus enabling synchronized processing, fault tolerance, and transparent traceability.

[0072] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0073] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A computer-aided system for multidimensional generative value assessment and applicant selection. 102 Data acquisition unit 104 Feature extraction unit 106 Weighting calculation unit 108 Quantitative unit of assessment 110 Evidence Examination Unit 112 Classification unit 114 Decision generation unit 116 System control unit

Claims

[1] A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes. [2] System according to claim 1, wherein the data acquisition unit comprises a configurable data interface architecture that includes structured input buffers for academic and professional datasets as well as adaptive narrative data structures of variable length for unstructured interview responses, such that the captured information is stored in a unified digital representation that preserves the applicant-specific semantic context, relational dependencies and metadata attributes including provenance, input source category and document type indicators, thereby enabling subsequent computer-assisted processing that maintains the contextual integrity of the generative indicators disclosed by the applicant. [3] System according to claim 1, wherein the feature extraction unit is configured such that extracted features are encoded as vectorized digital representations comprising fields for relational meaning, contextual emphasis, behavioral orientation and semantic polarity, wherein each representation is stored as a multidimensional fixed-length or dynamically allocated data structure and includes additional data attributes describing the origin of the extracted feature, the linguistic origin segment and a category identifier corresponding to a generative dimension associated with innovation, mentoring, ethical consistency, social contribution, resilience or other generative indicators derived from structured and unstructured inputs disclosed in the application documents. [4] System according to claim 1, wherein the weighting calculation unit stores the definition matrix of the generative profile in a persistent digital structure containing hierarchical dimension identifiers, mappings between generative indicators and descriptors of organizational relevance, metadata descriptors expressing qualitative importance, and machine-readable references to evidence requirements, such that the stored matrix enables the calculation of weighted representations of extracted generative indicators according to institution-specific importance assignments without changing the structural representation of the underlying feature vectors. [5] System according to claim 1, wherein the quantitative evaluation unit comprises a multidimensional scoring aggregation architecture configured to calculate composite generative values ​​by integrating weighted feature representations stored in system memory, wherein the scoring architecture includes data structures representing interdimensional associations, contextual relevance weights, semantic uncertainty identifiers and attribute confidence indicators, such that a consolidated, system-generated generative evaluation value reflects complex relational interactions between technical, behavioral and institutional impact dimensions within the applicant profile. [6] System according to claim 1, wherein the evidence verification unit comprises a digital validation architecture configured to maintain a machine-readable representation of reference evidence and associated narrative statements, wherein this representation includes metadata fields for source identification, temporal markers, authorship attributes, semantic alignment descriptors and indicators of confirmation consistency, such that each piece of evidence can be computationally linked to extracted feature vectors to determine the structural agreement or divergence between specified generative actions and documented external confirmation. [7] System according to claim 1, wherein the classification determination unit stores in memory a series of computational classification thresholds represented as digital parameter limits which are linked to lower, medium and higher generative performance categories, wherein each limit is defined by a multi-attribute representation comprising context-specific sensitivity indicators, dimensional significance coefficients and rating confidence attributes, such that the classification of an applicant is determined by comparing a consolidated generative rating score with the limit representations encoded in the system memory. [8] System according to claim 1, wherein the decision generation unit stores digital representations of applicant evaluations as structured, machine-readable data sets comprising fields for performance classification identifiers, aggregated generative attribute summaries, evidence verification results and interpretation metadata, wherein these data sets are encoded in a traceable digital format containing embedded identifiers for temporal sequencing, authorization status and change history, thereby enabling permanent traceability in the context of audits and institutional accountability for evaluation decisions. [9] System according to claim 1, wherein the system control unit manages a persistent state management representation comprising structured descriptions of operational phases associated with data acquisition, feature extraction, weighting calculation, quantitative evaluation, classification determination and decision generation, wherein this representation includes unique identifiers for each phase, dependency attributes, completion indicators and event origin information, such that system-wide synchronization and fault tolerance are maintained throughout the entire computation environment. [10] System according to claim 1, wherein the system control unit stores computing logs comprising time-related data records encoding operational results, execution status, interactions between units and system events, wherein each log entry contains metadata fields defining the temporal sequence, operational context, traceability identifiers and system state descriptions, such that the stored logs provide digital traceability enabling retrospective analysis, system optimization and audit-driven control of the multidimensional applicant evaluation framework.

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