Information processing methods

Abstract

In an information processing method that generates future states by placing indicators representing the state of intangible assets in a latent space, it enables counterfactual recalculation of past states, consideration of autoregressive effects due to the subject's own actions, and dynamic management of confidence levels based on user feedback to external modules. [Solution] The arrangement state of indicator 207 at a predetermined point in the past is stored as past state data 701, a portion of the external environmental parameters are replaced to regenerate the future state, and the regenerated future state is superimposed on the current future state 300 and displayed on the observation space 220. A market impact coefficient 711 is calculated for each candidate action of the subject, the transition parameters of the state transition model are corrected to generate a corrected future state 712 that reflects the autoregressive influence, and the presence or absence of the influence is displayed comparatively. Furthermore, feedback metadata 721 from multiple users to the output of the external module 40 is aggregated to calculate the feedback reflection confidence score 722 and reflected in the weighting of the output.

Description

[[Technical Field]]

[0001] The present invention relates to an information processing method for intangible assets owned or managed by an individual, a corporation, an organization or an association, which calculates an indicator based on a plurality of asset elements constituting the intangible asset and the binding state thereof, arranges the indicator in a multi-dimensional latent space, and enables exploration of temporal changes and branches of future states. The present invention also relates to: counterfactual recalculation, which calculates the state at a predetermined past time point under different external environment parameters; predictive control that takes into account autoregressive effects where preconditions for state transition are altered by the actions of the evaluation target subject itself; processing for dynamically calculating and updating the reliability of operation models or analysis modules provided from the outside based on feedback from a plurality of users; structuring of market potential and factorization of uncertainty; conflict resolution for outputs of a plurality of modules and cooperation with third-party data; snapshotting of evaluation states and metaphor-based visualization; as well as cost validity determination for individual countermeasures and deviation monitoring of set values. [[Background Art]]

[0002] In recent years, in corporate activities and the operation of various organizations, in addition to tangible assets such as equipment and funds, intangible assets including know-how, technical ideas, organizational capabilities, personal networks, customer relationships, and brand value are increasingly recognized as core elements of competitive advantage and value creation.

[0003] Rather than existing as a single element, these intangible assets often exhibit effective value only when a plurality of asset elements are mutually bound and function under a specific market environment or subject conditions.

[0004] Further, legal rights such as patent rights, utility model rights, design rights, and trademark rights, as well as systems such as contracts, trade secret management systems, and brand management, are positioned as means for protecting the value of intangible assets or controlling access and use by third parties. Although these protection means have different properties from the intangible assets themselves, they can function as elements that directly affect the market value, competitive position, and risk structure of intangible assets.

[0005] (Prior art concerning latent space and observation space) In the field of machine learning, methods have been proposed to encode high-dimensional data into a low-dimensional latent space and predict future states within that latent space (see Patent Document 1 and Non-Patent Document 1). Furthermore, in state-space models, a structure has been formulated in which a portion of the state space is selectively observed by an observation matrix (selection matrix) that defines a mapping from state vectors to observation vectors (see Non-Patent Document 2).

[0006] However, many of these prior arts deal with data of a single modality, such as images, audio, and natural language. Technologies for comprehensively representing intangible assets—where multiple heterogeneous asset elements (legal rights, know-how, brand value, etc.) combine to form value—in latent space are not yet fully established. Furthermore, technical considerations from the perspective of presenting the state in a format that users can intuitively understand during the conversion from latent space to observed space are limited.

[0007] (Prior art concerning the combined state of multiple elements) In the field of corporate or organizational evaluation, a method has been proposed in which potential factors are extracted from multiple evaluation indicators through factor analysis, the correspondence between these factors and the evaluation indicators is calculated as factor loadings, and an overall evaluation indicator is calculated using the extracted factors (see Patent Document 2). In this method, the correspondence between evaluation indicators and factors is not limited to one-to-one relationships, but is treated as a many-to-many relationship where multiple evaluation indicators contribute to a single factor, or a single evaluation indicator contributes to multiple factors.

[0008] Furthermore, in the field of decision support, a method has been proposed that estimates a multi-attribute utility function from the preference structure for multiple attributes and aggregates the multiple attributes into a single utility value using this utility function (see Patent Document 3). This method provides a technical foundation for quantifying the combined state of multiple elements in a form that can be calculated by weighted aggregation operations.

[0009] However, much of this prior art deals with static evaluations at a specific point in time, and there is a lack of established technology to continuously track and display the dynamic structure in which the combined state of multiple asset elements changes over time, in response to changes in the external environment, or in response to the actions of the entities. In particular, with intangible assets, the combined state can change discontinuously due to the expiration of legal rights, obsolescence of technology, changes in the market environment, the entry of competitors, etc., but there are limited technologies that can handle dynamic structures including such discontinuous changes.

[0010] (Prior art relating to future state prediction and branching scenarios) In the field of time series analysis, numerous methods have been proposed to predict future states based on past observational data. Furthermore, in the field of scenario planning, methods are known that qualitatively describe multiple future scenarios (future states) and use them as a basis for strategic planning.

[0011] However, when predicting the future state of intangible assets, numerous external factors such as market conditions, competitive landscape, technological trends, and legal regulations have a complex interplay of influences. Therefore, it is desirable to present the future state as a whole, encompassing multiple branches, rather than as a single predicted value. In prior art, there is no well-established technology that visually presents such a future state with multiple branches, along with its probability of occurrence, and allows users to explore the details of each branch.

[0012] Furthermore, there are limited technologies that, when generating future states, set branching conditions based on similarities with past cases (including success and failure cases) and make it possible to explain to the user the reason why such a branch was generated.

[0013] (Prior art concerning counterfactual recalculations of past states) In the field of counterfactual analysis in dynamic latent state models, methods have been proposed to estimate results under different assumptions by sequentially applying hypothetical operations to past state transitions (see Non-Patent Literature 3). This method formulates a three-stage procedure: an abduction step of estimating the latent state from observed data, an action step of applying hypothetical operations to the latent state, and a prediction step of predicting the future state from the state after the operation.

[0014] However, prior art for these counterfactual analyses is primarily discussed in the context of explainability or causal inference of machine learning models, and a series of configurations for valuing intangible assets that involves retaining the configuration of indicators at a given point in the past in a reusable format as input to a state transition model, selectively substituting at least some of the external environmental parameters for the past state data, regenerating the future state under the substituted parameters, and displaying the regenerated future state superimposed on the current future state in the observation space, are not yet well established. In particular, there are no explicit techniques for directly visualizing in the observation space, in comparison with the actual results, what future states could have occurred if different market environments or external conditions existed at the past decision point in time.

[0015] (Prior art concerning autoregressive effects caused by the actions of the subject) In the field of financial engineering, it is known that when large-scale trading entities execute a certain amount of transactions in the market, these transactions influence the formation of market prices, altering the price levels that are the basis for predictions. Methods have been proposed to quantify this effect as market impact and derive optimal execution strategies (see Non-Patent Literature 4). In these methods, a market impact model with two components, temporary impact and permanent impact, is formulated, and a framework for optimizing the trade-off between execution costs and market risk is presented.

[0016] However, these market impact models primarily focus on execution transactions of financial assets such as stocks, and their application to state transition models of intangible assets is not disclosed. Furthermore, the prior art does not explicitly describe a configuration in which the subject being evaluated calculates a coefficient (market impact coefficient) representing the magnitude of the impact that each action has on the preconditions for state transitions, corrects the transition parameters of the state transition model based on this coefficient, generates a future state that reflects the autoregressive effect of the subject's own actions changing the preconditions, and displays the future state that reflects this autoregressive effect and the future state that does not reflect it in a comparative manner on the observation space.

[0017] (Prior technologies related to external module integration) In the field of decision management, there is a known technology that integrates modules beneficial to decision-making, such as predictive models, optimization algorithms, business rules, decision tables, and scorecards, as services on a platform, and structures these modules using industry-standard formats such as PMML (Predictive Model Markup Language), XML, and JSON (see Patent Document 4). Such technology provides a foundation that allows externally provided modules to be called without disclosing their internal implementation.

[0018] Furthermore, in cloud-based decision-making platforms, technologies have been proposed to add decision-making-enhancing modules provided by third parties to the platform and integrate these modules into analytics-driven applications.

[0019] However, these prior arts primarily focus on routine decision-making processes such as credit assessment, fraud detection, and marketing optimization. Their application to non-routine valuations, such as the valuation of intangible assets, which require consideration of diverse factors including the combined state of multiple asset elements, the attributes of the valuation body, and the market environment, has not been adequately explored. In particular, the configuration of integrating the output obtained from external modules as indicators in a latent space and using them to generate future states is not explicitly stated in the prior art.

[0020] (Prior technology regarding the calculation of reliability of external modules) In the field of reliability evaluation using crowdsourcing, a technology has been proposed that aggregates reliability indicators collected from multiple evaluators to calculate a reliability score for an entity, and dynamically updates this score each time new evaluation data is acquired (see Patent Document 7). This technology discloses a configuration in which reliability indicators obtained from multiple data sources are weighted and integrated to calculate a peer evaluation score.

[0021] However, the crowdsourced trust score calculation technology in question is used to evaluate the trustworthiness of entities such as individuals or organizations, and is not applicable to the output quality of computational models or analysis modules used by information processing systems. Furthermore, the sequence of events in which the reliability of each computational model or analysis module (i.e., mathematical model) is calculated and updated by acquiring and aggregating feedback metadata representing evaluation behavior by multiple users for the calculation of indicators or generation of future states using the output of a computational model or analysis module, and the updated reliability is reflected in the weighting of the output of the computational model or analysis module, is not explicitly stated in the prior art.

[0022] (Prior technologies related to conflict resolution of multiple module outputs and third-party data integration) Although ensemble methods for integrating the outputs of a plurality of models or estimators are widely known in the field of machine learning, in the evaluation of intangible assets, when a plurality of external modules present mutually different outputs for the same input, the degree of inconsistency between said outputs is calculated using a predetermined index, a warning is issued when the inconsistency exceeds a threshold, and an integrated output is determined based on a consensus-building rule such as weighted average based on reliability, majority vote, conservative selection, or user judgment, such a technology has not been sufficiently established. Furthermore, a configuration in which data generated and provided by a third party is acquired via a predetermined interface, standardized into a unified data format, then quality verified from the viewpoints of completeness, timeliness, and consistency before being integrated into an evaluation system is also not explicitly disclosed in the prior art.

[0023] (Prior Art Related to Comparison of Evaluation Results and Uncertainty) In the field of metrology, methods for ensuring the reliability of measurement results have been established by evaluating the uncertainty of measurement results and specifying the measurement resolution and measurement range. Furthermore, in the field of statistics, methods of assigning confidence intervals to estimated values and quantitatively expressing the uncertainty of estimation are widely used.

[0024] However, in the evaluation of intangible assets, the accuracy and reliability of evaluation results can vary greatly depending on the nature of the evaluation target, the selection of evaluation methods, the attributes of the evaluation entity, and the like. In the prior art, a technology that specifies such uncertainty of evaluation results using metrological concepts such as measurement resolution, measurement range, and error bands, and treats differences smaller than the measurement resolution as unidentifiable (e.g., equalization in display, ranking as the same), has not been sufficiently established in the field of intangible asset evaluation.

[0025] In particular, in the case of comparing a plurality of future states, a technology that determines the significance of a difference with a branch point as a reference and highlights only differences equal to or larger than the measurement resolution as significant differences is not explicitly disclosed in the prior art.

[0026] (Prior Art Related to Structuring Market Potential and Factor Decomposition of Uncertainty) Although methods for estimating market size and conducting market analysis are known in the art, in the valuation of intangible assets, there has not been sufficiently established a technique for structuring the size and structure of potential revenue opportunities in a target market as a vector quantity having a plurality of components including the number of potential customers, distribution structure, reachability and the like, and quantifying, as a mapping (coverage relation mapping), the degree of reachability that each asset element constituting an intangible asset has with respect to each component of said market potential. In addition, the prior art does not explicitly disclose a technique of decomposing uncertainty included in a future state into factors respectively derived from a source caused by the quality of input information, a model caused by the structure of a state transition model, and an environment caused by future fluctuations of external environmental parameters, and presenting countermeasures according to the contribution ratio of each factor.

[0027] (Prior art relating to acquisition and re-evaluation of external information) In event-driven information processing systems, a technique for detecting an external event (trigger) and executing predetermined processing in accordance with said event is widely used. In addition, in the field of real-time analysis, a technique of continuously monitoring changes in external data and updating analysis results when a change is detected is known.

[0028] However, in the valuation of intangible assets, the degree of influence (importance) that changes in the external environment (such as revision of laws and regulations, trends of competitors, technical trends, changes in market environment, etc.) exert on valuation results may vary depending on the nature of the valuation target, the attributes of the valuation subject, the purpose of valuation, and the like. In the prior art, there has not been sufficiently established a technique of calculating the importance of external trigger information, executing updating of indicators and regenerating a future state only when said importance satisfies a predetermined condition, and visually presenting the difference before and after updating.

[0029] (Prior art relating to reliability display by facial expression) In the field of conversational agents, there is a known technology that influences the trustworthiness or credibility perceived by users by switching the facial expressions (demeanors) of avatars (see Patent Document 5). This technology suggests that avatars with specific facial expressions, such as smiles, can gain higher trust from users compared to avatars with neutral facial expressions.

[0030] Furthermore, there is a known technique that generates facial expression parameters, including eye movements, mouth movements, and head posture, using text, audio, or video as input, and renders an avatar based on these parameters (see Patent Document 6). This technique extracts angle information that constitutes facial expressions from the landmark coordinates of the face and makes it possible to apply it to two-dimensional or three-dimensional character representations.

[0031] However, these prior arts are primarily intended for interactive agents or communication support, and are not yet well-established as technologies that express the degree of deviation from standards or the application of double standards as facial expressions in the valuation of intangible assets, visualization of audit value, or assessment of credit risk between entities, thereby facilitating intuitive understanding by users.

[0032] (Prior technologies related to saving, comparing, sharing, and metaphorical visualization of evaluation status) Although technologies have been proposed to project and visualize the state of an object placed in a latent space onto an observation space, there is no established technology for managing static data (scenario snapshots) that store the placement state of indicators at a specific point in time, the content of future states, the values ​​of external environmental parameters, and related evaluation information as a single entity using a unique identifier, and for displaying the differences between two or more such static data sets in parallel on the observation space, or for sharing them with external parties after setting the scope of disclosure. Furthermore, technologies for converting abstract states in latent space into a three-dimensional metaphorical space using physical attributes familiar to users, such as terrain, buildings, and defensive postures, and dynamically updating the metaphorical representation in response to changes in the state, are not explicitly described in prior art.

[0033] (Prior technologies concerning the assessment of the appropriateness of countermeasure costs and monitoring of deviations from set values) While methods for macroeconomically calculating the total amount of protective investment for intangible assets can be considered, there is no established technology for microeconomically determining the cost appropriateness of individual protective actions (such as patent applications, trademark registrations, strengthening confidentiality systems, licensing agreements, litigation, and participation in standardization activities) by comparing the cost required to implement such actions with the expected benefit calculated as the difference in future state resulting from the implementation of such actions, and issuing a warning if the ratio of the two exceeds a predetermined threshold. Furthermore, prior art does not explicitly describe a technology that calculates the degree of deviation between values ​​set by users in each process and statistical standard values ​​generally adopted in similar evaluations, visualizes this deviation in the observation space, allows for objective verification of the validity of the set values, and discloses this deviation information externally as audit information.

[0034] (Summary of prior art) As described above, intangible assets, their combinations, protective measures, and the surrounding market environment and external conditions change dynamically in response to the passage of time, the actions of stakeholders, institutional changes, technological advancements, etc. Therefore, it is difficult to fully understand future value fluctuations, risks, and opportunities by merely grasping the state of intangible assets as a static list or single indicator.

[0035] Traditionally, patent maps, IP landscapes, technology roadmaps, financial indicator analysis, and market analysis reports have been widely used as methods for identifying and analyzing intangible assets. These methods have a certain degree of usefulness in visualizing the distribution of intangible assets and market conditions at a specific point in time.

[0036] However, much of the prior art has limitations in the following respects. Firstly, it is difficult to comprehensively address, within the same framework, the interconnectedness of the multiple elements constituting intangible assets, their relationship with protective measures, and the relativity of their value depending on the valuation body and market conditions. Secondly, it lacks sufficient functionality to systematically explore and compare state transitions along a timeline and multiple branching scenarios for the future. Thirdly, it is difficult to preserve the state at a given point in the past in a reusable format, regenerate the future state by substituting at least some of the external environmental parameters with different values, and directly visualize it in the observation space in comparison with the actual results. Fourthly, it is difficult to present a comparative view of the differences in future states depending on whether or not the actions of the subject being evaluated have an autoregressive effect that alters the preconditions for state transitions. Fifth, although future prediction technologies using machine learning models or generative models have been proposed, their internal structures tend to be black boxes, making it difficult for users to understand the impact of differences in evaluation criteria and assumptions on the results. Sixth, technologies for integrating externally provided mathematical models (external modules) for the evaluation of intangible assets and the generation of future states, particularly technologies for dynamically calculating and updating the reliability of such mathematical models based on feedback from multiple users and reflecting this in the weighting of the output, and technologies for resolving output conflicts between multiple mathematical models using consensus rules, are not yet sufficiently established. Seventh, in comparing evaluation results, techniques for applying metrological concepts such as measurement resolution, measurement range, and error band to improve the robustness, reproducibility, and accountability of judgments have not been sufficiently established. Eighth, there is a lack of established technology for decomposing uncertainties included in future states (future scenarios) based on their sources, or for structuring market potential to visualize the market reachability of intangible assets. Ninthly, technologies that dynamically update evaluation results in response to changes in the external environment and visually present the differences before and after the update to support user decision-making are not yet sufficiently established. Tenthly, technologies for saving, comparing, and sharing evaluation states as static data, or for converting states in latent space into three-dimensional metaphorical representations suitable for intuitive understanding, are not yet sufficiently established. Eleventh, there is insufficient technology to determine the appropriateness of individual countermeasures at a micro level by comparing costs with expected benefits, or to support accountability by monitoring deviations between set values ​​and statistical standard values.

[0037] In particular, technologies that explicitly handle variations in evaluation results due to differences in the attributes and normative standards of the evaluators, and that provide these in a way that users can intuitively operate and understand, are not yet well-established. [Prior art documents] [Patent Documents]

[0038] [Patent Document 1] U.S. Patent No. 11995528 [Patent Document 2] International Publication No. 2006 / 004131 [Patent Document 3] U.S. Patent No. 7392231 [Patent Document 4] U.S. Patent No. 10620944 [Patent Document 5] U.S. Patent Application Publication No. 2013 / 0266925 [Patent Document 6] U.S. Patent No. 9898849 [Patent Document 7] U.S. Patent No. 9438619 [Non-patent literature]

[0039] [Non-Patent Document 1] DP Kingma, M. Welling, "Auto-Encoding Variational Bayes", arXiv:1312.6114, 2013 [Non-Patent Document 2] H. Tanizaki, "Nonlinear Filters: Estimation and Applications", Chapter 1: State-Space Model in Linear Case, Springer-Verlag, 1996 [Non-Patent Document 3] M. Haugh, R. Singal, "Counterfactual Analysis in Dynamic Latent State Models", Proceedings of the 40th International Conference on Machine Learning (PMLR 202), 2023, pp.12686-12706 [Non-Patent Document 4] R. Almgren, N. Chriss, "Optimal Execution of Portfolio Transactions", Journal of Risk, Vol.3, No.2, 2001, pp.5-39 [Overview of the project] [Problems that the invention aims to solve]

[0040] The present invention aims to solve at least some or all of the following problems. (a) It is difficult to grasp the multiple asset elements that constitute an intangible asset, their combined state, and the relationship between protective measures and the market environment, not as a static representation at a single point in time, but as a dynamic state that includes subject dependence, time changes, and uncertainty. (b) Despite the fact that evaluation results for the same subject may vary due to differences in the attributes of the evaluator, normative standards, or purpose of use, it is difficult to explicitly address the impact of such differences on the results and present them in a way that users can intuitively understand and manipulate. (c) It is difficult to explore and compare future states as multiple branching and uncertain scenarios. (d) It is difficult to retain the state at a given point in the past in a format that can be reused as input to a state transition model, to regenerate the future state by substituting at least some of the external environmental parameters with different values, and to directly visualize it in the observation space in comparison with the results that actually occurred. (e) It is difficult to generate future states while considering the autoregressive effect, in which the preconditions for state transitions change as a result of the actions of the subject being evaluated, and to present the differences in future states with or without such autoregressive effect in a comparative manner on the observation space. (f) The methods for calculating indicators, state transition models, or evaluation models tend to be fixed, making it difficult to flexibly replace or adjust them according to differences in application, purpose, and evaluation perspective. In particular, it is difficult to integrate mathematical models provided from external sources into the evaluation of intangible assets and the generation of future states without depending on their internal implementation. (g) With respect to mathematical models provided from external sources, it is difficult to dynamically calculate and update the confidence level based on feedback representing evaluation behavior by multiple users using the output, to reflect the confidence level in the weighting of the output, and to detect inconsistencies when multiple mathematical models present different outputs and integrate them using consensus rules. (h) Regarding the evaluation results of distance or similarity used for comparison or ranking, the handling of measurement resolution, measurement range, or error may become ambiguous, potentially reducing the robustness of judgment, accountability, or reproducibility, and it may be difficult to determine the significance of differences based on branching points and to clearly indicate failure transition patterns. (i) It is difficult to structure the size and structure of potential revenue opportunities in the target market as vector quantities, to visualize the market reachability of each asset element of the intangible asset, and to decompose the uncertainties contained in future conditions based on their sources and propose countermeasures for each factor. (j) It is difficult to calculate the importance of external trigger information that indicates changes in the external environment, update the indicator and regenerate the future state only when the importance meets predetermined conditions, and visually present the difference before and after the update. (k) It is difficult to express the degree of deviation from the standard or the application of double standards as a demeanor, while ensuring transparency through disclosure control of judgment path attributes, third-party audits, and tamper detection, and thereby promoting intuitive understanding by users. (l) It is difficult to compare and share static data that stores the arrangement of indicators at a specific point in time, the content of future states, the values ​​of external environmental parameters, and related evaluation information as a whole, and it is difficult to convert abstract states in latent space into three-dimensional metaphorical representations that are suitable for intuitive understanding, such as topography, buildings, and defensive postures. (m) It is difficult to determine the appropriateness of individual countermeasures by comparing the costs required to implement them with the expected benefits from those countermeasures, and it is also difficult to monitor the deviation between user-defined values ​​and statistical standard values, and to disclose such deviation information to external parties as audit information. (n) The difficulty in optimizing the strategic defense posture for intangible assets by integrating multiple strategic evaluation axes such as market accessibility, counterparty suitability, protection investment limits, and gate strength.

[0041] The present invention aims to solve these problems and provide information processing technology that makes intangible assets, protective measures, and related markets intuitively understandable as dynamic states including evaluation entities and time axes, and enables the exploration and comparison of future states. [Means for solving the problem]

[0042] The information processing method according to the present invention acquires information on multiple asset elements constituting an intangible asset and their combined state, as well as rights or protective measures associated with the intangible asset and the market environment; calculates an index representing the state of the intangible asset and places it in a multidimensional latent space; projects the index onto a human-understandable observation space for visualization; observes state transitions along the time axis and generates future states based on a state transition model, presenting them as future states including multiple branches.

[0043] Here, latent space refers to a representation space inherent in an information processing entity (general-purpose computer, trained model, or information processing system including these), and is a multidimensional space that does not presuppose human perception. This concept is similar to the concept known as "latent space" in the field of machine learning (see Non-Patent Literature 1). Observation space refers to a subspace of state quantities in the latent space that are observed in a form understandable to the user. This concept is similar to the space of observation vectors defined by the measurement equation in a state space model, and has a structure in which a part of the state space is selectively observed by an observation matrix (selection matrix) that defines a mapping from state vectors to observation vectors (see Non-Patent Literature 2). A state transition model refers to a computational model that describes the temporal transitions of indicators in the latent space, and includes branch generation models based on similarity rates (Example 3), prediction models using external modules (Example 3(3-3)), etc.

[0044] Furthermore, the present invention allows evaluation, calculation, or analysis modules, which are mathematical models that can be replaced according to the purpose of use or evaluation perspective, to be referenced, used in combination with, or substituted from the above basic configuration, thereby improving the comparability, transparency, and scalability of the evaluation results.

[0045] Here, an evaluation, calculation, or analysis module provided from an external source (hereinafter referred to as an "external module") refers to a calculation processing function that is provided independently of the information processing system executing this information processing method and is called via a predetermined interface. The external module may be implemented as a service provided over a network (Web service, API, etc.), as a plugin, or as a combination thereof. Input and output to the external module may be performed using industry standard formats such as PMML (Predictive Model Markup Language), XML, JSON, or structured data formats based on a predetermined schema.

[0046] Specific examples of the aforementioned external modules may include prediction models (regression models, classification models, time series prediction models, etc.), optimization algorithms (linear programming, integer programming, constrained optimization, etc.), rule engines (business rules, decision tables, etc.), scoring models (credit scores, risk scores, etc.), or combinations thereof. This information processing method can utilize the external modules by sending input data and receiving output results without knowing the internal structure of the external modules (black box utilization).

[0047] Furthermore, the present invention may include at least the following configurations as configurations that can be added to the basic configuration. Each of these configurations may be implemented in combination with all or part of the basic configuration, or each configuration may be implemented independently. (1) Calibration of evaluations based on the attributes of the evaluating entity and normative standards, regression learning of the decision path and evaluation of predictability, evaluation based on the degree of deviation from the wording of promises or contracts, etc. and the actual effect (performance period, etc.), and identification of external observer models of third-party entities based on external observations and control of the learning intensity (loading). (2) A configuration that promotes intuitive understanding by users by expressing the degree of deviation from the standard or the application of double standards as a demeanor, including disclosure control of judgment path attributes, third-party audits by the auditing body, preservation of audit records, and ensuring transparency including tamper detection using digital signatures and hashes, and expressing the degree of deviation from the standard or the application of double standards as a demeanor. (3) Integration of publicly available intangible asset information (including not only success stories but also failure stories, and including feature representations embedded in trained models), and the assignment of metadata to such publicly available information, as well as the establishment of a rights management mechanism, rights trust, and consideration distribution based on actual usage of the publicly available information. (4) Multilayer evaluation models, stratified confidence levels, recording and reproducibility of evaluation history, generation of hierarchical explanations of evaluation results, and mutual learning among multiple evaluation systems (distillation learning, associative learning, etc.). (5) A configuration that introduces metrological concepts (measurement resolution, measurement range, error band) into distance evaluation and performs compatibility verification of external modules (scale consistency, resolution consistency, reproducibility, robustness, version consistency, etc.) and acceptance / rejection decisions. In the aforementioned compatibility verification, metadata provided by the external module (scope of application, accuracy information, version information, etc.) is referred to and its consistency with this information processing method is verified. (6) Display of difference comparisons based on branching points (significance display, expansion of stratified differences, explanation of difference factors, etc.), detection and explicit indication of failure transition patterns, decision support based on difference comparisons, detection of changes in intent, updating and re-evaluating the state based on the calculation of the importance of external trigger information, correction of evaluation functions based on user behavior, reflection of mutual influence between entities, and API output of collective states. (7) Market entry feasibility assessment, trade partner suitability assessment, calculation of protective investment limits based on expected future profits, and optimization of strategic defense lines based on gate strength correction factors and constrained optimization.

[0048] Furthermore, the information processing method according to the present invention may include a counterfactual virtual recalculation configuration that includes the steps of: holding the arrangement state of the indicators at a predetermined point in the past as past state data that can be reused as input to the state transition model; replacing at least a portion of the external environmental parameters at the predetermined point in time with current or virtual external environmental parameters in the past state data; regenerating an indicator corresponding to a future state based on the state transition model under the replaced external environmental parameters; and displaying the regenerated indicator on the observation space by superimposing it with the future state generated from the current arrangement state of the indicators.

[0049] Furthermore, the information processing method according to the present invention may include a predictive control configuration that takes autoregressive effects, comprising the steps of: setting candidate actions that an entity to which the index belongs can take; calculating a market impact coefficient for each of the candidate actions that represents the magnitude of the influence that action has on the preconditions for state transitions in the latent space; correcting the transition parameters of the state transition model based on the market impact coefficient; generating an index corresponding to a future state that reflects the autoregressive effects caused by the action based on the corrected state transition model; and displaying the future state that reflects the autoregressive effects and the future state that does not reflect the autoregressive effects in a comparative manner on the observation space.

[0050] In addition, the information processing method according to the present invention may include a user feedback-based confidence management configuration that includes: a step of executing at least a part of the state transition model or index generation step using an externally provided computation model or analysis module; a step of acquiring feedback metadata representing evaluation behavior by multiple users regarding the calculation of the index or the generation of the future state using the output of the computation model or analysis module; a step of aggregating the acquired feedback metadata and calculating or updating the confidence level for each computation model or analysis module; and a step of reflecting the updated confidence level in the weighting of the output of the computation model or analysis module in the calculation of the index or the generation of the future state.

[0051] The present invention may further include at least the following configurations as configurations that can be added to each of the above configurations. (8) A configuration that structures the size and structure of the potential revenue opportunities in the market targeted by intangible assets as a market potential vector having multiple components, and describes the reachability that each asset element has with respect to each component as a covering relation map, and a configuration that decomposes the uncertainty contained in future states into source-induced uncertainty due to the quality of input information, model-induced uncertainty due to the structure of the state transition model, and environment-induced uncertainty due to future fluctuations of external environmental parameters, and presents countermeasures according to the contribution rate of each factor. (9) A configuration that calculates the degree of inconsistency between outputs when multiple external modules present different outputs for the same input, issues an inconsistency alert if the inconsistency exceeds a threshold, and determines the integrated output based on consensus rules such as a weighted average based on confidence, majority vote, conservative selection, or user judgment, and a configuration that acquires data generated and provided by third parties through a predetermined interface, converts it into a standardized data format, verifies its quality from the standpoint of completeness, timeliness, and consistency, and integrates it into the evaluation system. (10) A configuration that manages static data (scenario snapshots) that store together the arrangement of indicators at a specific point in time, the content of future states, the values ​​of external environmental parameters, and related evaluation information using a unique identifier, and that displays two or more such static data in parallel on the observation space, highlighting the differences, or shares them with external parties after setting the scope of disclosure, and a configuration that converts the state on the latent space into a three-dimensional metaphor space based on metaphor projection rules including terrain representation, building representation and defense representation, and dynamically updates the metaphor representation in accordance with changes in the state. (11) A configuration that compares the cost of implementing an individual countermeasure with the expected amount of benefit calculated as the difference in future state resulting from the implementation of the countermeasure, and issues a cost overrun warning when the ratio of the two exceeds a predetermined threshold, thereby making a micro-level determination of cost appropriateness at the individual action level, and a configuration that calculates the degree of deviation between the value set by the user in each process and the statistical standard value, visualizes it in the observation space, and discloses the deviation information to the outside as audit information. [Effects of the Invention]

[0052] According to the present invention, the relationship between intangible assets, protective measures, and the market environment can be treated as a dynamic structure that includes subject dependence, time changes, and uncertainty, and users can explore and compare the transition of states and future states (future scenarios) through intuitive operation.

[0053] Furthermore, by employing methods that perform evaluations based on the substantial effects of evaluation entities, such as calibration based on evaluation entity attributes and normative standards, regression learning of decision-making pathways, and commitments, it is possible to explicitly address variations in evaluation results due to differences in evaluation entities and normative standards. Moreover, by ensuring transparency through disclosure control of decision-making pathway attributes, third-party audits, and tamper detection, and by expressing the degree of deviation from the standards or the application of double standards as a demeanor, users can intuitively grasp evaluation bias or credit risk.

[0054] Furthermore, by maintaining the arrangement of indicators at a predetermined point in the past in a reusable format and regenerating future states by replacing at least some of the external environmental parameters, users can directly verify in the observation space, by comparing with actual results, what future states could have occurred if different market conditions or external conditions had existed at the past decision point. This can contribute to ex post facto verification of the validity of past decisions, decision support in similar situations, and continuous improvement of evaluation criteria.

[0055] Furthermore, by calculating a market impact coefficient for each possible action that the subject being evaluated can take, and correcting the transition parameters of the state transition model based on these coefficients, it becomes possible to generate future states that take into account the autoregressive effect in which the subject's own actions change the preconditions for state transitions. This allows users to make decisions after quantitatively and intuitively understanding the impact their own actions have on future states.

[0056] Furthermore, by adding concepts such as measurement resolution, measurement range, or error band to the distance or similarity evaluation results, the robustness, reproducibility, and accountability of judgments in comparisons and rankings can be improved. Additionally, by displaying differential comparisons based on branching points and clearly indicating failure transition patterns, the rationality of users' risk perception and scenario selection can be improved.

[0057] Furthermore, by allowing a portion of the state transition model or evaluation model to be replaced as an external module, flexible operation becomes possible depending on the application, purpose, or evaluation perspective. In particular, by adopting a form in which the external module is called as a service, this information processing method can update, replace, or use multiple modules in combination with the external module without depending on the internal implementation of the external module. In addition, by dynamically calculating and updating the reliability level based on feedback from multiple users on the output of the external module and reflecting this in the weighting of the output, the quality evaluation of the external module can be continuously improved based on the collective intelligence of the user community, and by resolving output conflicts of multiple modules using consensus rules, the quality of the integrated output can be improved.

[0058] Furthermore, by visualizing the structure of potential revenue opportunities in the market and the market reachability of intangible assets using market potential vectors and covering relation mappings, and by factorizing uncertainty based on its source and presenting countermeasures for each factor, users can obtain concrete action guidelines to reduce uncertainty.

[0059] Furthermore, by calculating the importance of external trigger information and performing indicator updates and future state regeneration only when predetermined conditions are met, and presenting the difference before and after the update, it is possible to support user decision-making by dynamically updating evaluation results in response to changes in the external environment.

[0060] Furthermore, by recording, comparing, and sharing the evaluation status at a specific point in time using scenario snapshots, and by converting the state in the latent space into a three-dimensional metaphor space, users can easily intuitively grasp the changes in the evaluation status over time and the protective measures in place. In addition, by comparing the cost and expected benefit of individual countermeasures to determine their appropriateness at a micro level, and by monitoring the deviation between set values ​​and statistical standard values ​​and disclosing it as audit information, it is possible to support rational decision-making and accountability at the individual action level.

[0061] Furthermore, by optimizing the strategic defense posture by integrating market accessibility, counterparty suitability, protection investment limits, and gate strength, it is possible to construct a strategy that maximizes the value of intangible assets while minimizing risks, thereby improving the practicality of valuing intangible assets and rights. [Brief explanation of the drawing]

[0062] [Figure 1] This is a block diagram showing the overall configuration of the information processing system according to the present invention. [Figure 2] This is a block diagram showing the functional configuration of the processing unit. [Figure 3] This is an explanatory diagram showing the data structure of intangible assets. [Figure 4] This is a flowchart showing the overall processing procedure of the information processing method according to the present invention. [Figure 5] This is a conceptual diagram showing the projection relationship between the latent space and the observed space. [Figure 6] This is an example screen showing a display example in the observation space. [Figure 7] This is an example screen showing a presentation of future states (future scenarios). [Figure 8] This is an explanatory diagram showing examples of measurement resolution and error band assignment. [Figure 9] This is a conceptual diagram illustrating the relationship between calibration and value discrepancy. [Figure 10] This is an example screen showing an example of updating and comparing differences triggered by an external event. [Figure 11] This is an example screen showing an example of visualizing audit records and reliability levels. [Figure 12] This is a flowchart showing the processing flow of counterfactual recalculation. [Figure 13] This is an explanatory diagram showing an example of ghost display due to counterfactual recalculation. [Figure 14] This is an example screen showing a comparison of future states with and without autoregressive influence. [Figure 15] This is an explanatory diagram showing the flow for reflecting reliability based on user feedback. [Figure 16]This is an explanatory diagram illustrating the concepts of market potential assessment, uncertainty factorization, and cost justification. [Figure 17] This diagram illustrates the concepts of conflict resolution for multiple module outputs, sharing of scenario snapshots, and metaphor projection rules. [Modes for carrying out the invention]

[0063] Embodiments of the present invention will be described below with reference to the drawings. Note that the following embodiments are not limiting to the present invention, and various modifications are possible within the scope of the technical concept of the present invention. [Examples]

[0064] The following describes one embodiment of the information processing method according to the present invention. In this embodiment, the state in which intangible assets belong to an individual, corporation, organization, or group is treated not as a single concept, but as a state quantity that is established as a result of the combination of multiple asset elements, and an index representing the state of the intangible assets is calculated based on the asset elements and their combined state. This embodiment provides a technical framework (system configuration, definition of terms, definition of intangible assets, configuration of asset elements, definition of latent space and observation space, etc.) that will serve as a common foundation for each information processing method described in subsequent embodiments. Figure 4 shows an overview of the overall processing flow of the information processing method according to the present invention. Figure 4 schematically illustrates, as an example, a series of processes, from acquiring publicly available intangible asset information (step S1), acquiring or generating asset elements and their combined states (step S2), calculating indicators and placing them in latent space (steps S3, S4), projecting and visualizing them in observation space (steps S5, S6), calculating distance or similarity (step S7), generating future states (future scenarios) and presenting state transition branches (steps S8, S9), acquiring external trigger information, calculating its importance and determining predetermined conditions (steps S10, S11; if the conditions are not met, return to step S6), updating or regenerating indicators and displaying differences (steps S12, S13), and assigning measurement resolution and error bands, calibration, audit records, and calculating reliability (steps S14-S17). Details of each step will be explained in this embodiment and subsequent embodiments, and the execution order, necessity, and repetition patterns of each step are not limited to the example shown in Figure 4. The aforementioned reliability refers to a general term for indicators that show the certainty of evaluation results, information, audits, module outputs, etc.

[0065] (System Configuration Overview) Figure 1 shows the overall configuration of the information processing system according to this embodiment. The information processing system 1 includes an information processing device 10, a user terminal 20, an external information source 30, and an external module 40. The information processing device 10 comprises a processing unit 11, a storage unit 12, and a communication interface unit 13. The user terminal 20 comprises a display unit 21 and an input unit 22.

[0066] Figure 2 shows the functional configuration of the processing unit 11. The processing unit 11 includes at least a state representation means 100 and a visualization means 101, and may include a future state generation means 102, a calibration means 103, and an external observer model 104. The state representation means 100 calculates an index 207 representing the state of intangible assets based on asset elements and their combined states, which will be described later, and performs the process of placing the index in the latent space 210. The visualization means 101 performs the process of converting the calculated index into a human-understandable observation space 220 and displaying it through the display unit 21. In addition to these functional means, the processing unit 11 may further include a counterfactual calculation means 700, an autoregressive influence estimation means 710, and a user feedback acquisition means 720. Details of the counterfactual calculation means 700, the autoregressive influence estimation means 710, and the user feedback acquisition means 720 will be described in Examples 11, 12, and 13, respectively (see Figures 12, 14, and 15). Furthermore, past state data 701 refers to data that retains the arrangement state of indicators 207 at a predetermined point in the past in a format that can be reused as input to the state transition model, market influence coefficient 711 refers to a coefficient that represents the magnitude of the influence that a candidate action has on the preconditions for state transition, and feedback metadata 721 refers to data that represents the evaluation behavior of multiple users on the output of the external module 40. External environment parameters refer to parameters that quantify or structure external factors such as the market environment, laws and regulations, technological trends, and competitive situation.

[0067] (Definition of terms) In this specification, "attribution" means that an intangible asset or an asset element constituting an intangible asset is treated as being linked to a specific entity for the purpose of evaluation and index calculation. Such attribution includes both attribution based on legal rights and attribution not based on legal rights. It should be noted that attribution in this invention is a concept for defining the premise for evaluation and index calculation, and does not determine the attribution of legal rights or responsibilities.

[0068] Furthermore, "subject" refers to the scope set as the target of evaluation or index calculation in this information processing method. This scope may be set by the information processing method based on the user's instructions or requests, or it may be set automatically by prediction based on the user's history and the intangible assets they hold. This scope may include individuals, corporations, organizations, and groups, and may also be set considering regional factors, economic factors, distribution structure factors, etc., depending on the evaluation target. When this subject is used as a criterion for evaluating intangible assets, it is called the "evaluation subject," and when it is treated as the recipient of intangible assets or asset elements, it is called the "recipient subject."

[0069] (Basic definition of intangible assets) In this embodiment, an intangible asset 200 is defined as a state in which multiple asset elements combine to create value. Figure 3 shows the data structure of the intangible asset 200. The intangible asset 200 includes asset elements 201 to 205, combination status 206, indicator 207, update history 208, and audit log 209. Even if individual asset elements 201 to 205 belong to the entity alone, those asset elements are not necessarily treated as a state in which they have value as an intangible asset 200.

[0070] For example, factors that may influence intangible assets include municipalities, countries or regions, economic alliances or economic zones between countries, and factors related to trade flows and supply chains. In other words, these factors (regional factors, economic factors, distribution structure factors) are treated as external conditions or environmental factors that affect the intangible asset, and their influence is assigned to the asset element and they are treated in combination. In this combination, an importance level indicating the ratio of influence may be further assigned and treated in combination. In this case, the combination status 206 and indicator 207 that reflect the result of the combination of the external conditions or environmental factors, as well as the relevant portions of the update history 208 and audit log 209 related thereto, may be treated as information belonging to the aforementioned entity.

[0071] As one specific example, the external conditions or environmental factors act as factors that change the distribution of evaluations in the latent space 210 used for evaluation, and the bias in this distribution is reflected in the trend of the calculated evaluation indicators, and is treated in conjunction with the asset elements. Here, the latent space 210 is a representational space inherent in the information processing entity, as defined in the means for solving the problems of this specification, and is a multidimensional space that does not presuppose human perception.

[0072] This information processing method evaluates information regarding regional factors, economic factors, and distribution structure factors, then maps the evaluation results to corresponding asset elements and combines them with other asset elements to form state quantities that constitute intangible assets 200.

[0073] Thus, the information processing method according to this embodiment calculates an index 207 representing the state of the intangible asset 200 based on the asset element and its combined state 206. This index 207 is used in subsequent embodiments for observing temporal changes (Embodiment 2), placement in the latent space 210 and generation of future states (Embodiment 3), and calibration of evaluation criteria (Embodiment 4).

[0074] (Intangible asset value and market value) In this embodiment, the value of the intangible asset 200 is evaluated as a value that reflects not only a value calculated based on the combined state 206 of the asset elements itself, but also a value adjusted according to the attributes of the valuation entity in the market in which the intangible asset 200 is located (hereinafter referred to as market value). This market value is treated as an adjustment value applied according to the market environment in which the intangible asset 200 is located and the attributes of the valuation entity.

[0075] Here, market value refers to the value estimated based on the assumption that the intangible asset 200 will be used in a specific market or business environment. This market value is treated as potentially varying depending on the attributes of the valuation entity, i.e., the industry, market, business area, etc., to which the entity acquiring or using the intangible asset belongs.

[0076] Furthermore, in Example 4, the concept of market value is separated into general market value 412 and value specific to the valuation body 413, and is described in detail as an explicit operation called calibration.

[0077] (Required binding element) Specifically, in this embodiment, the following asset elements are defined as essential connecting elements for establishing the intangible asset 200 (see Figure 3).

[0078] (Element 1) Asset element 201 that is established as a legal right such as a patent right, utility model right, design right, or trademark right.

[0079] (Element 2) Assets related to know-how such as technical ideas, design information, and business knowledge (Element 202)

[0080] In this embodiment, element 1 (asset element 201) alone is not the dominant factor as an intangible asset, and is treated as only having value as an intangible asset 200 when it is combined with element 2 (asset element 202) and other asset elements (in the case of trademark rights, market history etc. which can be combined as element 5 (asset element 205) described later).

[0081] In other words, with respect to patent rights and utility model rights, since the subject matter of these rights is a technical idea, the substance protected by these rights corresponds to know-how, and rights that are not combined with the aforementioned element 2 (asset element 202) are treated to a limited extent as a subject of valuation.

[0082] Furthermore, while design rights differ from patent rights in that the external shape is specified in writing, etc., the knowledge relating to the external shape is treated as being combined as element 2 (asset element 202) to form value as an intangible asset 200.

[0083] With regard to trademark rights, the value of the intangible asset 200 is formed when the chronological history of market recognition, goodwill, and transaction history is combined as information elements representing the state of the intangible asset 200. Trademark rights that do not have this history combined may be evaluated as having no value as an intangible asset 200. Furthermore, the aforementioned history can be combined as element 5 (asset element 205) described later.

[0084] (auxiliary connecting element) Furthermore, in this embodiment, the following elements are defined as asset elements that can be supplementarily combined with the essential combination elements.

[0085] (Element 3) Asset Element 203 relating to trade secret protection capability based on systems, regulations, and actual operational practices for managing and maintaining confidential information.

[0086] With respect to Element 3 (Asset Element 203), the valuation is based not on the existence of formal systems or regulations themselves, but on the ability to continuously operate those systems.

[0087] Furthermore, the following asset elements can be added as auxiliary components.

[0088] (Element 4) Asset element 204 relating to copyrighted works

[0089] With regard to copyrighted works, the value of their existence, whether tangible or intangible, is treated as being calculated based on past performance, and this information processing method does not consider that a sufficient initial value exists for such copyrighted works. This value may be set based on information obtained through user input or integration with external systems.

[0090] (Evaluation modification element: brand value) When the aforementioned element 4 (asset element 204) is combined, its value is greatly influenced by the following valuation modification elements.

[0091] (Element 5) Asset element 205 related to brand value formed from market recognition, credibility, usage history, transaction history, etc.

[0092] Element 5 (asset element 205) is evaluated by referring to accumulated information based on past events and is treated as an asset that is gradually realized based on its relationship with resource inputs such as funds and man-hours. Furthermore, element 5 may also affect the value of other asset elements such as element 4 and element 1 related to trademark rights.

[0093] On the other hand, asset element 205 related to the brand value is also treated as a state quantity that can be impaired in the opposite direction to its assetization. For example, if another brand is launched in the same or similar market, or if tangible or intangible goods that can be considered similar to the brand begin to circulate in the market, asset element 205 is assessed as being impaired in proportion to the decline in market share. This assessment is carried out in conjunction with the expansion or contraction of the overall market.

[0094] Furthermore, if the brand reaches a stage where it is assessed to have spilled over into markets different from those initially anticipated, the asset component 205 relating to the brand value may be shown as increasing non-linearly.

[0095] (Impact of exercising rights) If asset element 205 relating to the brand value is based on the scope of rights to which the exercise of those rights is left to the discretion of the rights holder, then the actions of the rights holder, whether or not they take legal action or other action to exercise those rights, will be treated as affecting the value of asset element 205.

[0096] This information processing method evaluates the impact of an action on the impairment or increase of intangible asset value based on its similarity to past cases. In this case, the evaluation results at the present time and evaluation results at future time can be displayed side by side. Furthermore, a configuration that more precisely handles the impact of the subject's own actions on the preconditions for state transitions is described in detail in Example 12 as predictive control that considers autoregressive effects based on the market influence coefficient 711 (see Figure 14).

[0097] (Evaluation considering market value) The information processing method according to this embodiment includes a step of calculating an index 207 based on the combination state 206 of the asset elements, and then performing a correction process on the index 207 to reflect the market value. If the index 207 includes element 5 (asset element 205), the correction process shall be applied to the index 207 that reflects the influence of element 5.

[0098] In this adjustment process, an adjustment factor is applied to the intangible asset 200 being evaluated, taking into account the market to which the intangible asset 200 belongs, whether or not the entity utilizing the intangible asset 200 has already entered that market, and the costs, equipment, know-how, etc., required to enter that market.

[0099] For example, if the valuation entity is already in the same or a similar market as the intangible asset 200, the market value may be adjusted to be higher because the additional costs to utilize the intangible asset 200 will be relatively low. On the other hand, if the valuation entity belongs to a different market than the intangible asset 200, the market value may be adjusted to be lower by considering the barriers to entry required to utilize the intangible asset 200.

[0100] Furthermore, the correction process is explicitly defined as calibration in Example 4 and is described in detail as calibration of market value based on the attributes of the evaluation entity (for example, users, organizations, etc., to which attributes such as regional factors, economic factors, and distribution structure factors are assigned).

[0101] (Selection of the evaluator) In the information processing method according to this embodiment, the user can select evaluation criteria corresponding to the evaluating entity. These evaluation criteria can be switched by the user's operation and are not limited to discrete selections; they may also be set continuously using ratios or the like.

[0102] This makes it possible to switch between displaying the value of the same intangible asset (200) from the perspective of its own market and from the perspective of other markets.

[0103] Furthermore, the concept of the evaluation entity is extended in Example 4 to serve as the basis for calculating and calibrating the evaluation entity's intrinsic value 413.

[0104] (Calculation of indicators and placement in latent space) In this specification, "combination state 206" refers to a state in which the correlation, contribution, or dependency between multiple asset elements 201 to 205 is quantified. The combination state 206 is calculated as factor loadings, correlation coefficients, contribution rates, output values ​​of weighted aggregate functions, or combinations thereof, and is expressed as a scalar quantity, a vector quantity, or a matrix.

[0105] In the information processing method according to this embodiment, an index 207 is calculated based on the combination state 206 of the asset elements 201 to 205.

[0106] In quantifying the combined state 206, the correlation, contribution, or dependency between the multiple asset elements 201 to 205 may be calculated as factor loadings, correlation coefficients, contribution rates, or weighted aggregate functions (see Patent Documents 2 and 3). For example, the factor loadings of each asset element for latent factors extracted by factor analysis may be calculated, and an index 207 representing the combined state 206 may be constructed based on these factor loadings. Alternatively, the states of the multiple asset elements 201 to 205 may be calculated as a single scalar quantity or a low-dimensional vector quantity by weighted aggregate operations using the framework of a multi-attribute utility function.

[0107] In the conversion from asset elements 201 to 205 to index 207, the correspondence between asset elements and indexes is not limited to one-to-one. That is, multiple asset elements may contribute to a single index component, and a single asset element may contribute to multiple index components. This many-to-many correspondence is analogous to the relationship between observed variables and factors in factor analysis, or the structure of the measurement model in structural equation modeling.

[0108] The calculated index 207 is placed and stored in the latent space 210 of the information processing entity. Figure 5 shows the projection relationship between the latent space 210 and the observation space 220.

[0109] The aforementioned latent space 210 is a representational space inherent in an information processing entity, as defined in the means for solving the problems of this specification, and is a multidimensional space that does not presuppose human perception. This concept is similar to the concept known as "latent space" in the field of machine learning (see Non-Patent Document 1).

[0110] The arrangement of the indices 207 in the latent space 210 may be transformed into a human-readable observation space 220 based on predetermined rules to facilitate user understanding. The observation space 220 is a subspace of the state quantities in the latent space 210 that are observed in a form understandable to the user, as defined in the means for solving the problems of this specification. This concept is analogous to the space of observation vectors defined by the measurement equations in a state space model, and has a structure in which a part of the state space is selectively observed by an observation matrix (selection matrix) (see Non-Patent Literature 2). In Figure 5, the projection 222 shows the transformation from the latent space 210 to the observation space 220, and the boundary 221 shows a conceptual demarcation that defines the area occupied by the intangible asset 200 within the latent space 210 (in Figure 5, the indices projected onto the observation space 220 are shown as 207'). Boundary 221 may be displayed as a boundary representation such as a castle wall in the observation space 220 (Example 2), and may be treated as an object (gate) for evaluating the defensive capabilities against external intrusion or intrusion (Example 10).

[0111] (display) In the information processing method according to this embodiment, the user can select the asset element to be displayed from among the multiple asset elements 201 to 205 that constitute the intangible asset 200. This information processing method displays the index 207 based on the combination state 206 of the asset elements, calculated according to the above description, and the corrected index 207 that reflects the market value, as images on the observation space 220. Figure 6 shows an example of the display in the observation space 220.

[0112] This selection is not limited to being statically set, but may also be configured to be dynamically changeable in response to user actions. This allows users to instantly switch between the asset elements to be displayed while checking the status of each asset element.

[0113] Furthermore, users may set multiple asset elements 201 to 205 as a single integrated asset element. In this case, the information processing method calculates an index 207 based on the combined state 206 of the multiple asset elements and generates a display based on an overview perspective. This allows users to visually grasp the value of intangible assets 200 from their own perspective.

[0114] Each calculated asset element or integrated asset element is displayed as an image in the observation space 220. In this embodiment, the image may be displayed as a three-dimensional image so that the user can intuitively grasp the relationships between the asset elements.

[0115] Here, "an image recognizable as three-dimensional" is not limited to images showing only a single elevation or plan view, but rather includes the concept of images that simultaneously show multiple views of an object from different perspectives, specifically, three-view drawings.

[0116] More preferably, the image is displayed as a perspective view and may be displayed with shading or lighting. For example, by using lighting techniques such as loop lighting or Rembrandt lighting, the combination and interrelationships of asset elements can be visually emphasized.

[0117] Furthermore, the image that can be recognized as three-dimensional may be displayed using binocular parallax, thereby allowing the user to grasp the state of the intangible asset 200 as a three-dimensional display with depth.

[0118] Furthermore, the information processing method according to this embodiment may be configured to allow the user to arbitrarily change the position of their viewpoint. This makes it possible for the user to observe the intangible asset 200 from different viewpoints, and to grasp the combined state 206 of the asset elements and its changes from multiple perspectives.

[0119] Furthermore, in the information processing method according to this embodiment, the position of the light source in the lighting may be configured to be arbitrarily changed according to the user's operation. This allows the user to observe the state of the intangible asset 200 under different lighting conditions and to more clearly grasp irregularities, discontinuities, hidden structures, etc., in the combined state 206 of the asset elements.

[0120] Furthermore, the information processing method according to this embodiment may include a step of providing audible notification in response to changes in the display. Such audible notification is output in response to, for example, the occurrence of a predetermined event during automatic playback along the time axis 320 described later, or changes in state in response to time specification operations or seek operations by the user. As a result, the user can grasp changes in the state of the intangible asset 200 through auditory information in addition to changes in the visual display.

[0121] Furthermore, the information processing method according to this embodiment includes a step for receiving instructions regarding time. The user can specify whether to display information corresponding to a past, present, or future point in time. In response to the instructions regarding time, the information processing method updates the indicators 207 of each asset element or combination state 206 and changes the displayed content. This allows the user to confirm the changes in the state of the intangible asset 200 over time, based on the perspective they desire.

[0122] (Expansion of experiential expression) Furthermore, the information processing method according to this embodiment may include a step of presenting the state of the index 207 in the observation space 220 to the user through senses other than sight.

[0123] Presentation through senses other than sight may include one or more of the following:

[0124] (Auditory presentation) In addition to the aforementioned audible notifications, the state changes of indicator 207, the probability of branching scenarios occurring, or the risk level may be auditorily represented as pitch, volume, timbre, rhythm, or harmonic structure. For example, by representing an increase in the evaluation indicator as an ascending melody and a decrease as a descending melody, users can recognize state changes without relying on visual cues.

[0125] (Tactile presentation) The state of the indicator 207 in the observation space 220 may be presented to the user via a haptic feedback device. This haptic feedback may be expressed as a vibration pattern, temperature change, pressure change, or texture sensation. For example, by expressing an increase in the value of the intangible asset 200 as a warming sensation and a decrease in value as a colding sensation, or by expressing the uncertainty of the branching scenario as the intensity of vibration, the user can grasp the state through physical sensation.

[0126] (kinesthetic presentation) The state of the indicator 207 in the observation space 220 may be presented to the user via a force feedback device. This force feedback may be expressed as resistance, repulsion, or traction. For example, by expressing the bonding strength between intangible assets 200 as resistance during operation and the influence from the market environment as an external force, the user can intuitively grasp the relationships between the states.

[0127] (Olfactory presentation) The state of the indicator 207 in the observation space 220 may be presented to the user via a fragrance output device. This olfactory presentation may be expressed as a fragrance pattern associated with a specific state or classification. For example, associating asset elements that are showing a growth trend with a specific fragrance can help maintain attention during long-term observation.

[0128] (Multisensory presentation) Each of the aforementioned sensory modalities may be used individually or in combination. By combining multiple sensory modalities, complex states that are difficult to perceive with a single sense can be presented to the user more intuitively. Furthermore, users with impairments in one or more senses can be provided with equivalent information through other senses.

[0129] Thus, the information processing method according to this embodiment may include a step of expressing the index 207 in the observation space 220 experientially through one or more of the five human senses. This experiential expression may be used in place of visual visualization, or in combination with visual visualization.

[0130] Furthermore, the specific device configurations for realizing presentation through each of the aforementioned sensory modalities (such as tactile feedback devices, force feedback devices, and fragrance output devices) can be those of known devices, and the technical scope of the present invention is not limited to such device configurations.

[0131] (System Configuration) As shown in Figure 1, the information processing system 1 that implements the information processing method according to this embodiment comprises at least a processing unit 11 that performs arithmetic processing, a storage unit 12 that stores information, a display unit 21 that displays information to the user, and an input unit 22 that receives input from the user.

[0132] The processing unit 11 executes each of the steps described in this embodiment based on the information stored in the storage unit 12 and the input from the user.

[0133] The storage unit 12 stores information about the multiple asset elements 201 to 205 that constitute the intangible asset 200, information about the combination state 206 of the asset elements, information about the valuation entity, information about the market value, and information about the calculated index 207. The storage unit 12 also holds information about the arrangement of the index 207 in the latent space 210.

[0134] The display unit 21 displays information representing the combination state 206 of asset elements in the observation space 220, generated by the processing unit 11, in a format that can be visually grasped by the user. The display unit 21 may be configured to display an image that can be recognized as three-dimensional.

[0135] The display unit 21 may include, in addition to or instead of, a visual display, an auditory output means (speaker, earphones, etc.), a tactile output means (vibrator, temperature change element, etc.), a force-feedback means (force-feedback device, etc.), or an olfactory output means (fragrance generator, etc.). This allows the information processing system 1 to present information through the most appropriate sensory modality according to the user's sensory characteristics or usage environment.

[0136] The input unit 22 accepts user input such as the selection of asset elements to be displayed, the selection of the evaluation body, instructions regarding time, changes in the viewpoint, and other operational instructions.

[0137] As shown in Figure 2, the processing unit 11 includes at least a state representation means 100 and a visualization means 101.

[0138] The state representation means 100 calculates an index 207 representing the state of the intangible asset 200 based on the asset elements 201 to 205 and their combined state 206, and executes a process to place the index 207 in the latent space 210. The state representation means 100 treats multiple asset elements, including asset element 201 (element 1) relating to legal rights, asset element 202 (element 2) relating to know-how, asset element 203 (element 3) relating to trade secret protection ability, asset element 204 (element 4) relating to copyright, and asset element 205 (element 5) relating to brand value, as representations in a multidimensional space.

[0139] The visualization means 101 converts the index 207 calculated by the state representation means 100 into a human-understandable observation space 220 and performs the process of displaying it through the display unit 21. The visualization means 101 performs operations such as switching the asset elements to be displayed, switching the evaluation entity, and changing the viewpoint in response to operations by the user through the input unit 22.

[0140] The observation space 220 is not limited to a display format, but can be configured as a representational format different from the latent space 210, to assist human understanding and judgment.

[0141] The processing unit 11 may, if necessary, include a future state generation means 102 that generates a future state using the current state represented by the state representation means 100 as an initial condition. Details of the future state generation means 102 will be described in a subsequent embodiment (Embodiment 3).

[0142] Furthermore, the processing unit 11 may include extension means that, if necessary, allow at least a portion of the calculation processing to be replaced or used in combination with an externally provided calculation model or analysis module (hereinafter referred to as the external module 40).

[0143] The external module 40 is provided independently of the information processing system 1 and is a arithmetic processing function that is called via a predetermined interface (see Figure 1). Any of the following configurations or combinations thereof may be used for cooperation with the external module 40.

[0144] (a) Service calls over the network If the external module 40 is provided as a Web service or API (Application Programming Interface), the information processing system 1 sends a request to the external module 40 via the network through the communication interface unit 13 and receives the calculation result as a response.

[0145] (b) Plugin-in If the external module 40 is provided as a plug-in, the information processing system 1 dynamically loads the plug-in and performs calculations via a predetermined interface.

[0146] (c) Collaboration in batch processing format When the external module 40 is provided as batch processing, the information processing system 1 provides the input data to the external module 40 as a file or data stream, and obtains the output result after processing is complete.

[0147] The input / output data format for the external module 40 may be PMML (Predictive Model Markup Language), XML, JSON, CSV, or a structured data format based on a predetermined schema. By using these standard data formats, general-purpose collaboration that is independent of the provider of the external module 40 can be achieved.

[0148] Specific examples of the external module 40 include the following:

[0149] (i) Predictive models Machine learning models or statistical models such as regression analysis models, classification models, time series forecasting models, neural networks, and decision trees.

[0150] (ii) Optimization algorithms Optimization techniques such as linear programming, integer programming, constrained optimization, and genetic algorithms.

[0151] (iii) Rule engine Rule-based inference capabilities such as business rules, decision tables, and decision graphs.

[0152] (iv) Scoring Model A model for calculating evaluation scores such as credit score, risk score, and suitability score.

[0153] (v) Simulation engine Probabilistic simulation functions such as Monte Carlo simulation and agent-based simulation.

[0154] This information processing system 1 can utilize the external module 40 in a form (black box use) where it transmits input data and receives output results without knowing the internal structure of the external module 40. This allows the provider of the external module 40 to provide computational functions to this information processing system 1 while keeping the internal implementation of the module confidential. The dynamic calculation of confidence levels based on the evaluation behavior of multiple users of the output of the external module 40 and its reflection in the weighting of the output will be described in detail in Example 13 (see Figure 15).

[0155] Even when the aforementioned extension means is used, the processing unit 11 is responsible for preparing the inputs, integrating the outputs, and managing the calculation of the index 207, and the external module 40 is used in a configuration that assists or replaces all or part of the processing performed by the processing unit 11.

[0156] This information processing system 1 may be configured as a single device, or as a system in which multiple devices are connected in a communicative manner. In the latter case, the components of the processing unit 11, storage unit 12, display unit 21, and input unit 22 may be distributed across different devices.

[0157] The functions of the processing unit 11 in this information processing system 1 may be realized by a program that causes a computer to execute each of the steps described in this embodiment. This program may be recorded on a computer-readable recording medium or distributed via a communication line.

[0158] (Connection to subsequent embodiments) Furthermore, the concepts of "latent space 210" and "observation space 220" defined in this embodiment will be used in subsequent embodiments as a basis for observing state transitions along the time axis (Embodiment 2), generating and branching future states (Embodiment 3), and calibrating evaluation criteria (Embodiment 4). In addition, the counterfactual virtual calculation means 700, autoregressive influence estimation means 710, and user feedback acquisition means 720 included in the processing unit 11 of this embodiment will be described in detail in Embodiments 11, 12, and 13, respectively, and will be used as a common basis for counterfactual virtual recalculation of states at predetermined points in the past (see Figures 12 and 13), predictive control considering autoregressive influences caused by the subject's own actions (see Figure 14), and dynamic management of reliability based on user feedback to the external module 40 (see Figure 15). Furthermore, various configurations such as structuring market potential and factorizing uncertainty, resolving conflicts among multiple module outputs, saving, comparing, and sharing scenario snapshots, visualization using metaphor projection rules, and determining the cost appropriateness of individual countermeasures (see Figures 16 and 17) will be described in detail in subsequent embodiments, based on the indicator 207, latent space 210, and observation space 220 defined in this embodiment.

[0159] Furthermore, the concept of "asset elements 201-205" in this embodiment will be expanded in subsequent embodiments as a relationship with "asset properties" in a multidimensional space. Here, "asset properties" are computational degrees of freedom set to describe asset elements in latent space 210, and are concepts equivalent to factors in factor analysis or latent variables in structural equation modeling. The asset elements 201-205 and the asset properties do not need to correspond one-to-one; a single asset element may be represented by multiple asset properties (e.g., multiple columns of a factor loading matrix), and multiple asset elements may be represented by a single asset property (e.g., a scalar quantity calculated by weighted aggregation) (see Patent Documents 2 and 3). Through this many-to-many correspondence, the combination state 206 of asset elements can be computably represented as a position or distribution in latent space 210.

[0160] (effect) The information processing method according to this embodiment acquires information on the multiple asset elements 201 to 205 and their combination state 206 as described above, calculates an index 207 for each asset element and combination state, and displays the index 207 on the same observation space 220. This allows users to visually grasp the combination state 206 of the asset elements constituting the intangible asset 200 and its changes over time. Furthermore, the system configuration, definition of terms, configuration of asset elements, and definitions of the latent space 210 and observation space 220 provided by this embodiment form a common foundation for counterfactual recalculations, predictive control considering autoregressive effects, reliability management based on user feedback, and other configurations added by subsequent embodiments. [Examples]

[0161] (Examples of display and operation involving time-series transitions) The following describes another embodiment of the information processing method according to the present invention. This embodiment adds the configuration of observing the temporal changes of the index 207 to the common infrastructure provided in Embodiment 1 (system configuration, definition of terms, definition of intangible asset 200, configuration of asset elements 201 to 205, definition of latent space 210 and observation space 220, etc.).

[0162] This embodiment is based on the information processing method described in Embodiment 1, and calculates an index 207 based on predetermined calculation criteria from information concerning an intangible asset 200 and the rights protecting said intangible asset 200, and makes said index 207 displayable and operable over time.

[0163] In this embodiment, while maintaining the concept of the latent space 210 introduced in Embodiment 1, a configuration is shown that allows for the observation of the temporal change of the index 207 in that space.

[0164] (Information acquisition) The information processing method according to this embodiment includes a step of acquiring information on multiple intangible assets 200 and corresponding rights held by an individual or legal entity (company or organization). The acquired information includes the duration of the rights, the registration date, the scope of the rights, the usage status, and information on the usage environment and market conditions of the intangible assets 200.

[0165] Furthermore, in this embodiment, if external environmental information such as treaties between nations, national budgets, customs duties, tax systems, administrative guidelines or administrative operations, and reports published by government agencies may affect the status of the intangible asset 200 or rights, such external environmental information may be acquired as information used in calculating the indicator. This may improve the accuracy of the representation of valuation in international transactions, etc. The external environmental information is a specific example of external conditions or environmental factors corresponding to the regional factors, economic factors, and distribution structure factors defined in Example 1, and is mapped to and combined with the corresponding asset elements 201 to 205 for processing.

[0166] Furthermore, it is also possible to acquire IR information from companies and investors in proportion to the national budget, and use their investment plans as external environmental information for calculating indicators.

[0167] Furthermore, when it is necessary to emphasize the influence of municipal-level external environmental information (i.e., when it is of higher importance than others) as information used to calculate one of the elements constituting intangible asset 200 (for example, asset element 202 corresponding to element 2), the indicator may be calculated assuming that there are times when the influence of these factors outweighs that of other external environmental information. This calculation method can improve the accuracy of representations for small-scale retailers, service businesses, restaurants, etc.

[0168] This information may be obtained through user input, or through automated acquisition using RPA (Robotic Process Automation), etc. Furthermore, information on current financial transactions and market conditions, various rights and obligations, publicly available information from other companies, international affairs, etc., may be obtained as discrete automated information acquisition at set time intervals using APIs, etc. (see Figure 1).

[0169] This allows, for example, when it is foreseen that damage to intangible assets 200 will exceed a predetermined threshold due to an event unexpected by the user (such as an accident, disaster, or dispute), the system to automatically acquire information and push a detailed report to the user (see Figure 10). In other words, the information processing method may be configured to acquire additional information when conditions specified by the user are met. The acquisition of the external trigger information 500, the calculation of its importance 502, and the updating of the indicator 207 and regeneration of the future state 300 according to the importance 502 will be described in detail in Example 7 (see Figure 10).

[0170] It should be noted that the aforementioned external environmental information indicates the external conditions of the subject of evaluation and is different in nature from the publicly available intangible asset information (reference knowledge used for evaluation) introduced in Example 6.

[0171] (Calculation of indicators based on calculation criteria) Next, based on the acquired information, an index 207 is calculated that indicates the status of each intangible asset 200 and each right. In this embodiment, the index 207 is calculated based on predetermined calculation criteria and is used to enable comparison between different intangible assets 200 or their status at different points in time.

[0172] The aforementioned calculation criteria may be set based on physical quantities or definitive numerical values ​​if the information can be treated as such. For example, if a legal right has a term of duration, an evaluation function indicating the status of the right can be constructed using the publication date, the expiration date, or the time from the publication date to the expiration date.

[0173] On the other hand, if the calculation includes factors that are difficult to treat as definitive physical quantities, such as whether or not pension payments will be made in the future, objections, cancellation proceedings, or the stability of rights based on the relationship with prior art, or market trends, the calculation criteria may be set based on statistical methods, probabilistic methods, or a prescribed reference model.

[0174] In this embodiment, as an example of such calculation criteria, an information processing model that has learned information about the legal system or business practices under which the right is established can be used as a reference criterion. In this case, the information processing model may reflect case law and trade practices in a specific country or region, and the weighting of the referenced information may be adjusted according to the area of ​​operation of the company or organization and its relationship to applicable law.

[0175] The aforementioned reference model may be used to evaluate the correspondence between the current state of intangible assets 200 or rights and similar past cases. This correspondence is dealt with in more detail as a similarity rate, which is detailed in Example 3.

[0176] However, in this embodiment, the reference space for arranging the indicator 207 is treated as fixed. That is, even if the indicator 207 is calculated based on different time points, different intangible assets 200, or different evaluation conditions, it can be compared with each other by being arranged on the same reference space. The configuration for calibrating the reference space itself according to the attributes of the evaluation subject, etc., is described in detail in Embodiment 4 as the calibration of evaluation criteria by the calibration means 103 (see Figures 2 and 9).

[0177] Therefore, in practice, it is preferable to use the same model as the information processing model used as the reference criterion. Examples of information processing models include LLMs (Large Language Models). This makes it possible to suppress the discontinuous changes in the relative relationships between the indices 207 due to fluctuations in the criteria.

[0178] While the above explanation primarily focuses on legal rights, similar calculation criteria and indicators will be applied to information regarding the scope of rights, usage, the usage environment of Intangible Assets 200, and market conditions.

[0179] (External observation instrument function and past record documents) In this embodiment, the information constituting the reference model may include the following:

[0180] (a) Standards and guidelines Documents referenced as normative standards (e.g., UN Charter, laws and regulations, industry guidelines). These are detailed in Example 4 as components of standard module 410.

[0181] (b) Historical documents A collection of documents containing records of past successes and failures related to 200 intangible assets and rights. This information is used to constitute the external monitoring function.

[0182] The aforementioned historical documents may be used as information that constitutes the external observation device function.

[0183] The external observation function may also function as a reference basis for positioning the current status of the intangible asset 200 and rights relative to other intangible assets 200, related market information, past events, rights status, or combinations thereof present in the latent space 210.

[0184] For example, when using a pre-trained language model, the data set relating to past intangible assets 200, rights, economic events, or market events used to train the language model may correspond to the historical document set that constitutes the external observation function.

[0185] In other words, the training data in a trained language model functions as a collection of past cases for evaluating the current state of intangible assets 200 or rights.

[0186] In addition, the external observation function is embodied in Example 4 as an external observation model 104 that identifies internal responses from input and output for a specific third-party entity (see Figure 2).

[0187] (Placement in latent space and transformation into observation space) The calculated index 207 is placed in the latent space 210 described in Example 1 (see Figure 5).

[0188] In this embodiment, a process is performed to convert the arrangement of the indicators 207 in the latent space 210 into the observation space 220 that can be understood by the user. This conversion process generates a display on the observation space 220.

[0189] (Data structure of update history) In the information processing method according to this embodiment, the update history 208 of the index 207 is stored in a format having the following data structure (see Figure 3).

[0190] (a) Update identifier An identifier for uniquely identifying each update operation. This identifier may consist of a sequential number, a UUID (Universally Unique Identifier), or an identifier based on a timestamp.

[0191] (b) Update time The date and time the update was performed. The update time is recorded as Coordinated Universal Time (UTC) or based on the time zone specified by the user.

[0192] (c) Index value before update The value of index 207 before the update. This value can be expressed as a scalar quantity, a vector quantity, or a matrix, and is recorded as a coordinate in latent space 210.

[0193] (d) Updated index value The updated value of index 207. It is recorded in the same format as the index value before the update.

[0194] (e) Update factors Information indicating the factors that triggered the update. These factors may include any of the following: - Time elapsed (regularly updated) - User input - Information obtained from external source 30 - Receipt of external trigger information 500 (see Figure 10) - Correction by calibration means 103 (see Figure 2)

[0195] (f) Update difference The difference between the index value before the update and the index value after the update is 501 (see Figure 10). This difference is calculated and recorded as distance, direction vector, or rate of change in the latent space 210.

[0196] (g) Reference update source Reference information to external sources 30, external modules 40, or other intangible assets 200 related to the update.

[0197] The update history 208 is stored in the storage unit 12 defined in Embodiment 1 and made accessible upon user request. The update history 208 also works in conjunction with the audit log 209 to ensure traceability of updates (see Figure 11). The configuration for counterfactually recalculating the state at a predetermined point in the past based on information available at that time is described in detail in Embodiment 11 as a configuration using counterfactual calculation means 700 (see Figures 12 and 13).

[0198] (Generating the display) In this embodiment, multiple intangible assets 200 and indicators 207 showing the status of their corresponding rights are placed on a display space configured as an observation space 220 (see Figure 6). This allows users to visually grasp the relative relationships between the intangible assets 200 and rights at a given point in time.

[0199] The generation of the aforementioned display only needs to express the state or relative relationship of the indicator 207 in a manner that is easy for the user to understand. For example, the state of the elements constituting the intangible asset 200 may be associated with the state of the land, and the boundary 221 surrounding the land may be associated with the state of rights or access routes.

[0200] Boundaries 221 can be represented by, for example, castle walls, fences, moats, stone walls, ditches, mountain ranges, cliffs, rivers, oceans, etc. The number or difficulty of access routes to the intangible assets 200 may also be represented by density, width, or shade.

[0201] Furthermore, the paths through which the value of the intangible asset 200 may be impaired may be dynamically represented, for example, by relating them to insect damage. In this case, the priority of countermeasures can be indicated by the number of insects, the amount of movement, the intensity of the colors, etc.

[0202] (Display updates along the timeline) Furthermore, in this embodiment, the display is updated in accordance with the status of intangible assets 200 and rights, which may change over time. Users can select or switch the point in time to be displayed by performing operations corresponding to the time axis 320 via the input unit 22 (see Figure 6). The time axis 320 refers to the display axis in the time direction in the observation space 220.

[0203] The updates may be continuous updates along the time axis 320, or discrete updates corresponding to predetermined time points.

[0204] In response to this operation, the position, spacing, or arrangement of each indicator 207 placed on the observation space 220 is updated, making it possible to compare the state at different points in time on the same observation space 220. This allows users to understand, continuously or intermittently, how the state of intangible assets 200 and rights changes over time.

[0205] In updating the display along the time axis 320, the index values ​​at each point in time recorded in the update history 208 are referenced, and the state at that point in time is reproduced. This allows the user to check the state of the intangible asset 200 at any point in the past on the observation space 220.

[0206] (Highlighting of differences) Furthermore, in this embodiment, the user can operate the system to highlight the differences between the state at a specific point in time and the state at a past or future point in time (see Figure 10). This makes it easier to visually recognize the direction and extent of changes over time.

[0207] Furthermore, to convey transitions to the past or future, visual techniques such as fade-out, fade-in, zoom-out, and zoom-in may be used, or combined with other visual techniques, to enhance understanding and awareness.

[0208] In highlighting the differences, the update differences recorded in the update history 208 are referenced. These update differences are visually displayed as differences 501 on the observation space 220, allowing users to intuitively understand which asset elements have changed and to what extent. The configuration for displaying differences 501, in which differences with a measurement resolution of less than 401 are treated as indistinguishable and displayed identically, is described in detail in Example 9 (see Figure 8).

[0209] (Connection to subsequent embodiments) In this embodiment, the index 207 is displayed in an observable form along the time axis 320, but behind this display, the latent space 210 introduced in Embodiment 1 exists.

[0210] In Example 3, the latent space 210 is explicitly treated as a space that is difficult for humans to directly understand, and the projection 222 from that space to the observation space 220, as well as the generation of multiple future states and the presentation of branching scenarios (future states 300), are described in detail (see Figures 5 and 7).

[0211] Furthermore, the concept mentioned in this embodiment as a correspondence with past examples using a reference model is expanded in Embodiment 3 as a state transition model based on the similarity rate.

[0212] Furthermore, the autoregressive temporal changes that occur when the subject's own actions affect the preconditions for state transitions of the intangible assets 200 belonging to that subject are described in detail in Example 12 as predictive control based on the autoregressive influence estimation means 710 and the market influence coefficient 711 (see Figure 14).

[0213] (effect) According to this embodiment, an information processing method is provided that allows observation of state changes along the time axis 320 while maintaining the index calculation and display configuration defined in Embodiment 1.

[0214] This allows users to understand the changes in the status of intangible assets 200 and the rights protecting such intangible assets 200, both continuously and discretely, from the past to the present and into the future.

[0215] Furthermore, highlighting differences based on a specific point in time makes it easier to visually recognize the direction and extent of changes over time.

[0216] Furthermore, by placing indicators 207 at different points in time on a fixed reference space, comparability between time points is ensured, making it possible to objectively evaluate the value fluctuations of intangible assets 200.

[0217] Furthermore, by maintaining the update history 208 in a structured data format, the traceability of changes in indicator 207 is ensured, making it possible to refer to the status and basis for changes at each point in time during audits or verifications. In addition, the update history 208 can also be used as the basis data for counterfactual recalculations (see Figures 12 and 13) detailed in Example 11. Note that the display mode, operation method, and specific configuration of indicator 207 in this embodiment are examples, and various modifications are possible within the scope of the information processing method described in Example 1. [Examples]

[0218] (Examples of future state generation, branching scenario presentation, and projection control from latent space to observed space) The following describes another embodiment of the information processing method according to the present invention. This embodiment is based on the common infrastructure provided in Embodiment 1 (system configuration, definition of terms, definition of intangible asset 200, configuration of asset elements 201 to 205, definition of latent space 210 and observation space 220, etc.) and the observation of state transitions along the time axis 320 introduced in Embodiment 2. On top of these, it adds the following configurations: projection 222 control from the latent space 210, which is difficult for humans to directly understand, to the observation space 220, which is understandable to humans, and generation of future states and presentation of branching scenarios (future states 300) based on a state transition model (see Figures 5 and 7).

[0219] This embodiment is based on the information processing method described in Embodiment 1, which calculates an index 207 from information relating to intangible assets 200 and the rights associated therewith, and further extends the observation and manipulation of state transitions along the time axis 320 described in Embodiment 2 to include the generation of future states, the presentation of multiple branching scenarios, and the control of projection 222 from the latent space 210, which is difficult for humans to directly understand, to the observation space 220, which is understandable to humans (see Figure 5).

[0220] In this embodiment, the concept of the latent space 210, which was introduced in Example 1 and unfolded on the time axis 320 in Example 2, is explicitly treated as a multidimensional latent space 210, and the relationship between the latent space 210 and the observation space 220 is explained in detail.

[0221] (Definition of terms) In this embodiment, the "asset elements" constituting the intangible asset 200 or right refer to the constituent units (elements 1 to 5: reference numerals 201 to 205) defined in Embodiment 1, and have the following two properties.

[0222] Firstly, the aforementioned asset elements include parts that are obvious through registration, etc. (the real part of the legal asset elements). Examples include the description of claims in patent rights and the indication of registered trademarks in trademark rights.

[0223] Secondly, the aforementioned asset elements include non-obvious components (conceptual constituent units, equivalent to imaginary parts). For example, goodwill, know-how, brand value, or even legal asset elements may be accompanied by non-obvious elements such as the actual market value of the right, synergies with other rights, and ease of exercise.

[0224] The aforementioned non-trivial portion is described in the latent space 210, and the user perceives it in the observation space 220, which is transformed from the latent space 210 by a predetermined projection rule 222 or dimensionality reduction rule (see Figure 5).

[0225] Furthermore, since legal asset elements also have non-obvious elements, all asset elements include the elements described in the latent space 210.

[0226] In this embodiment, "asset property" represents a computational degree of freedom set for describing the asset element in the latent space 210. There is no one-to-one correspondence between the asset element and the asset property; multiple asset elements may be represented by a single asset property, and a single asset element may be represented by multiple asset properties.

[0227] (1) Calculation of indicators and placement in latent space The information processing method according to this embodiment includes a step of acquiring information concerning intangible assets 200 and rights associated therewith held by an individual or legal entity (company or organization). The acquired information may include asset elements (201 to 205) constituting the intangible asset 200, the state of connection between elements 206, the duration of the rights, the registration date, the scope of the rights, the usage status, and external environmental information surrounding the intangible asset 200 and rights (see Figure 3).

[0228] Next, based on the acquired information and the set of historical records (standard documents and historical documents) defined in Example 2, the status representation means 100 calculates an index 207 representing the status of each intangible asset 200 and each right (see Figure 2). The standard documents are described in detail as components of the standard module 410 in Example 4 (see Figure 9).

[0229] (1-1) Integrated processing of asset elements In calculating the aforementioned index 207, the obvious parts of the asset elements (parts that can be confirmed by registration, etc.) and the non-obvious parts (value components that are difficult to observe, sensitivity to the external environment, reputation, potential risks, etc.) are integrated and processed as asset properties in the potential space 210.

[0230] The aforementioned process may include, for example, one of the following methods:

[0231] (a) A method of expressing the trivial part as a real component and the non-trivial part as an imaginary component, and integrating them by complex number operation

[0232] (b) A method of expressing uncertainty by amplitude and phase using the membership function of a complex fuzzy set

[0233] (c) A method of expressing hard-to-observe interference effects using complex probability amplitude in quantum-like modeling

[0234] (d) A method of expressing non-trivial value components as an intensity dimension (axis perpendicular to the paper surface) in an intellectual capital model

[0235] (e) A method of expressing verifiable components and components requiring estimation as different subspaces in a multidimensional vector space, and integrating them by tensor operation

[0236] The selection of the above method may be determined according to the nature of the intangible asset 200 to be evaluated, the characteristics of available data, or the requirements of a user.

[0237] (1-2) Placement in latent space The calculated indicators 207 are placed and held by the state representation means 100 in the multidimensional latent space 210 that is not premised on visual recognition by a user (see FIGS. 2 and 5). The latent space 210 is a space not limited to a three-dimensional space directly understandable by humans or a time axis 320, and the relationship between the plurality of indicators 207 may be expressed as a positional relationship or a distance relationship in the latent space 210.

[0238] (2) External observer function and relativization processing In the present embodiment, the external observer function introduced in Embodiment 2 is used to relatively position the current state of the intangible asset 200 and rights. Note that the external observer function is embodied as an external observer model 104 that identifies an internal response from input and output for a specific third-party entity in Embodiment 4 (see FIG. 2).

[0239] (3) Generation of future states and branching events based on similarity ratio Furthermore, in this embodiment, the future state generation means 102 generates a future state using a state transition model based on the update history 208 of the indicator 207, the current state of the intangible asset 200 and its associated rights, and the similarity rate between past success or failure events related to the intangible asset 200 and rights included in the history document group constituting the external observation function (see Figures 2 and 7).

[0240] The similarity rate may be calculated based on the correspondence between an index 207 representing the current state of intangible assets 200 or rights and an index 207 representing past events related to intangible assets 200 or rights.

[0241] (3-1) Method for calculating the similarity ratio The aforementioned similarity rate is calculated using statistical methods. Specifically, it may be obtained by calculating the correlation coefficient between the input group, output group, and series on the time axis 320 relating to the current intangible asset 200 and the corresponding series relating to past cases. This calculation may be performed on the series projected onto the observation space 220, or on the series of indicators 207 on the latent space 210.

[0242] In calculating the similarity rate, the setting of the population (the range of past cases to be compared) and the evaluation method (correlation coefficient, cosine similarity, Euclidean distance, etc.) affect the calculation result. This information processing method provides pre-set default values ​​for the population and evaluation method. These default values ​​may include a setting in which the group of cases included in the publicly available intangible asset information introduced in Example 6 is used as the population and Pearson's correlation coefficient is used as the evaluation method. Users may use the default values, or they may change the population or evaluation method based on their own judgment. If a user makes a change, this information processing method records the changed settings and retains them in a reproducible format.

[0243] (3-2) Conditions for generating branching events The state transition model may be configured to generate a future state branching event (state transition branch 301) when the similarity rate exceeds a predetermined threshold (for example, a first threshold) (see Figure 7). The similarity rate may be compared with the threshold as a value normalized to a range of 0 to 1. The threshold may be set as a probability value, and a calculation method similar to the next token selection probability in a language model may be used.

[0244] In addition, a second threshold may be set, and if the probability of the most likely future state occurring is greater than or equal to the second threshold (e.g., 80%), only a single future state may be displayed. If the probability of that state occurring falls below the second threshold, subsequent future states may be displayed as branching scenarios. The probability may also be calculated as a probability mass, which improves the validity of the scenario display by dispersing the probability mass and decreasing the maximum likelihood probability when multiple past cases with a high similarity rate are detected.

[0245] In other words, the first threshold controls the generation of branching events (the boundary for similarity rate), and the second threshold controls the display format of branching scenarios (the boundary for the probability of maximum likelihood occurrence). When the probability of the maximum likelihood scenario occurring falls below the second threshold, multiple branches become visible.

[0246] (3) Future state generation using external modules At least a portion of the state transition model or future state generation in this information processing method may be performed using the external module 40 defined in Example 1 (see Figure 1).

[0247] The generation of future states using the external module 40 may include the following steps:

[0248] (a) Preparation of input data This information processing method converts the current placement status of indicators 207, past update history 208, external environment information, and prediction conditions into a predetermined data format (JSON, XML, PMML, etc.) and prepares them as input data for the external module 40.

[0249] (b) Calling an external module In the information processing method, prepared input data is transmitted to the external module 40 via an API call over a network, a plug-in interface, or a batch processing interface.

[0250] (c) Receiving an output result In the information processing method, an output result including an indicator 207 representing a future state, a branch probability, a reliability 601 score, or a combination thereof is received from the external module 40. Note that the reliability 601 refers to a value obtained by converting an indicator indicating the certainty of an evaluation result, information, audit, module output, etc. into a format that can be processed by information processing (such as a scalar or vector value).

[0251] (d) Integrating output results In the information processing method, the received output result is arranged as a future state in the latent space 210 by the state representation means 100, and then subjected to the projection 222 and display processing described after (4) (see FIG. 2).

[0252] Specific examples of the external module 40 may include the following:

[0253] (i) Time series prediction service A service that receives time series data of past indicators 207 as input and predicts future indicator 207 values. For example, a service that provides time series prediction models such as ARIMA, Prophet, and LSTM.

[0254] (ii) Scenario generation service A service that receives a current state and external conditions as input and generates a plurality of future states 300 with probabilities. For example, a service that provides Monte Carlo simulation.

[0255] (iii) Risk assessment service A service that receives the state of an intangible asset 200 as input and outputs a risk score or risk scenario related to the intangible asset 200.

[0256] (iv) Market forecasting services A service that uses market environment data and the status of 200 intangible assets as input to predict future market value or market trends.

[0257] This information processing method can utilize the external module 40 based solely on the relationship between input and output, without knowing its internal structure (algorithm, training data, parameters, etc.). This configuration allows the provider of the external module 40 to provide a predictive or evaluation function to this information processing method without disclosing its internal implementation, which would be a source of competitive advantage.

[0258] Furthermore, this information processing method may call multiple external modules 40 in parallel and compare or integrate the output results obtained from each external module 40. The comparison or integration may be performed based on the framework of collaborative evaluation between multiple systems, which is detailed in Example 8.

[0259] The uncertainty (confidence interval, variance, confidence score of 601, etc.) included in the output results of the external module 40 may be presented to the user as an error band of 400 or a confidence score of 601 in the display in the observation space 220 described in (4) and later (see Figures 8 and 11). The handling of differences with a measurement resolution of less than 401 in the distance evaluation and difference comparison will be described in detail in Example 9 (see Figure 8).

[0260] (4) Projection of multidimensional branching structures and construction in observation space The aforementioned future states and branching scenarios may be generated in the latent space 210 as a multidimensional state transition structure. To facilitate understanding by the user, this multidimensional state transition structure may be projected 222 by the visualization means 101 onto a human-readable two- or three-dimensional observation space 220 based on a predetermined projection 222 rule or dimensionality reduction rule (see Figures 2 and 5).

[0261] The projection rule 222 is a rule for transforming the arrangement of the indicators 207 in the multidimensional latent space 210 into a two- or three-dimensional space that is perceptible to humans. Through this projection 222, the complex relationships in the latent space 210 are transformed into a form that can be visually grasped (see Figure 5).

[0262] Specific examples of the projection 222 rule or dimensionality reduction rule may include principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), multidimensional scaling (MDS), or the encoder portion of an autoencoder. The selection of these methods may be determined according to the number of dimensions of the latent space 210, the nature of the relationships between the indices 207 (linearity, nonlinearity, preservation of local structure, etc.), or the user's requirements. Furthermore, multiple projection 222 rules may be configured to be switchable, allowing the user to select the display format.

[0263] For example, the structure after projection 222 may be configured as a root-like structure of a tree that branches out toward the future starting from the current state, and may be represented as a tree diagram, a fishbone, or a similar structure (see Figure 7).

[0264] Here, the structure is generated as a result of projecting the multidimensional state transition structure in the latent space 210 onto the two- or three-dimensional observation space 220, and should not be understood simply as a planar tree diagram.

[0265] Furthermore, each branching path may be represented by line thickness, width, brightness, transparency, depth representation, or field of view dominance ratio, depending on the probability of the future state occurring (see Figure 7).

[0266] (5) Viewpoint control and current location display using a floating map In this embodiment, the user may determine their viewpoint using the floating map window 310 (see Figure 6). The floating map window 310 maps the current state and branching structure in the observation space 220 and may be capable of zooming in, zooming out, rotating, panning, and changing viewpoints.

[0267] This operation may provide a user experience similar to, for example, viewpoint control in Google Maps or Google Earth.

[0268] This allows users to observe future states while understanding where the current state corresponds to within a multidimensional branching structure.

[0269] In the floating map window 310, a cursor or blinking display indicating the user's current position may be placed before or at the branching point. Furthermore, multiple transition paths branching off from the branching point may be displayed as lines with thicknesses or widths corresponding to their probability of occurrence, with future states having a higher probability of occurrence being displayed in a more visible manner (see Figures 6 and 7).

[0270] In this way, users can receive support from this information processing method regarding which future states they should prioritize observing.

[0271] Furthermore, by giving depth to the root-like structure and using expressions that mimic atmospheric perspective and depth of field, the user's current position in a three-dimensional or multidimensional space can be displayed more effectively, improving their understanding.

[0272] In the aforementioned root-like structure, the axes other than the time axis 320 (two axes in the case of three dimensions, one axis in the case of two dimensions) may be arranged with asset elements (201-205) constituting the intangible asset 200, sets of elements, types of rights, or other indicators 207. These may be predetermined or may be changed based on the user's specifications.

[0273] The information processing method according to this embodiment may be configured to select the future state 300 with the highest probability of occurrence as the first display based on asset elements predetermined or selected by the user, and to display the first display so that it becomes the dominant field of view in the user's field of view. Other future states are displayed with a field of view or display ratio corresponding to their respective probability of occurrence, and these display ratios may be dynamically changed if specified by the user (see Figure 7).

[0274] (6) Providing reasons in response to a request for explanation The information processing method according to this embodiment is configured to display the reason for the branching scenario presented in response to a request from the user. The reason does not need to be displayed at all times in the initial state, and may be displayed only when the user shows interest in the branching scenario.

[0275] The reasoning statement may be generated by the information processing method, referencing the historical document group constituting the external observation function, and based on the similarity relationship with the current intangible asset 200 and rights status. Alternatively, the reasoning statement may be generated via an external information processing device or external module 40 (external API). The configuration for presenting publicly available intangible asset information to the user by quoting or referencing it in the reasoning statement will be described in detail in Example 6 (see Figure 7).

[0276] In other words, the system may use past success or failure events as a motif to show which past case the current state is similar to, and explain that a branching event (state transition branch 301) occurred because the similarity (similarity rate) exceeded a predetermined threshold.

[0277] For example, when a user performs an operation such as mouseover, long press, or right click, or when they perform actions such as gaze fixation, hand gestures, or pointing in an AR / VR environment, an explanatory window may be displayed, showing the reason why the branching scenario was generated.

[0278] This structure allows users to gain a deeper understanding of why a particular branching scenario was presented. Furthermore, this explanatory function is useful not only for evaluating intangible assets 200 and intellectual property, but also for educational purposes related to economics and management.

[0279] (7) AR / VR environment and time axis manipulation The information processing method according to this embodiment is not limited to a 2D screen environment, but may also be executed in an AR or VR environment. In an AR / VR environment, the observation space 220, branching scenario (future state 300), floating map window 310, and explanatory window are arranged in three-dimensional space and may be operated by gaze, gestures, proximity operations, etc. (see Figures 6 and 7).

[0280] Furthermore, in addition to the configuration described in Example 2, the operation of the time axis 320 may be performed using a spatial rail, a curved timeline, a jog dial, or a spatial representation of the time axis 320 (see Figure 6).

[0281] Furthermore, for visual representations during transitions to the past or future (fade out, fade in, zoom out, zoom in, etc.), the same representations as those described in Example 2 may be used.

[0282] (8) Clarification of the relationships between the examples This embodiment integrates and expands upon the concepts of Embodiments 1 and 2, as follows:

[0283] Relationship with Example 1: The concept of "latent space 210" introduced in Example 1 is described in detail in this embodiment as a multidimensional space. Furthermore, the concept of "asset elements" (201-205) in Example 1 is developed in this embodiment in relation to "asset properties".

[0284] Relationship with Example 2: In Example 2, the concept treated as a state change along the time axis 320 is generalized in this embodiment as a multidimensional state transition structure in the latent space 210. Furthermore, based on the concepts of the external observer function, reference document, and history document group introduced in Example 2, this embodiment formalizes the generation of future states by the "similarity rate" using the future state generation means 102 (see Figures 2 and 7).

[0285] Uniqueness of this embodiment: In this embodiment, by introducing an explicit transformation process of projection 222 from the latent space 210 to the observation space 220, a technical means is provided for transforming multidimensional information that is difficult for humans to directly understand into a visually comprehensible format (see Figure 5).

[0286] (Connection to subsequent embodiments) The latent space 210, the projection 222 onto the observation space 220, the state transition model based on the similarity rate, and the presentation of branching for future states 300 introduced in this embodiment are used as the basis for processing in each subsequent embodiment.

[0287] In other words, the evaluation criteria and reference space treated as fixed in this embodiment are calibrated in Embodiment 4 by the calibration means 103 according to the evaluation subject attributes and normative criteria (see Figures 2 and 9). Furthermore, the external observer function used as the reference base for relativization in this embodiment is materialized as the external observer model 104 in Embodiment 4 (see Figure 2), and the concept of similarity rate in this embodiment is applied to the similarity evaluation of the decision path in Embodiment 4. Moreover, regarding the distance evaluation on the observation space 220 after projection 222 in this embodiment, metrological concepts such as measurement resolution 401, measurement range, and error band 400 are introduced in Embodiment 9 and described in detail as significance determination of the difference based on the branching point (see Figure 8).

[0288] Furthermore, the population and state transition model used to calculate the similarity rate in this embodiment are enhanced by the publicly available intangible asset information introduced in Embodiment 6 (see Figure 7). In Embodiment 7, the state transition model is extended to a configuration that is dynamically updated in response to changes in user intent, fluctuations in the external environment, and mutual influences between entities (see Figure 7). The integration of the output of the external module 40, which performs at least part of the generation of future states, and the cooperative evaluation between multiple systems are described in detail in Embodiment 8.

[0289] Furthermore, the updating of the state in response to an external trigger and the regeneration of the future state 300 in this embodiment are described in detail in Embodiment 7 as a configuration involving the acquisition of external trigger information 500, the calculation of its importance 502, and the display of the difference 501 (see Figure 10). The generation of the future state in this embodiment is further developed in Embodiment 11 by a counterfactual virtual calculation means 700 that recalculates the state at a predetermined point in the past under different external environmental parameters (see Figures 12 and 13), in Embodiment 12 by an autoregressive influence estimation means 710 that reflects the autoregressive influence that the subject's own actions have on the preconditions for state transitions using a market influence coefficient 711 (see Figure 14), and in Embodiment 13 by a user feedback acquisition means 720 that dynamically calculates and updates the confidence level 601 based on feedback from multiple users to the output of the external module 40 (see Figure 15).

[0290] (9) Effects As described above, this embodiment provides an information processing method that holds information concerning intangible assets 200 and the rights associated therewith in a multidimensional manner in the latent space 210, and projects the latent space 210 onto the observation space 220 that is understandable to humans (see Figure 5).

[0291] This will result in the following effects:

[0292] (a) Ease of understanding multidimensional information By projecting the multidimensional latent space 210, which is difficult for humans to directly understand, onto the two- or three-dimensional observation space 220, complex relationships can be visually grasped (see Figures 5 and 6).

[0293] (b) Prioritizing branching scenarios The relative importance of multiple future states can be intuitively understood through the field of view dominance rate, line thickness, etc., corresponding to the probability of occurrence (see Figure 7).

[0294] (c) Decision support through explainability By explaining the reasons for the presented branching scenarios in terms of similarities to past cases, transparency in decision-making is ensured.

[0295] (d) Deepening understanding through perspective recognition The floating map window 310 allows users to always be aware of their location within the multidimensional branching structure, thereby supporting their understanding of the overall picture (see Figure 6).

[0296] (e) Expandability to AR / VR environments By supporting display environments that are not limited to two-dimensional screens, it becomes possible to provide a more immersive observation experience.

[0297] (f) Integration of Example 1 and Example 2 Building upon the basic concepts of Example 1 and the time-series development of Example 2, this method adds new technological value in the form of future prediction and branching prediction in multidimensional space.

[0298] Users can understand multiple branching scenarios in the future while being aware of their own perspective, and can also grasp the reasons why those branches were presented (see Figure 7). Note that the configuration of the latent space 210, the projection rules 222, the display mode, the operation method, and the specific configuration of the index 207 in this embodiment are examples, and various modifications are possible within the scope of the information processing method described in Embodiment 1. [Examples]

[0299] (Examples of calibration of evaluations based on evaluator attributes and normative criteria, regression learning of decision paths, and identification enhancement of external observer models)

[0300] The following describes yet another embodiment of the information processing method according to the present invention.

[0301] (0) Positioning of this embodiment and relationships between embodiments This embodiment is constructed based on the following embodiment.

[0302] Direct premise (required): In Example 1, the concept of market value (separated into general market value 412 and value specific to the valuation entity 413 in this example), the definition of asset elements (201-205), and the introduction of the latent space 210 are implemented (see Figures 3 and 5).

[0303] In Example 2, the concept of a standard document (extended as standard module 410 in this example), the concept of an external observer function (embodied as external observer model 104 in this example), and a time-series transition observation function are introduced.

[0304] In Example 3, the explicit definition of the latent space 210, the concept of similarity rate, the state transition model, and the projection 222 onto the observation space 220 are described in detail (see Figure 5).

[0305] Indirect premise (foundation): The asset element system (elements 1-5: symbols 201-205) in Example 1 forms the basis for evaluating the synergy effect of the valuation subject's inherent value 413 in this example (see Figure 3).

[0306] Uniqueness of this embodiment: This embodiment includes the introduction of an explicit concept called calibration, the extension of the evaluation subject attributes to include decision path attributes, predictability evaluation by regression learning of decision paths, the concretization of external observer functions into external observer model 104, the improvement of the accuracy of external observer model 104 through sequential exploration and collection, focusing on specific targets by selecting the load distribution of learning intensity, and the introduction of evaluation methods based on the substantive effects of international law and treaties, etc. (see Figures 2 and 9).

[0307] Effects on subsequent embodiments: The concepts introduced in this embodiment are further developed and applied in the following embodiments. Embodiment 5 involves the disclosure control of decision path attributes (targeting the decision path attributes introduced in this embodiment), Embodiment 6 involves the dynamic learning of the external observer model 104 (based on the sequential search and collection introduced in this embodiment), Embodiment 7 involves the application of the external observer model 104 to inter-subject influence evaluation, Embodiment 8 involves the confidence level 601 evaluation of the evaluation model (based on the calibration concept of this embodiment), Embodiment 9 involves the retrospective application of the measurement resolution 401 to the distance evaluation of this embodiment, and Embodiment 10 involves the use of evaluation subject attributes to adjust the protection investment coefficient (see Figures 8 and 11).

[0308] This embodiment is based on the calculation method for intangible assets 200 and rights indicators 207 described in Embodiment 1, the observation of time-series transitions described in Embodiment 2, and the projection 222 control from latent space 210 to observation space 220 described in Embodiment 3, and is an example in which the calibration of the evaluation criteria in the information processing method is explicitly performed by the calibration means 103 (see Figures 2 and 9).

[0309] This embodiment demonstrates that the index 207 calculated and displayed in Examples 1 to 3 can be calibrated along two axes: (i) its value to whom (dependent on the evaluator) and (ii) what criteria are used (dependent on norms). Furthermore, it includes a configuration that (iii) performs regression learning using the user's decision-making process as an attribute, and (iv) generates an external observer model 104 (a concrete manifestation of the external observer function conceptually introduced in Example 2) that identifies internal responses to third-party entities other than the user based on external observations, and allows its learning intensity to be enhanced by the user's selection (load distribution) (see Figure 2).

[0310] (1) Intangible assets as the object of measurement and the need for calibration Unlike physical quantities (length, mass, time, etc.), the value, creditworthiness, or risk of intangible assets 200 and rights are measured by which there is no absolute and unchanging standard, or the standard itself fluctuates depending on the environment and the assessor.

[0311] In the case of physical measuring instruments, calibration against known standards is essential to ensure the accuracy of measurement results. Similarly, in the valuation of intangible assets 200 and rights, comparing valuation results at different points in time or between different entities without explicitly calibrating the valuation standards will result in measurements that lack validity.

[0312] The information processing method according to this embodiment is based on the arrangement of the index 207 in the latent space 210 introduced in Embodiment 1, and when projecting the index 207 onto the observation space 220 222, the calibration means 103 performs calibration of the evaluation criteria from the following two viewpoints (see Figures 5 and 9).

[0313] (A) Calibration of market value based on the attributes of the evaluator The value of the item being evaluated is calibrated to determine "for whom" it holds value and what that value represents. This calibration allows for the calculation of the value discrepancy (Δ) 411 between the general market value 412 and the value specific to the valuation body 413 (see Figure 9).

[0314] (B) Calibration of credit and risk assessments based on normative standards The system calibrates the level of reliability or risk associated with the evaluation target, based on "what criteria." This calibration is performed as a distance evaluation from the reference module 410 (see Figure 9).

[0315] (2) Calibration of market value based on the attributes of the evaluator

[0316] (2-1) General market value and value specific to the valuation subject The information processing method according to this embodiment treats the concept referred to as market value in Embodiment 1 by more explicitly separating it into general market value 412 and value specific to the valuation subject 413 (see Figure 9).

[0317] The aforementioned general market value 412 is an evaluation value assuming an unspecified number of market participants, and is a value obtained by calculating the basic indicator 207 in Example 1.

[0318] On the other hand, the aforementioned value specific to the valuation entity 413 is a valuation value for a specific valuation entity (individual or legal entity (company or organization)) that uses the information processing method, and is a value that fluctuates depending on attributes such as the intangible assets already possessed by the valuation entity, business area, geographical conditions, and strategic policies.

[0319] This separation formalizes the dependence of market value valuation on the valuation subject, which was implicitly suggested in Example 1, as an explicit, computable concept.

[0320] (2-2) Acquisition of evaluation subject attribute information (static attributes and judgment path attributes) In this embodiment, the information processing method includes a step of acquiring attribute information of the evaluation subject. The attribute information may include at least the following static attributes.

[0321] In other words, this may include the composition of intangible assets 200 and rights already held by the valuation entity, the valuation entity's main business areas and markets, the valuation entity's geographical scope of activity, the valuation entity's strategic policy or investment policy, and the valuation entity's organizational capabilities or resources.

[0322] Furthermore, in this embodiment, the attribute information may also include the decision path formed by the user when using the information processing method (the sequence of operations such as the order, selection, reference, comparison, and weighting used in this information processing method).

[0323] In other words, path information—which information a user referenced, in what order, and by what criteria, and which candidates they adopted or rejected—can be defined as attribute information that represents the decision-making characteristics of the evaluator.

[0324] The aforementioned decision-making process may be obtained, for example, as the following log or serial data: the viewing order, time spent, focus, history of switching calibration conditions (evaluator attributes or normative criteria) for the displayed evaluation groups, selection of comparison targets, similarity search conditions, filter conditions (time range, industry, region, etc.), weighting operations for indicator 207 (value, credit, risk, etc.), generation of alternatives, history of running counterfactual simulations, and input of the final decision (adopt, hold, reject) and its reasoning.

[0325] The aforementioned decision path may be encoded as a sequence feature in the latent space 210 and represented as a decision path attribute (see Figure 5). In this way, the evaluation subject attribute is treated not only as static profile information but also as a recursive element that can change over time.

[0326] Furthermore, users may be allowed to set weighting parameters for each element constituting the judgment path attributes. This allows users to emphasize or reduce specific judgment elements (e.g., emphasis on value assessment, emphasis on risk assessment, time urgency, etc.) according to their own strategy or tactics.

[0327] (2-3) Regression learning of decision-making pathways and evaluation of predictability (long-term calibration) The information processing method according to this embodiment may evaluate the predictability of the user by using the consistency between the judgment output corresponding to the judgment path (forecast, investment judgment, partnership judgment, rights exercise policy, etc.) and the results observed after the passage of time (actual results, success or failure, profit or loss, dispute occurrence, change in credit, etc.).

[0328] Specifically, the information processing method may take the decision path at decision time t0 as input, and use the result label obtained at time t1 after a predetermined period (e.g., several weeks to several months) as a training signal or reinforcement signal to update the weights of the decision path attributes.

[0329] This makes it possible to adjust the evaluator's inherent value 413 (correction of the evaluator's evaluation criteria) and calculate the confidence level of the judgment 601 (predictability score) based on whether the user's own judgment is consistent with reality (see Figure 11).

[0330] Furthermore, since results including failures (inconsistencies) can be incorporated as learning targets to correct the user's judgment tendencies, this information processing method values ​​not only success cases but also failure cases, contributing to the improvement of long-term decision-making ability.

[0331] This configuration is based on the time-series transition observation function in Example 2, and explicitly handles the temporal relationship between the decision point and the result observation point.

[0332] Furthermore, the learning of the decision-making process is, in principle, performed using anonymized, aggregated, or concealed features, and information that could identify individual users is removed.

[0333] (2-4) Distillation know-how and metacognition through learning decision-making pathways from multiple users If the information processing method according to this embodiment is capable of accumulating and learning the decision paths of a large number of users, the information processing method may go beyond the decision paths unique to each individual user to extract highly predictable decision path patterns and model them as distilled know-how.

[0334] For example, the decision paths of a group of users with high predictability scores are clustered to form high predictability decision types (hereinafter also referred to as know-how clusters). The distance between a user's current decision path and the know-how cluster is then calculated in the latent space 210 in Example 3, and the proximity is displayed in the observation space 220 (see Figures 5 and 6).

[0335] (Integration with Example 9: Consideration of measurement resolution) Furthermore, the calculation of the distance is subject to the constraint of the measurement resolution 401, which will be detailed in the subsequent Example 9 (see Figure 8). That is, if the difference in the calculated distance is less than the measurement resolution 401, multiple know-how clusters or user decision paths may be treated as identical with "no significant difference." This suppresses excessive subdivision of decisions based on apparent minor differences.

[0336] When the distance is small, the user's judgment can be interpreted as being consistent with the distillation know-how, while when the distance is large, it can be interpreted as having a tendency to deviate.

[0337] In this case, the information processing method may, as metacognition, provide, for example, the following comparative displays: consistency (distance) with the user's decision-making process over a specific past period (e.g., the past 12 months), consistency (distance) with the majority decision-making process of the population (whole or specific segment) at the same point in time, and consistency (distance) and estimated predictability (probability) with high predictability clusters.

[0338] Here, the population may be specified not only by time range, but also segmented and compared based on conditions such as industry, region, company size, and business phase.

[0339] Furthermore, for past samples that made similar judgments, it is also possible to present a time-series analysis (judgment trajectory and outcome progression) showing how those judgments were updated over time and what results were reached.

[0340] The presentation may be presented as a result based on statistical processing (frequency, distribution, regression, etc.), or as a probabilistic estimation (probability of success, probability of risk occurrence, expected loss, etc.) using an information processing model (e.g., LLM (Large Language Models)) as mentioned in Example 2.

[0341] (2-5) Analysis of synergy effects and unique risks (calculation of value discrepancy) The information processing method according to this embodiment analyzes the synergy effects or unique risks when the intangible asset 200 or right to be evaluated is incorporated into the existing asset group of the evaluation entity, based on the arrangement relationship in the latent space 210 (see Figures 5 and 9).

[0342] Specifically, the following processes may be performed in the latent space 210.

[0343] (a) Calculation of general market value vector The evaluation target is placed in a reference space that assumes an unspecified number of market participants, and a vector V_market representing market value is calculated. This vector corresponds to the general market value of 412 (see Figure 9).

[0344] (b) Calculation of the value vector intrinsic to the evaluator The objects to be evaluated are placed in a space that takes into account the existing asset group and judgment path attributes of the evaluation entity, and a vector V_entity representing the value unique to the evaluation entity is calculated. This vector corresponds to the evaluation entity's unique value 413 (see Figure 9).

[0345] (c) Calculation of value discrepancy Both the V_market and V_entity vectors are represented on a common coordinate system (for example, a common basis in latent space 210), and the deviation Δ = ||V_market - V_entity|| (norm) between the two vectors is calculated. This deviation is displayed in observation space 220 as value deviation (Δ) 411 (see Figure 9).

[0346] In calculating the aforementioned discrepancy, it is necessary to consider the measurement resolution 401 detailed in Example 9 (see Figure 8). That is, if the calculated discrepancy is less than the measurement resolution 401, it is treated as "no significant difference" between the general market value 412 and the value specific to the valuation body 413.

[0347] If the deviation Δ exceeds a predetermined threshold and is a significant difference with a measurement resolution of 401 or higher, the information processing method may calibrate the evaluation result for the evaluation body using the calibration means 103 and present a corrected evaluation value that differs from the general market value 412 (see Figures 2 and 9).

[0348] (2-6) Specific application examples For example, consider a case where an appraisal entity that operates a retail chain evaluates the location value (a type of intangible asset 200) associated with commercial real estate in a certain area.

[0349] From the perspective of a large, unspecified number of market participants, properties located in existing commercial districts are generally considered to have a higher market value.

[0350] However, if the evaluating entity's strategy involves developing new market areas and it already has its own logistics network and existing stores nearby, then a different property located in a generally less-valued area may actually be of higher value to that entity.

[0351] This is because elements 2 (202: know-how) and 3 (203: trade secret protection ability), as defined in Example 1, generate value that is not valued in the general market when combined with the existing assets of the valuation entity (see Figure 3).

[0352] Furthermore, if the evaluation body has a history of successful or unsuccessful decision-making processes in similar site evaluations, the attributes of that decision-making process can be regressively reflected in the calibration of the current evaluation. That is, if a decision-making process similar to past inconsistencies (failures) is detected, the information processing method may display a warning and present additional comparisons (results of similar samples).

[0353] (2-7) Identification of external observer models for third-party entities (regression outside the user) The information processing method according to this embodiment, in addition to regression learning based on the user's own decision-making process, can also set a third-party entity other than the user (such as a country, company, organization, or individual) as the target of observation, and identify the entity's internal response (input / output characteristics) to a limited extent from the information expressed externally to that entity.

[0354] This configuration implements the "external observer function," which was conceptually introduced in Example 2, into a concrete computable model as the external observer model 104 (see Figure 2).

[0355] (Collection and preprocessing of input / output information) Specifically, for the entity under observation S, an externally observable output sequence O(t) is collected from official announcements, publicly available documents, behavioral history, transaction history, public events such as lawsuits and public notices, and market reactions.

[0356] Furthermore, the input sequence I(t) may be estimated as an input proxy (external conditions such as regulatory environment, capital policy, supply and demand, partnerships, and investments) based on publicly available information.

[0357] A step may be added to assign weights to the information constituting the input proxy in advance. These weights may be arbitrary (e.g., uniformly the same value) or pre-learned values ​​as initial values, and the accuracy of the external observer model 104 can be improved by regressively updating them based on subsequent results.

[0358] Furthermore, confidential information to which access permissions have been granted may be used as needed.

[0359] (Identification and construction of response models) The information processing method may involve performing an inverse transformation (inverse estimation) from the input and output based on the degree of correlation or causal consistency of multiple collected events (I(t), O(t)), and identifying a response model M_S that represents the internal processing of the subject S under observation.

[0360] The response model M_S may be held as a parametric representation on the latent space 210, or it may be configured as an External Observer Model 104 using the information processing model (e.g., LLM) mentioned in Example 2 (see Figure 2). Other methods such as Bayesian estimation and discrimination models may also be applied.

[0361] The external observer model 104 specifically implements the concept of "external observer function" introduced in Example 2, and provides a function to dynamically construct training data corresponding to past record documents (history documents) from publicly available information from third parties.

[0362] (Functionality of the response model) The response model M_S may include the following functions:

[0363] (a) Predictive function The predicted future output for the subject S is calculated. This predicted value may be accompanied by uncertainty (confidence interval, variance, confidence 601 score, etc.) (see Figure 11).

[0364] (b) Explanatory function It includes an explanatory model that outputs the feature contributions that form the basis of the prediction, allowing it to present the reasoning behind the decision to the user.

[0365] (c) Warning function If the uncertainty exceeds a predetermined threshold, the information processing method may display a recommendation to collect additional data or a warning to the user to postpone a decision.

[0366] (d) Visualization integration function The external observation instrument model 104 may be used to provide users with a function that allows for highlighting of displays (images visible to the user in the observation space 220) according to the contribution of specific information (see Figure 6). Examples of this highlighting include color, brightness, line width, blinking period, and animation speed. This function makes it easier for users to grasp the causal relationship with the information, thereby enhancing their understanding.

[0367] (3) Calibration of credit and risk assessments based on normative standards

[0368] (3-1) Introduction of standards and norms information Similar to valuation, it is necessary to clearly calibrate the measurement baseline when evaluating aspects such as creditworthiness, governance, compliance, and the appropriateness of exercising rights.

[0369] In this embodiment, standard reference information (hereinafter referred to as standard module 410) that serves as the benchmark for evaluation is introduced (see Figure 9).

[0370] The aforementioned standard module 410 is an extension of the concept of "standard document" mentioned in Example 2, and may be configured as a standard vector or standard area generated by analyzing the United Nations Charter, national laws and regulations, industry guidelines, corporate ethics codes, or codes of conduct adopted by the evaluation body itself.

[0371] (3-2) Evaluation methods based on substantive effects in international law and treaties, etc. A key technical feature of this embodiment is that, with respect to international law, the UN Charter, statements, and treaties exchanged between countries (hereinafter collectively referred to as "international law, etc."), the evaluation method emphasizes the substantive effect of how agreements between parties and related parties were handled, rather than determining whether requirements were met through formal legal interpretation methods (literal interpretation, logical interpretation, etc.).

[0372] (Technical challenges in handling international law, etc.) Regarding international law and other similar laws, interpretations that they take precedence over domestic law and interpretations that they are subordinate to domestic law may coexist, and the degree of legal binding force is often not unambiguously determined. For this reason, methods that determine whether "the requirements are met or not" or "whether or not an effect occurs" using formal legal interpretation and then calculate distance based on the results may not be appropriate in assessing substantive credibility or risk.

[0373] In this embodiment, the following evaluation criteria are introduced to address this technical challenge.

[0374] (Evaluation criteria based on actual effects) The information processing method according to this embodiment will be evaluated with respect to international law, etc., based on the following evaluation criteria.

[0375] (a) The process and basis for the formation of the promise This study analyzes the process by which the relevant international laws were established (negotiation process, agreement-making process, etc.) and the preconditions that formed the basis of those agreements (historical background, economic situation, security considerations, etc.).

[0376] (b) Legitimacy and preconditions We will assess the extent to which the promise is perceived as legitimate between the parties, and whether the preconditions for guaranteeing the promise (mutual trust, balance of interests, existence of a third-party oversight mechanism, etc.) are maintained.

[0377] (c) Discrepancy in interpretation between the parties The extent to which there is or was a discrepancy in interpretation of the wording used in the agreement between the parties is analyzed. The discrepancy in interpretation may be quantified as a distance on the latent space 210 (see Figure 5).

[0378] (d) Changes in interpretation and their temporal process This study analyzes the chronological changes in how the parties have interpreted the agreement, as well as the motivations behind those changes (political intentions, economic interests, security needs, etc.). These changes are represented by the time-series transition observation function described in Example 2.

[0379] (e) The period during which the promise was fulfilled (the period during which the actual effect was achieved) This measures the period during which the promise was effectively fulfilled, that is, the period during which the parties actually acted in accordance with the promise. It uses the period during which the actual effect was observed, rather than the formal effective period, as the basis.

[0380] (Structure of the practical effectiveness evaluation model) This information processing method constructs a model for evaluating the substantive effects of international law, etc., based on the evaluation axes (a) to (e) described above (hereinafter referred to as the substantive effect evaluation model).

[0381] The aforementioned effective performance evaluation model performs the following process.

[0382] (i) Calculation of the degree of deviation from the wording The degree of discrepancy between each wording (article, statement, etc.) used in international law and the actual actions of the parties is calculated. This degree of discrepancy may be expressed as a distance in the latent space 210 (see Figure 5).

[0383] Specifically, the obligations described in the articles are set as a reference vector, the actual actions of the parties are represented as action vectors, and the distance between the two is calculated. This distance is processed by the calibration means 103 as a deviation from the reference module 410 (see Figures 2 and 9).

[0384] (ii) Evaluation of time-series stability The degree of deviation from the aforementioned wording is evaluated as changing over time. If the degree of deviation remains stable over time (stays within a certain range), the promise is considered to be substantially kept. If the degree of deviation tends to increase over time, the credibility of the promise is considered to have decreased.

[0385] The aforementioned time-series stability may be visualized by the time-series transition observation function in Example 2.

[0386] (iii) Construction of a future prediction model A model is constructed to predict future deviations based on the degree of deviation from past wording and its time-series stability. This model may be implemented as the state transition model in Example 3 (see Figure 7).

[0387] This future prediction model estimates the future distance (degree of deviation from the wording) based on the assumption that "the promise will be similarly observed (or not observed) in the future."

[0388] (Addressing the ambiguity of the concept of trust) In this embodiment, we explicitly recognize that the term "trust" is polysemous and take the following technical measures.

[0389] Instead of treating "credibility" as a single indicator 207, it will be expressed as multiple components based on the aforementioned evaluation axes (a) to (e). The primary indicator will be the substantive effect (degree of deviation from the wording, time-series stability), and the presence or absence of formal legal effect will be a secondary indicator. Users will be able to select which evaluation axes they want to emphasize, and this will be clearly stated as a condition for calibration of the evaluation results.

[0390] (Specific application example) For example, consider the case of evaluating a bilateral investment treaty (BIT).

[0391] Formally, the agreement is assumed to have been ratified in both countries and to be in force under domestic law. However, this scenario assumes that one of the parties has amended its domestic law after the conclusion of the agreement in a manner contrary to the agreement, thereby effectively restricting the rights of foreign investors.

[0392] Under traditional formal legal interpretation methods, the agreement may be deemed valid, and investors may be considered to be protected under the agreement.

[0393] However, the actual effectiveness evaluation model in this embodiment performs the following evaluations: As a deviation from the wording, the deviation between the investment protection guaranteed by the agreement's wording and the actual application of domestic law is calculated as a distance. As time-series stability, the trend of the deviation over time after the agreement's conclusion is observed and an expanding trend is detected. As a future forecast, assuming that the expanding trend will continue, further deviations in the future are predicted.

[0394] Based on these evaluation results, this information processing method will inform users that the substantive reliability of the protection under the said agreement has decreased and will warn them of this as an investment risk.

[0395] (3-3) Distance evaluation between action vector and reference module The information processing method according to this embodiment arranges the behavioral data or rights exercise status of the entity to be evaluated (intangible asset 200 or rights holder) as behavioral vectors in the latent space 210 (see Figure 5).

[0396] Next, the distance or directional difference between the action vector and the reference module 410 is calculated (see Figure 9).

[0397] When international law or the like is involved, the reference module 410 is configured as a reference vector based on the wording of the international law or the like, and the distance is calculated as the degree of deviation from the wording detailed in (3-2) above.

[0398] In calculating the aforementioned distance, it is necessary to consider the measurement resolution 401 detailed in Example 9 (see Figure 8). That is, if the calculated distance is less than the measurement resolution 401, it is treated as "no significant discrepancy" between the action vector and the reference module 410.

[0399] If the aforementioned distance expands over time and represents a significant change with a measurement resolution of 401 or higher, or if the subject of evaluation exhibits different behaviors depending on the attributes of the trading partner (e.g., strong / weak party, transaction size), the information processing method may calculate this as credit risk or norm deviation risk.

[0400] This risk assessment, when combined with the time-series transition observation function in Example 2, can be expressed as consistency or trend in behavior from the past to the present.

[0401] (3-4) Detection of differences in behavior based on opponent attributes A particularly important function in this embodiment is the detection of cases where the object being evaluated exhibits different behaviors depending on the attributes of the other party.

[0402] For example, if a rights holder does not exercise their rights against large corporations but actively exercises them against small and medium-sized enterprises or individuals, this difference in behavior can be expressed as a dispersion or multimodality of the behavior vectors in the latent space 210 (see Figure 5).

[0403] This information processing method detects such behavioral heterogeneity and visualizes potential credit risk by evaluating it as a deviation from standard module 410 (for example, a norm that emphasizes the fairness of transactions).

[0404] Similarly, when a country exhibits different compliance attitudes towards international law, etc., depending on the attributes of the other country (economic power, military power, political influence, etc.), the aforementioned substantive effectiveness evaluation model calculates the degree of deviation for each attribute of the other country and visualizes the heterogeneity.

[0405] (4) Dynamic switching of calibration conditions, sequential search and acquisition, and control of learning intensity (load distribution)

[0406] (4-1) Switching the attributes of the evaluator The information processing method according to this embodiment may allow the evaluation subject attributes to be dynamically changed as parameters.

[0407] This redefines the "selection of evaluation body" function introduced in Example 1 as an explicit concept of calibration.

[0408] Users can perform the following operations: displaying their own company's (current evaluation entity) evaluation value, displaying another company's (hypothetical evaluation entity) evaluation value, and displaying the difference between the two (value deviation (Δ) 411) (see Figure 9).

[0409] Furthermore, in this embodiment, since the evaluation subject attribute includes the decision path attribute, a comparison of cases where the decision path differs even if the evaluation subject is the same (e.g., the difference between an exploratory path and a conservative path) may be displayed.

[0410] (4-2) Switching of normative standards Similarly, the information processing method according to this embodiment may allow the applicable reference module 410 to be dynamically changed (see Figure 9).

[0411] For example, the following options are available: evaluation based on the UN Charter, evaluation based on the laws of a specific country, evaluation based on industry guidelines, evaluation based on the company's own corporate ethics code, and evaluation based on a substantive effectiveness evaluation model (based on the degree of deviation from the wording of international law, etc., and time-series stability).

[0412] This allows users to immediately see how the credit risk or norm deviation risk being assessed changes when the applicable norms change.

[0413] In particular, by comparing and displaying evaluations based on formal legal interpretation with evaluations based on substantive effect assessment models, especially regarding international law, it is possible to visualize the discrepancy between form and substance.

[0414] (4-3) Specifying the population (time, industry, region, etc.) and displaying the distance The information processing method according to this embodiment may allow the user to specify which population to be compared.

[0415] The population may be defined by the following conditions, for example: a time range (e.g., the past 6 months, the past 3 years, before and after a specific event), industry classification (e.g., manufacturing, IT, healthcare, etc.), regional classification (e.g., by country, by prefecture, by economic zone), and attributes such as company size, stage of growth, and regulatory intensity.

[0416] The information processing method may calculate the distance between the user's current decision path and the majority decision path (or representative trajectory) of the population and display it on the observation space 220 (see Figure 6). This allows the user to understand their distance from the "general market decision at that point in time."

[0417] (4-4) Presentation of time-series changes of similar samples and probabilistic output The information processing method according to this embodiment may extract past samples (decision path, input information at the time, and subsequent results) that are similar to the user's decision path, and present the time-series changes of said samples.

[0418] For example, the period after the decision could be divided into predetermined windows (1 month, 3 months, 12 months, etc.), and the trajectory of how the sample updated its perception of the situation and what results it arrived at could be presented.

[0419] Furthermore, the presentation may be presented as statistically aggregated results (success rate, average profit / loss, variance, risk occurrence rate, etc.), or as probabilities estimated by language models, etc. (probability of future event occurrence, conditional success probability, etc.).

[0420] This function applies the time-series transition observation function from Example 2 to a set of similar cases.

[0421] (4-5) Sequential search and collection (enhanced by external observation) and learning intensity parameters The information processing method according to this embodiment may include a process of sequentially searching for, collecting, and updating input and output events related to the subject S being watched in order to improve the accuracy of the external observer model 104 described in (2-7) (see Figure 2).

[0422] Sequential exploration and collection may include, for example, expanding the scope of public information sources (medium, language, geography, time period), increasing the frequency of event extraction (shortening the collection cycle), adding external variables used to estimate the input proxy, verifying events (consistency checks, counter-searches, deduplication), and updating the model based on the collection results (parameter updates, retraining, distillation).

[0423] In this embodiment, the information processing method may allow setting parameters to control the learning intensity (such as search depth, data collection frequency, verification intensity, and update frequency). A higher learning intensity setting may consume more computing, search, and data collection resources.

[0424] This configuration enables the dynamic expansion and updating of the "set of past records" that was statically defined in Example 2.

[0425] (Controlling model updates) In the aforementioned model update, the update may be performed if the degree to which a new event contradicts the existing model exceeds a threshold. However, if the confidence level of the event is below the threshold (601), the update may be suppressed and additional data collection may be prioritized (see Figure 11).

[0426] Alternatively, the process may prioritize the search for information sources or events that maximize information gain. Here, information gain is calculated as the amount of entropy reduction or uncertainty reduction in the predictive distribution.

[0427] (4-6) Load allocation as an option for learning intensity (focusing on specific subjects) The information processing method according to this embodiment may provide the user with the option to specify a particular subject S to be observed and adjust the load distribution to improve the learning intensity of the external observer model 104 related to that subject, or to add resources that can handle a new load (see Figure 2).

[0428] Here, load balancing refers to a change in at least one of the following: for example, search depth, validation intensity, model update frequency, model capacity, or allocated computing resources. Resources can be implemented as, for example, compensation, credits, budget limits, processing priority, or additional contractual options.

[0429] Even if the resources of the information processing method provider (for-profit or public enterprise) are limited, users can choose to focus on specific targets, thereby improving the performance of the external observer model 104 with respect to those targets.

[0430] This can serve as a means to meet the specific needs of users when an entity that is a low priority for general market participants may hold high strategic importance to a particular user (e.g., a potential trading partner, competitor, or potential partner).

[0431] Furthermore, the additional resources obtained through this load allocation can be reallocated to improve the generalization performance of the 104 external observation model groups (improving the overall prediction function). This makes it possible to achieve both the resolution of individual user issues and the improvement of the overall performance of the information processing method.

[0432] (4-7) Display of Calibration Results The calibration results may be displayed on the observation space 220 as a projection 222 onto the observation space 220 (see Figures 5, 6, and 9).

[0433] Specifically, the following display methods are possible: Display the general market value 412 and the value specific to the evaluator 413 in different colors or brightness levels. Visualize the distance from the reference module 410 using dynamic representations such as "insect damage" in Example 2. Present the change in evaluation values ​​before and after calibration using the "highlighting" function mentioned in Example 2.

[0434] Furthermore, the distance between the decision-making process and the distillation know-how cluster is represented by metaphors such as "fortress walls" and "boundaries" in Example 2. The distance from the population majority is mapped and displayed to visual attributes such as color, brightness, line width, blinking period, and animation speed. The causal relationships of the information are visualized through highlighting (color, brightness, line width, blinking period, etc.) according to the contribution of the external observer model 104.

[0435] Furthermore, the time-series progression of the degree of deviation from the wording of international law, etc., is visualized using the 320-time axis representation in Example 2 (see Figure 6). Future predictions based on the substantial effect evaluation model are presented as branching scenarios (future states 300) in Example 3 (see Figure 7).

[0436] (5) Clarification of the relationships between the examples This embodiment is constructed based on the concepts of Embodiments 1 to 3, as described below.

[0437] Relationship with Example 1: In this embodiment, the concept of "market value" introduced in Example 1 is separated into "general market value 412" and "valuation subject-specific value 413," and the relationship between the two is explicitly expressed as a calibration operation (see Figure 9). Furthermore, "selection of valuation subject" in Example 1 is redefined in this embodiment as switching of valuation subject attributes and is further expanded to include judgment path attributes. The asset elements (elements 1-5: symbols 201-205) defined in Example 1 are used in this embodiment as a basis for explaining the synergistic effect of valuation subject-specific value 413 (see Figure 3).

[0438] Relationship with Example 2: The concept of a "standard document" introduced in Example 2 is extended in this embodiment as a "standard module 410," forming the basis for calibration based on standard criteria (see Figure 9). Furthermore, the "external observer function" conceptually mentioned in Example 2 is materialized in this embodiment as an external observer model 104 constructed from input and output observations by a third party (see Figure 2). The time-series transition observation function in Example 2 is used in this embodiment for time-delay alignment of decision paths and results (predictability evaluation) and for time-series presentation of similar samples.

[0439] Relationship with Example 3: The latent space 210 detailed in Example 3 is used in this embodiment to arrange value vectors, action vectors, and decision path attributes, and functions as a field for distance evaluation and cluster (distillation know-how) formation (see Figure 5). Furthermore, the projection 222 onto the observation space 220 in Example 3 is used in this embodiment as a means of visualizing calibration results and distance displays. The concept of "similarity rate" in Example 3 is applied in this embodiment to the evaluation of the similarity of decision paths and the matching of input and output patterns of the external observer model 104.

[0440] Furthermore, the distance or similarity measure used to calculate the similarity rate can be selected by the user from cosine similarity, Euclidean distance, Mahalanobis distance, Manhattan distance, correlation coefficient, kernel function-based similarity, etc., depending on the purpose (e.g., improving accuracy, improving computational efficiency).

[0441] Uniqueness of this embodiment: In this embodiment, by introducing the explicit concept of "calibration," a means to technically handle the dependency of evaluation criteria (by whom and based on what criteria). Furthermore, it is unique in that it incorporates the user's decision-making process as an attribute and regressively calibrates it based on consistency with the results, and introduces an external observer model 104 that identifies internal responses from input / output observations by a third party, making it updatable through sequential exploration and collection, and allowing the learning intensity to be controlled by load distribution selection (see Figure 2).

[0442] In particular, this embodiment introduces an evaluation method for international law and treaties that is based on substantive effects (the process of agreement formation, legitimacy, deviations in interpretation, performance record, etc.) rather than formal legal interpretation, and provides a novel technical concept not found in conventional technologies by using the degree of deviation from the wording and chronological stability as the main indicators.

[0443] This allows the system to present a set of evaluation values ​​that change depending on the selection of evaluation criteria, rather than outputting a single "correct evaluation value," and improves predictability over time for both the user and the third-party entities that are their trading partners.

[0444] Development into subsequent embodiments: The concepts introduced in this embodiment are further developed and applied in the following embodiments. Embodiment 5 deals with the disclosure control and auditing functions of decision path attributes. Embodiment 6 deals with the integration of public information as a dynamic learning base for the external observer model 104. Embodiment 7 deals with the application of the external observer model 104 to inter-entity influence evaluation. Embodiment 8 deals with the multi-layer evaluation structure as the theoretical basis for the calibration concept. Embodiment 9 deals with the explicit specification of the measurement resolution 401 in distance evaluation (retroactively applied to the distance evaluation in this embodiment) (see Figure 8). Embodiment 10 deals with the application of evaluation entity attributes to strategic defense posture optimization.

[0445] (6) Effects As described above, the following effects can be obtained according to this embodiment.

[0446] (a) Explicitly clarifying the dependence on the evaluator The value of the object being evaluated, and for whom, can be quantitatively shown as the discrepancy between its general market value 412 and the value specific to the valuation body 413 (see Figure 9).

[0447] (b) Explicit expression of norm dependence The degree of reliability of the evaluation target, "based on what criteria," can be quantitatively shown as its distance from the standard module 410 (see Figure 9).

[0448] (c) Visualization of hidden value and risks It can detect synergistic effects or unique risks specific to the evaluator that are often overlooked in general evaluations, thereby supporting decision-making.

[0449] (d) Detection of behavioral heterogeneity It can detect entities that exhibit different behaviors depending on the attributes of their trading partners and warn of potential credit risks.

[0450] (e) Calibration of predictability through regression learning of decision paths The system can acquire the user's decision-making process as an attribute and evaluate and update predictability based on its consistency with the results of time delays. This allows experiences, including failures, to be accumulated as learning resources, contributing to the improvement of long-term decision-making capabilities.

[0451] (f) Providing distillation know-how and metacognition It can extract highly predictable decision types from the decision-making processes of a large number of users and display the distance from those decisions to user decisions. Furthermore, it enables consistency comparisons with populations defined by time, industry, region, etc., presentation of time-series changes in similar samples, and presentation of statistical or probabilistic results.

[0452] (g) Construction of an external observation model involving a third party For third-party entities other than the user, an external observer model 104 can be generated to identify internal responses from externally observable inputs and outputs (see Figure 2). This improves the accuracy of predicting the behavior of trading partners, competitors, potential partners, etc.

[0453] (h) Improving the accuracy of external instrument models through sequential search and collection By dynamically searching for and collecting information about the target entity, the predictive performance of the external observation instrument model 104 can be continuously improved.

[0454] (i) Implementing focus on specific targets as a load allocation option. Under limited resources, it is possible to establish pathways that enhance the learning intensity for specific targets in accordance with the strategic needs of the user.

[0455] (j) Achieving both individual optimization and overall optimization By redirecting additional resources towards improving overall performance, it becomes possible to simultaneously address the challenges faced by individual users and enhance the predictive capabilities of the entire information processing method.

[0456] (k) Ensuring transparency in evaluation criteria By explicitly allowing the calibration conditions to be switched, it is always possible to clearly understand "what assumptions" the evaluation results are based on.

[0457] (l) Supporting strategic decision-making By virtually changing the attributes of the evaluation entity, it is possible to support strategic decisions such as "For whom is this intangible asset 200 most valuable?" and "To whom should it be sold?"

[0458] (m) Ensuring metrological rigor (integration effect with Example 9) In this embodiment, the concept of measurement resolution 401 introduced in Embodiment 9 is retrospectively applied to the distance evaluation (value deviation (Δ) 411, norm deviation, judgment path distance, etc.), thereby suppressing overinterpretation of apparent slight differences and improving the validity of the measurement (see Figure 8).

[0459] (n) Substantive evaluation of international law, treaties, etc. (unique effects of this embodiment) Regarding international law, treaties, and statements, it becomes possible to evaluate them not based on formal legal interpretation, but on substantive effects such as the process of agreement formation, legitimacy, discrepancies in interpretation, and performance. This makes the discrepancy between form and substance visible, allowing for the assessment of true credibility or risk.

[0460] In particular, by using the degree of deviation from the wording of international law and other regulations, as well as time-series stability, as primary indicators, and predicting the likelihood of future compliance, the accuracy of decision-making in international transactions, investment decisions, contract strategies, and other areas can be dramatically improved.

[0461] The information processing method according to this embodiment improves the validity of measurements in the evaluation of intangible assets 200 and rights, as well as in decision support, by adding new technical value such as calibration of evaluation criteria, regression learning of decision paths, dynamic construction of external observer model 104 (see Figure 2), and evaluation of the substantive effects of international law and treaties, on top of the technical foundation established in Examples 1 to 3. [Examples]

[0462] (Examples of disclosure control, transparency assurance, and audit functions for decision-making path attributes)

[0463] The following describes yet another embodiment of the information processing method according to the present invention.

[0464] (0) Positioning of this embodiment and relationships between embodiments This embodiment is constructed based on the following embodiment.

[0465] Direct premise (required): In Example 4, regression learning and predictability evaluation of the decision path, identification of the external observer model 104, and distance evaluation with the reference module 410 are introduced (see Figures 2 and 9).

[0466] Examples 1 to 3 detail the definition of the asset elements (201 to 205) of the intangible asset 200, the introduction of the latent space 210, the projection 222 onto the observation space 220, and the observation function of time series transitions (see Figures 3 and 5).

[0467] Indirect premise (foundation): The metaphors such as "boundary," "fortress wall," and "insect infestation" used in Example 2 are applied as means to represent the audit scope, audit reliability, and tampering risk in this example (see Figure 6).

[0468] Uniqueness of this embodiment: In this embodiment, we provide a technical means that reconciles the conflicting demands of privacy protection through anonymization and trust building through selective disclosure of sensitive information such as decision-making path attributes, through explicit selection by the user. Furthermore, it is unique in that it integrates a social verification means, such as third-party audits, into the information processing method and quantifies and visualizes the value of the audit as a distance (see Figure 11).

[0469] Effects on subsequent embodiments: The concepts of audit record 600 and confidence level 601 introduced in this embodiment are used in the confidence level evaluation in the dynamic learning of the external observer model 104 in Embodiment 6, applied to the confidence level calculation in the inter-stakeholder influence assessment in Embodiment 7, and used in the stratified confidence level calculation of the multi-layer evaluation model and the confidence level display in decision support in Embodiment 8 (see Figure 11).

[0470] This embodiment is based on the regression learning and predictability evaluation of the decision path described in Embodiment 4, and includes configurations for disclosing and controlling the attributes of the decision path, ensuring transparency, and third-party audits (see Figures 2 and 11).

[0471] In this embodiment, the system provides a function that allows users to strategically control the scope of disclosure of their decision-making pathway attributes while balancing privacy protection and trust building, and to undergo audits by third parties as needed, with the results made visible.

[0472] (1) The need to control the disclosure of judgment path attributes

[0473] As explained in Example 4, the decision-making process (operation sequence, selection history, weighting, etc.) is important attribute information that represents the user's decision-making characteristics.

[0474] The predictability score calculated from this decision-making process can function as an indicator of user reliability, but at the same time, this information carries the risk of revealing the user's strategies, thinking patterns, weaknesses, etc.

[0475] Therefore, in handling the attributes of the decision-making process, it is necessary to reconcile the following conflicting requirements.

[0476] (Request A) Privacy protection Protection of information that could identify individual users, or information that should be strategically kept confidential.

[0477] (Request B) Building trust Gaining trust from trading partners, investors, potential partners, etc., by demonstrating one's predictiveness score or judgment ability.

[0478] This embodiment technically implements these conflicting requirements as an explicit option for user-controlled disclosure.

[0479] (2) Anonymization and concealment of decision-making path attributes (default setting)

[0480] (2-1) Anonymization process as a general principle In the information processing method according to this embodiment, the learning of decision path attributes is performed, in principle, after going through the following processes.

[0481] (a) Anonymization Remove or pseudonymize identifiers that can identify an individual or organization (such as user IDs, organization names, IP addresses, etc.).

[0482] (b) Aggregation Individual decision paths are aggregated with multiple paths that have similar patterns to reduce individuality.

[0483] (c) Confidentiality We apply differential privacy, k-anonymity, or similar concealment technologies to reduce the risk of re-identifying individual users.

[0484] As a result, the extraction of distillation know-how and the provision of metacognition in Example 4 are carried out without infringing on the privacy of individual users.

[0485] (2-2) Adjustment of the degree of secrecy The degree of anonymization (for example, the ε value in differential privacy, the k value in k-anonymity) may be configurable by the user.

[0486] Increasing the degree of anonymization (decreasing ε, increasing k) enhances privacy protection, but may decrease the accuracy of the predictability score.

[0487] Reducing the degree of anonymization may improve the accuracy of the predictability score, but it could also increase the risk of re-identification.

[0488] This information processing method may present the trade-off to the user and set the anonymization parameters based on the user's choice.

[0489] (3) Selective disclosure of judgment path attributes

[0490] (3-1) Motivation and purpose of disclosure On the other hand, individuals or organizations may want to clearly demonstrate transparency regarding their own decision-making processes in order to improve the level of trust others place in them (the calculated trust score of 601) (see Figure 11).

[0491] For example, the following situations are possible: when you want to prove your investment judgment ability to investors; when you want to demonstrate your credibility to trading partners; when you want to appeal your strategic thinking ability to potential partners; and when you want to prove your compliance to regulatory authorities.

[0492] Based on these motivations, users may choose to disclose some or all of their decision-making pathway attributes to specific parties.

[0493] (3-2) Setting the scope of disclosure The information processing method according to this embodiment may provide the user with a function to set the range of decision path attributes to be disclosed.

[0494] The scope of disclosure may be defined, for example, by the following dimensions:

[0495] (a) Types of attributes Which attributes should be disclosed from among viewing order, time spent, weighting operations, and final judgment?

[0496] (b) Time range Which period of the past should the decision-making process be disclosed for (e.g., the past 6 months, the past 3 years, or the entire period)?

[0497] (c) Target area Which business areas, markets, or evaluation categories will have their decision-making processes disclosed?

[0498] (d) Aggregation level Should individual decision-making processes be disclosed, or should only aggregated statistical values ​​(mean, variance, success rate, etc.) be disclosed?

[0499] (3-3) Designation of recipient of disclosure Furthermore, the information processing method according to this embodiment may also provide a function for specifying the recipient of disclosure according to the following criteria.

[0500] (a) Individual designation A specific individual or organization is designated by an identifier.

[0501] (b) Attribute specification The recipients of the disclosure are defined based on attributes such as industry, region, company size, and credit score.

[0502] (c) Conditional disclosure Disclosure will only be made if the other party meets specific conditions (e.g., mutual disclosure, conclusion of a confidentiality agreement, presentation of audit certificates, etc.).

[0503] (3-4) Separation of learning contributions on a per-user basis The information processing method according to this embodiment may include a configuration that allows the user to choose whether or not to contribute the judgment path attributes, evaluation inputs, or other information provided by the user to the learning of the entire information processing method.

[0504] If a user chooses not to allow contributions, their information will only be used for their own specific calibration and predictions and will not contribute to improving the accuracy of other users' evaluations.

[0505] This configuration achieves both the protection of trade secrets and other confidential information, and the improvement of the overall evaluation accuracy of this information processing method.

[0506] (4) Third-party audit function

[0507] (4-1) Purpose and significance of audits When disclosing decision-making pathway attributes, it is desirable that third parties be able to verify that these attributes are accurate, have not been tampered with, and that the claimed predictability score has been properly calculated.

[0508] In this embodiment, a third-party audit function for decision path attributes and predictability scores is provided (see Figure 11).

[0509] The aforementioned audit has the following objectives: to verify the accuracy of the disclosed decision path attributes, to verify the validity of the predictability score calculation process, to detect falsification, fabrication, or selective disclosure (presentation of only favorable data), and to record the audit results and preserve the evidence.

[0510] (4-2) The entity that conducts the audit The aforementioned audit may be conducted by any of the following entities:

[0511] (a) Auditing body with legal backing Certified public accountants, audit firms, certified information security auditors, and other entities authorized to audit by law or regulation.

[0512] (b) Independent third-party organization Industry associations, non-profit organizations, or similar independent bodies.

[0513] (c) Technical Verification Services A specialized company that provides technical tools such as blockchain auditing and cryptographic verification.

[0514] (d) Self-audit Internal audits conducted by the users themselves, however, have limited independence.

[0515] This information processing method identifies the type of auditing entity and reflects its confidence level of 601 in the evaluation (see Figure 11).

[0516] (4-3) Structure of audit records In this embodiment, the audit record 600 may include the following information (see Figure 11).

[0517] (a) Audit entity identifier 602 Identification information of the entity that conducted the audit (name, authentication ID, public key, etc.).

[0518] (b) Audit date 603 The date and time (timestamp) when the audit was conducted.

[0519] (c) Identifiers of audited features An identifier that specifies the scope, period, type, etc., of the decision-making path attributes that were subject to the audit.

[0520] (d) Audit results Audit opinions such as "acceptable," "conditionally acceptable," "unacceptable," or "no opinion," as well as facts or findings discovered during the audit process.

[0521] (e) Signature information 604 for tamper detection A digital signature, hash value, or transaction identifier on the blockchain to detect tampering with the audit record 600 itself.

[0522] (4-4) Disclosure of audit records The aforementioned audit record 600 may be disclosed simultaneously with the disclosure settings for the judgment path attributes.

[0523] In other words, when a user discloses decision-making path attributes to a specific party, the corresponding audit record 600 is also disclosed, thereby ensuring the reliability of the disclosed information (see Figure 11).

[0524] (5) Assessment and visualization of audit value

[0525] (5-1) Definition of the audit value vector The information processing method according to this embodiment may define an audit value vector in order to quantitatively evaluate the value of the audit.

[0526] The aforementioned audit value vector may include, for example, the following components:

[0527] (a) Auditor confidence score A score based on factors such as legal backing, degree of independence, past audit performance, and industry reputation.

[0528] (b) Comprehensiveness of the audit scope To what extent are the audited decision-making pathway attributes comprehensive to the entire scope of disclosure?

[0529] (c) Audit depth Was the verification limited to a superficial check, or was a detailed verification (sampling, recalculation, falsification search, etc.) conducted?

[0530] (d) Novelty of the audit The elapsed time from audit date 603 to the present (the older the audit, the lower the confidence level of 601).

[0531] (e) Strength of tamper detection Tamper resistance based on the signature technology used, hash algorithm, and the decentralized nature of the blockchain.

[0532] (5-2) Distance from the standard audit vector This information processing method may also calculate the distance between the audit value vector and a predetermined standard audit vector.

[0533] The aforementioned standard audit vector, similar to standard module 410 in Example 4, represents a normative audit level and may be defined based on, for example, the following (see Figure 9): recommended audit levels in industry guidelines, audit standards required by regulatory authorities, international audit standards (ISA, etc.), and audit requirements independently set by the user.

[0534] If the aforementioned distance is small, the audit is evaluated as conforming to the standards; if the distance is large, the reliability of the audit is evaluated as limited.

[0535] (5-3) Visualization of audit value In this embodiment, the information processing method may visualize the distance between the audit value vector and the reference audit vector on the observation space 220 (see Figures 6 and 11).

[0536] Specifically, the following display methods are possible.

[0537] The confidence level of the audit (601) is indicated by color (e.g., high confidence = green, medium confidence = yellow, low confidence = red). The comprehensiveness of the audit scope is represented as the continuity or thickness of the boundary in the "boundary" and "wall" metaphors of Example 2. The recency of the audit is represented as transparency or brightness (older audits are displayed more lightly). The strength of the tamper detection is represented as the difficulty of the intrusion route in the "insect infestation" metaphor of Example 2.

[0538] (6) Treatment of value differences by the auditing body

[0539] (6-1) Differences in confidence due to differences in auditing bodies In this embodiment, the information processing method explicitly handles the fact that the confidence level of the audit (601) differs depending on the type of auditing entity (whether or not there is legal backing, the degree of independence, etc.) (see Figure 11).

[0540] For example, the following differences in evaluation are possible.

[0541] (a) Audit by a legally compliant auditing body Audits conducted by certified public accountants, audit firms, etc., are assigned a high confidence score because independence, confidentiality, and professional ethics are guaranteed in accordance with the law.

[0542] (b) Audit by an independent third-party organization Audits conducted by industry associations and similar bodies have a certain degree of independence, but their legal enforceability is limited, resulting in them being assigned a moderate confidence score.

[0543] (c) Audit by technical verification services While cryptographic verification offers strong tamper detection capabilities, its confidence score is only partial when the audit scope is limited to technical aspects.

[0544] (d) Self-audit Audits conducted by users themselves lack independence and therefore receive the lowest confidence score.

[0545] (6-2) Representation as distance The aforementioned confidence difference is expressed by the concept of "distance" introduced in Example 4 (see Figure 9).

[0546] In other words, the distance between the audit value vector and the standard audit vector changes depending on the type of auditing entity, and this distance is reflected in the presentation.

[0547] This allows users to visually understand the extent to which the disclosed decision path attributes and predictability scores are based on reliable audits (see Figure 11).

[0548] (6-3) Handling of bias in external modules and information processing models In this embodiment, the same concept as the confidence difference of the auditing entity in (6-1) and (6-2) above is also applied to the external module, the auditing entity, and the information processing model (e.g., LLM) used in this information processing method.

[0549] It is self-evident that when these entities or models make evaluations or judgments, they themselves possess inherent biases (such as biases stemming from training data, design assumptions, and limitations on their scope of application).

[0550] Therefore, this information processing method may include a function to present the following information to the user.

[0551] (a) Distance from the reference point of the external module For the external module used in Example 3, the evaluation characteristics of the external module (scope of application, area of ​​expertise, known bias tendencies, etc.) and the distance from a predetermined reference module 410 are calculated and displayed to the user (see Figure 9).

[0552] (b) Distance from the reference of the information processing model (LLM, etc.) For the information processing models (e.g., LLM) used in Examples 2 and 8, the distance between the known bias characteristics of the model (such as bias in the training data, tendency to overestimate / underestimate in specific domains, and biases due to language and culture) and a neutral standard is estimated and displayed to the user.

[0553] (c) Distance from the auditing body's standards For the auditing entities described in (6-1) and (6-2) above, the distance between the auditing entity's evaluation tendencies and normative auditing standards is shown.

[0554] The aforementioned distance may be expressed using the concept of "distance" introduced in Example 4 and visualized on the observation space 220 (see Figure 6).

[0555] This allows users to interpret the evaluation results and make appropriate decisions, after recognizing the potential biases in the external modules, information processing models, and auditing entities on which this information processing method relies.

[0556] Therefore, this configuration has the effect of improving the transparency of evaluation results, reducing the risk of users over-relying on evaluation results, and supporting autonomous decision-making.

[0557] (6-4) Metaphorical expression through facial expressions In the information processing method according to this embodiment, when displaying the confidence level 601 or the degree of deviation from the standard, an expression may be added to the object (including metaphors) placed on the observation space 220 (see Figures 6 and 11).

[0558] Prior art has shown that switching the facial expressions (demeanors) of an avatar can influence the trustworthiness perceived by the user (see Patent Document 5). Furthermore, techniques are known for generating facial expression parameters from text, audio, or video, and rendering avatars based on eye movements, mouth movements, head posture, etc. (see Patent Document 6).

[0559] In this embodiment, these prior art techniques are applied to represent the confidence level 601, audit value, or deviation from the standard in the valuation of intangible assets 200 as a metaphorical expression (see Figure 6).

[0560] Specifically, in Example 2, a facial expression is assigned to the metaphor of insect damage or other character representations according to a confidence level of 601 or a deviation level. Here, a facial expression is a visual feature composed of facial parameters such as the angle of the eyes, the angle of the mouth, the position of the eyebrows, the tilt of the entire face, and the direction of the gaze.

[0561] When expressing the differences in the reliability of auditing entities as described in (6-1) above using facial expressions, high reliability (audits by auditing entities with legal backing, etc.) may be represented by facial expressions that give a trustworthy impression (for example, a forward gaze, gentle eyes, upturned corners of the mouth, etc.), while low reliability (self-audits, etc.) may be represented by facial expressions that give an untrustworthy impression (for example, averted gaze, downturned corners of the mouth, asymmetrical eyebrows, etc.).

[0562] Similarly, the degree of bias in the external module or information processing model described in (6-3) above may be expressed as a metaphorical facial expression symbolizing the module. If the bias is large, an expression that suggests a deviation from a neutral standard (e.g., a tilted head, asymmetrical mouth, etc.) can be assigned, allowing users to intuitively receive a warning when interpreting the evaluation results of the module.

[0563] In particular, in displaying behavioral differences based on counterparty attributes detected in Example 4 (3-4), facial expressions are effective. That is, when an entity exhibits different behaviors depending on the attributes of its trading partner (e.g., strong / weak party, transaction size, etc.) (application of double standards), by assigning the metaphor of "untrustworthy facial expression" to this non-uniformity of behavior, users can intuitively recognize the credit risk in transactions with that entity.

[0564] For example, in the case of an entity that refrains from exercising its rights against large corporations while actively exercising its rights against small and medium-sized enterprises, the existence of a double standard may be visually suggested by assigning a metaphor representing that entity an expression with an unfocused gaze or an expression that gives different impressions on the surface and underneath.

[0565] In generating the aforementioned facial expression parameters, a method may be used that extracts angle information of the eyes and mouth from the landmark coordinates of the face and applies this angle information to the avatar or character representation, similar to the technique disclosed in Patent Document 6. Alternatively, a mapping function or a pre-trained model may be used that takes a confidence score or deviation as input and outputs the corresponding facial expression parameters.

[0566] These facial expression parameters are applied to a two-dimensional character image, a three-dimensional avatar model, or an animation representation, and are rendered on the observation space 220 (see Figure 6).

[0567] This configuration allows the information processing method to convert abstract numerical indicators (confidence score, deviation degree, bias level, etc.) into visual representations of facial expressions that humans can instinctively and immediately understand. As a result, users can grasp an overview of the reliability or credit risk of the subject simply by glancing at the metaphorical facial expression, without having to refer to numerical values ​​or graphs in detail, thereby reducing cognitive load and accelerating decision-making.

[0568] In this embodiment, the display using facial expressions may be used in combination with the display using color, transparency, brightness, boundary continuity, etc., as exemplified in (5-3) above, or it may be selectively switched on or off depending on the user's settings.

[0569] (7) Clarification of the relationships between the embodiments This embodiment is built upon the concept of Embodiment 4, as described below.

[0570] Relationship with Example 4: The "regression learning of decision paths" introduced in Example 4 is extended in this embodiment as a disclosure control function to balance privacy protection and transparency. Furthermore, the "predictability score" in Example 4 is positioned in this embodiment as an index 207 that can be verified by a third-party audit. In addition, the concept of the external observer model 104 in Example 4 is applied in this embodiment to the evaluation of the auditing entity (see Figure 2).

[0571] Relationship with Examples 1-3: The concepts of the latent space 210, the projection 222 onto the observation space 220, and distance evaluation constructed in Examples 1 to 3 are used in this embodiment as means of visualizing audit value (see Figures 5 and 6). In particular, the metaphors of "boundary," "wall," and "insect infestation" in Example 2 are applied in this embodiment as means of representing the audit scope, audit confidence 601, and tampering risk.

[0572] Uniqueness of this embodiment: In this embodiment, we provide a technical means that reconciles the conflicting demands of privacy protection through anonymization and trust building through selective disclosure of sensitive information such as decision-making path attributes, through explicit selection by the user. Furthermore, it is unique in that it integrates a social verification means, such as third-party audits, into the information processing method and quantifies and visualizes the value of the audit as a distance (see Figure 11).

[0573] (8) Effects As described above, the following effects can be obtained according to this embodiment.

[0574] (a) Ensuring the protection of privacy As a general rule, anonymization, aggregation, and confidentiality are implemented to protect user privacy in the learning of decision-making pathway attributes.

[0575] (b) Achieving strategic transparency When a user wishes to prove their own decision-making capacity, they can selectively disclose their decision-making process attributes while controlling the scope of disclosure and the recipients of that disclosure.

[0576] (c) Support for building trust By presenting decision-making pathway attributes and predictability scores verified by third-party audits, companies can gain the trust of trading partners, investors, potential partners, and others (see Figure 11).

[0577] (d) Visualization of audit reliability The audit value, based on the type of auditing entity, audit scope, audit depth, etc., can be quantified as a distance and presented visually (see Figure 11).

[0578] (e) Realization of tamper detection Tampering with audit records 600 can be detected using technical means such as digital signatures, hash values, and blockchain.

[0579] (f) Facilitating regulatory compliance By setting the audit standards required by regulatory authorities as the benchmark audit vector, compliance can be demonstrated.

[0580] (g) Designing incentives for disclosure Users who undergo high-reliability audits receive a higher rating for the reliability of their predictability score, creating an incentive to undergo audits.

[0581] (h) Mitigation of information asymmetry Because users with high judgment abilities can demonstrate those abilities in a verifiable way, adverse selection (where only users with low abilities remain in the market) can be prevented.

[0582] (i) Transparency of bias (unique effect of this embodiment) By visualizing the biases of external modules, information processing models (such as LLMs), and auditing entities as distances from the standard, users can appropriately interpret evaluation results and make autonomous decisions.

[0583] (j) Intuitive understanding through facial expressions (a unique effect of this embodiment) By representing a confidence score of 601 or the degree of bias as a metaphorical facial expression, users can intuitively grasp the reliability of the subject without having to refer to the numerical values ​​in detail (see Figure 11).

[0584] The information processing method according to this embodiment improves the reliability of valuing intangible assets 200 and rights by adding social, ethical, and technical values ​​such as privacy protection, transparency assurance, and third-party verification to the regression learning function of the decision path constructed in Embodiment 4 (see Figures 3 and 11). [Examples]

[0585] (Examples of acquiring, harmonizing, placing into potential space and applying to the calibration of valuation criteria for publicly available intangible asset information, as well as rights management and consideration distribution)

[0586] The following describes yet another embodiment of the information processing method according to the present invention.

[0587] This embodiment assumes the calculation method for intangible assets 200 and indicators 207 related to rights described in Example 1 (see Figure 3), the observation of time-series transitions described in Example 2, the control of projection 222 from latent space 210 to observation space 220 described in Example 3 (see Figure 5), and the calibration of evaluations based on evaluation subject attributes and normative standards described in Example 4 (see Figure 9), and shows a configuration for integrating publicly available intangible asset information into the evaluation system of this information processing method, as well as the mechanisms for rights processing, trust management, and consideration distribution in the use of said information.

[0588] In this embodiment, the concept of calibration introduced in Embodiment 4 is extended to provide a technical means for handling not only the intangible assets 200 directly owned by the user, but also knowledge about intangible assets contained in publicly available literature, case studies, or trained models, on a unified evaluation platform, while also ensuring the protection of rights holders and appropriate compensation in the provision of such knowledge (see Figures 1 and 2).

[0589] (1) Definitions of terms and relationships between examples

[0590] (1-1) Definition of publicly available intangible asset information In this embodiment, "publicly available intangible asset information" refers to any of the following types of information:

[0591] This includes descriptions of intangible assets found in literature such as books, papers, reports, and research materials; autobiographies, interview transcripts, and oral accounts by managers and experts; case studies on the creation, growth, damage, or extinction of intangible assets; structured datasets (such as patent databases and corporate financial information); and feature representations contained within trained models (e.g., LLMs) that have learned from the aforementioned information.

[0592] The aforementioned information includes not only success stories, but also failure stories, partially successful stories, or stories involving multiple contributing factors.

[0593] The aforementioned publicly available intangible asset information is reference knowledge used for evaluation and differs in nature from the external environmental information (information indicating the external conditions of the subject of evaluation) mentioned in Example 2. The aforementioned publicly available intangible asset information is obtained from the external information source 30 (see Figure 1).

[0594] (1-2) Relationship with Examples 1-5 This embodiment is connected to the existing embodiment as follows.

[0595] Relationship with Example 1: The asset elements (elements 1-5: symbols 201-205) defined in Example 1 are used as classification criteria for publicly available information in this embodiment (see Figure 3). The asset elements extracted from publicly available information are classified according to the definition in Example 1 and represented as combination state 206.

[0596] Relationship with Example 2: The concept of standard documentation introduced in Example 2 is extended in this embodiment to serve as a reference base for evaluating publicly available intangible asset information. Furthermore, the time-series transition observation function in Example 2 is used to reproduce past state changes contained in the publicly available information on the latent space 210 (see Figure 5). The external observer function in Example 2 forms the basis for the function of utilizing publicly available information as a reference base in this embodiment.

[0597] Relationship with Example 3: The latent space 210 detailed in Example 3 functions in this embodiment as a space for arranging the indicators 207 extracted from publicly available information (see Figure 5). Furthermore, the concept of similarity rate in Example 3 is applied when evaluating the similarity between publicly available examples and the user's current state.

[0598] Relationship with Example 4: The calibration concept introduced in Example 4 forms the core of this embodiment (see Figure 9). Publicly available information is treated as a set of historical documents constituting the external observer function in Example 4 and is used for calculating the intrinsic value 413 of the evaluation subject and for evaluating the distance with the reference module 410. The external observer model 104 in Example 4 forms the basis of the function that dynamically incorporates publicly available information as training data in this embodiment (see Figure 2).

[0599] Relationship with Example 5: The concept of disclosure control detailed in Example 5 is applied in this embodiment to control the scope of use and display granularity of publicly available information. Furthermore, the concepts of third-party audit function, audit record 600, and confidence level 601 introduced in Example 5 are used in this embodiment as means to verify the confidence level 601 of publicly available information, and as means to ensure transparency in rights processing and consideration calculation and distribution (see Figure 11).

[0600] (2) Acquisition of publicly available intangible asset information

[0601] (2-1) Information source This information processing method may include a step of obtaining publicly available intangible asset information from the following entities via an external information source 30 (see Figure 1).

[0602] (a) Provided by the user The user provides literature, materials, or knowledge they possess as input through the input unit 22.

[0603] (b) Provision by a third party Provision of information created by researchers, experts, data providers, etc.

[0604] (c) Provision by the provider of this information processing method The platform operator provides information that has been collected and compiled.

[0605] (d) Automatic acquisition Automatic acquisition via RPA or API integration as mentioned in Example 2 (see Figures 1 and 10).

[0606] (2-2) Format of information The aforementioned information may be in any of the following formats:

[0607] This includes descriptions in natural language (books, papers, reports, etc.), structured data (databases, spreadsheets, etc.), semi-structured data (XML, JSON, etc.), and internal representations of trained models (embedding vectors, features, etc.).

[0608] (3) Structure of the rights management organization

[0609] (3-1) Necessity of rights management When the information processing method according to this embodiment presents publicly available intangible asset information to the user by quoting or referring to it in the explanation request function or the presentation of branching reasons for future state 300 in Embodiment 3, the following rights may be involved with such information (see Figure 7).

[0610] (a) Copyright Copyrights relating to written expressions, diagrams, photographs, videos, etc., in books, papers, reports, etc.

[0611] (b) neighboring rights Rights of performers, record producers, broadcasters, etc.

[0612] (c) Rights under the Unfair Competition Prevention Act Rights relating to trade secrets, limited-access data, product labels, etc.

[0613] (d) Publicity rights The right to commercially use the names, likenesses, etc., of famous people.

[0614] (e) portrait rights The right not to have one's appearance or figure photographed or published without permission.

[0615] (f) Other intellectual property rights Intellectual property rights, such as design rights and trademark rights, that are included in publicly available information.

[0616] In this embodiment, we provide a technical means to appropriately handle the aforementioned rights, protect the interests of the rights holders, and realize effective information provision to users.

[0617] (3-2) Acquisition of rights information and assignment to metadata This information processing method may include a step of acquiring rights information related to publicly available intangible asset information when acquiring such information.

[0618] The aforementioned rights information may include the following: rights holder identification information (information identifying authors, copyright holders, performers, and other rights holders), type of right (copyright, neighboring rights, publicity rights, portrait rights, and other types of rights), scope of right (specific rights such as the right of reproduction, the right of adaptation, and the right of public transmission), license terms (the scope of use and the conditions of use), validity period of the right (copyright protection period, contractual license period, etc.), and whether or not a trust exists (the status of the conclusion of a rights trust agreement, as described later).

[0619] The aforementioned rights information is attached as metadata to publicly available intangible asset information and is managed in conjunction with confidence level 601, update date, etc.

[0620] (3-3) Determination of compliance with patent rights The information processing method according to this embodiment may include a step of determining, based on the rights information, whether the presentation of publicly available intangible asset information to a user constitutes an infringement of rights.

[0621] The aforementioned determination may include the following processes.

[0622] (a) Determining whether a quotation is appropriate Determining whether the requirements of Article 32 (Quotation) of the Copyright Act are met. Specifically, this involves confirming that the work is a published work, conforms to fair practice, is within a reasonable scope for the purpose of reporting, criticism, research, or other purposes of quotation, that the relationship between the quoted portion and the main text is clear, and that the source is clearly indicated.

[0623] (b) Limitation on the number of quoted characters Limit the number of quoted words from a single source to a predetermined upper limit (e.g., less than 15 words).

[0624] (c) Performing summarization and extraction processing If displaying the full text may infringe on rights, the work will be converted into a format that does not reproduce the essential parts of the work through summarization, keyword extraction, or structured extraction.

[0625] (d) Confirmation of publicity rights and portrait rights If the publicly available information includes the names, likenesses, etc., of famous people, we will confirm that the use of such information is not solely for the purpose of exploiting the customer-attracting power of those famous people.

[0626] (e) Confirmation of whether it constitutes a trade secret. We will confirm that the publicly available information does not constitute a trade secret under the Unfair Competition Prevention Act. We will refrain from disclosing information that may potentially constitute a trade secret.

[0627] If the determination process determines that the risk of infringement exceeds a predetermined threshold, the information processing method may suppress the presentation of the information or display a warning to the user on the display unit 21 (see Figure 1).

[0628] (3-4) Implementation of rights-compliant information presentation The information processing method according to this embodiment may include a step of presenting information in one of the following ways based on the result of the patent compliance determination.

[0629] (a) Lawful methods of citation Quoting in a form that meets the requirements of Article 32 of the Copyright Act. This includes ensuring clear indication of the source, clarification of the quoted portion, and maintenance of the principal-subordinate relationship.

[0630] (b) Summary presentation method The original text is summarized and presented in a format that does not reproduce the essential parts of the copyrighted work. The summary is generated by this information processing method or an external summarization model.

[0631] (c) Structured information presentation method This structured data presents only factual information (dates, amounts, names of people, places, etc.) extracted from publicly available sources. It does not include creative expressions from copyrighted works.

[0632] (d) Link reference method Instead of directly presenting the content of publicly available information, only the location of the information (URL, bibliographic information, etc.) will be provided, allowing users to refer to the original source.

[0633] (e) Full text presentation method of entrusted information Based on the rights trust agreement described later, we will present the full text or a detailed extract of the information for which we have received permission to use from the rights holder.

[0634] The selection of the presentation method may be automatically determined based on rights information, license terms, and user settings (such as requirements for level of detail), or it may be manually selected by the user from the input unit 22 (see Figure 1).

[0635] (4) Rights trust agreement and trust management organization

[0636] (4-1) Definition of a rights trust agreement In this embodiment, "rights trust agreement" means an agreement in which a rights holder (copyright holder, publicity rights holder, etc.) relating to publicly available intangible asset information entrusts the management of said rights to the provider of this information processing method or a designated trust management entity.

[0637] The aforementioned rights trust agreement may include the following clauses: the purpose of the trust (that the rights holder's copyrighted works, portraits, and other intellectual property will contribute to the development of industry in society, the sharing of knowledge, the valuation of failure cases, etc., by being provided to users through this information processing method); the trust property (the scope and content of the rights subject to the trust); the scope of the license (the form of use, purpose of use, and scope of users in this information processing method); the method of calculating the consideration (the method of calculating the consideration based on actual use); the method of distributing the consideration (the method of distributing the calculated consideration to the rights holder, the distribution cycle, the minimum payment amount, etc.); the trust fee (the method of calculating the trust fee received by the trust administrator); and the conditions for termination of the contract (the conditions for termination of the contract by the rights holder or the trust administrator).

[0638] The aforementioned rights trust agreement may be concluded individually between the rights holder and the trust administrator, or it may be presented as standard contract terms and conditions and established upon the consent of the rights holder.

[0639] (4-2) Functions of the trust administrator In this embodiment, the trust administrator has the following functions.

[0640] (a) Registration and management of rights holders This system registers and manages the rights holder's identification information, contact details, rights details, and license terms.

[0641] (b) Rights clearance for publicly available information Regarding the publicly available intangible asset information provided through this information processing method, the permission status of the rights holder will be confirmed and registered as usable information.

[0642] (c) Records of actual usage The actual usage of publicly available information in this information processing method (viewing, citation, evaluation, etc.) is recorded as update history 208 (see Figure 3).

[0643] (d) Calculation and distribution of consideration The compensation will be calculated based on actual usage and distributed to the rights holders.

[0644] (e) Reporting to rights holders The rights holders will be regularly informed of the actual usage, the basis for calculating the compensation, and the amount of distribution.

[0645] The aforementioned trust administrator may be the same as the provider of the information processing method, or it may be established as an independent third-party organization.

[0646] (4-3) Management of rights trust information The information processing method according to this embodiment may include a step of managing information based on a rights trust agreement in the storage unit 12 as metadata for publicly available intangible asset information (see Figure 1).

[0647] The aforementioned rights trust information may include the following: a trust flag (a flag indicating whether or not the information is subject to a rights trust agreement), rights holder identification information (identification information of the rights holder who entered into the trust agreement), usage permission level (level of permitted usage, such as summary only, extraction only, full text display allowed, etc.), fee calculation method (fee calculation method such as viewing fee, citation fee, or fixed amount), and contract expiration date (expiration date of the rights trust agreement).

[0648] The aforementioned rights trust information is treated as subject to disclosure control in Example 5 and is not disclosed to any entity other than the rights holder.

[0649] (5) Recording of actual usage and calculation of fees

[0650] (5-1) Records of actual usage The information processing method according to this embodiment may include a step of recording the usage status as an update history 208 when a user views, quotes, or evaluates publicly available intangible asset information (see Figure 3).

[0651] The record of usage may include the following: date and time of use (date and time the information was used, timestamp), user identification information (user identification information, anonymized ID, etc.), usage type (usage type such as viewing, quoting, summarizing, displaying the full text, inputting evaluations, etc.), usage amount (usage amount such as viewing time, number of quoted characters, number of displays, etc.), purpose of use (purpose of use such as evaluation, comparison, explanation, learning, etc., which can be specified by the user), and information identifier (identifier of the publicly available intangible asset information used).

[0652] The records of the aforementioned usage patterns are treated as part of the decision-making path attributes in Example 5, and personally identifiable information is anonymized or aggregated to protect privacy.

[0653] (5-2) Calculation of consideration The information processing method according to this embodiment may include a step of calculating the consideration to be paid to the rights holder based on the record of actual usage.

[0654] The aforementioned consideration may be calculated by any of the following methods:

[0655] (a) Per-use pricing method The cost is calculated by multiplying the number of uses (such as views and citations) by the unit price.

[0656] (b) Time-based pricing The calculation is done by multiplying the viewing time, usage time, etc., by the unit price.

[0657] (c) Price per character The calculation is done by multiplying the number of quoted characters by the unit price.

[0658] (d) Evaluation-linked method Based on user evaluations (star ratings, usefulness ratings, etc.), information that receives a high rating will be compensated with a higher price.

[0659] (e) Fixed amount method A fixed fee will be calculated at regular intervals, regardless of actual usage.

[0660] (f) Combination method The calculation is performed by combining several of the above (a) to (e).

[0661] The aforementioned calculation method is determined in advance in the rights trust agreement.

[0662] (5-3) Aggregation and distribution of consideration This information processing method may include a step of aggregating the compensation for each rights holder at predetermined intervals (e.g., monthly, quarterly, etc.) and distributing it to the rights holders.

[0663] The aforementioned distribution may be carried out in accordance with the following procedure: setting the aggregation period (setting the period for aggregating the consideration), calculating the consideration for each rights holder (calculating the consideration for each rights holder based on the calculation method in (5-2) above), deducting the trust fee (deducting the trust fee received by the trust administrator from the calculated consideration), confirming the minimum payment amount (confirming whether the consideration after deduction exceeds the contractual minimum payment amount. If it falls below the minimum payment amount, it will be carried over to the next period), executing the payment (executing the payment to the rights holder by bank transfer to the designated account, electronic payment, etc.), and sending the payment details (sending the rights holder a payment details that include details of the actual usage, the basis for calculating the consideration, the breakdown of the trust fee, etc.).

[0664] The distribution process is recorded in a format that allows for tamper detection using a timestamp, digital signature, etc., similar to the audit record 600 in Example 5 (see Figure 11).

[0665] (6) Priority presentation of information that has been placed in trust

[0666] (6-1) Setting the presentation priority In this embodiment, the information processing method may set a higher presentation priority for publicly available intangible asset information based on a rights trust agreement compared to information that is not in trust.

[0667] The following effects can be obtained by setting the aforementioned presentation priority.

[0668] (a) Improving the value provided to users Since the entrusted information can be displayed in full or extracted in detail, it becomes possible to provide users with more detailed information.

[0669] (b) Facilitating the return of compensation to rights holders Prioritizing the presentation of information already in trust will increase usage and facilitate the return of compensation to rights holders.

[0670] (c) Creating incentives for providing information For rights holders, entrusting their information creates an incentive to receive compensation, leading to the integration of more useful information into this information processing method.

[0671] (6-2) Differentiation of presentation methods This information processing method may differentiate between trusted information and non-trusted information on the observation space 220 using the following display methods (see Figure 6).

[0672] (a) Display of Trusted Badge For information that has been placed in trust, badges such as "Placed in Trust" and "Details Available" will be displayed.

[0673] (b) Display of level of detail For information that has been placed in trust, the level of detail is indicated as "Full text available," while for information that has not been placed in trust, it is indicated as "Summary only," etc.

[0674] (c) Differentiation of color tones Trusted information will be differentiated by color, such as green for trusted information and gray for non-trusted information.

[0675] (d) Adjusting transparency Similar to the visualization of confidence level 601 in Example 5, the transparency of the trusted information is set high (opaque), and the transparency of the non-trusted information is set low (transparent) (see Figure 11).

[0676] This differentiation allows users to visually understand which information is available in more detail.

[0677] (7) Reporting to rights holders and ensuring transparency

[0678] (7-1) Usage Report The information processing method according to this embodiment may include a step of reporting to the rights holder the actual usage status of their copyrighted works, etc.

[0679] For example, the report may include the following: trends in usage (trends in the number of views, citations, etc., over time, displayed using the time-series transition observation function in Example 2), distribution of user attributes (distribution of user attributes such as industry, region, and company size, anonymized aggregated information), analysis of usage context (in what evaluation context it was used), aggregated evaluations (aggregated user evaluations), and basis for calculating compensation (details of the basis for calculating compensation based on actual usage) (see Figure 6).

[0680] The aforementioned reports may be provided in real time or periodically through a dashboard exclusively for rights holders.

[0681] (7-2) Ensuring Transparency The information processing method according to this embodiment may provide the following functions in order to ensure transparency in the calculation and distribution of consideration.

[0682] (a) Disclosure of calculation logic The method for calculating the consideration, the basis for setting the unit price, etc., will be disclosed to the rights holder.

[0683] (b) Verifiability of actual usage Rights holders will be able to verify the actual usage of their copyrighted works through log records, etc. However, the privacy of individual users will be protected.

[0684] (c) Provision of auditing functions By applying the third-party audit function from Example 5, it becomes possible to verify the calculation of consideration by an auditing body designated by the rights holder (see Figure 11).

[0685] (d) Acceptance of objections A contact point will be established for complaints regarding the calculation of compensation, and a recalculation or explanation will be provided based on the complaint.

[0686] Ensuring the aforementioned transparency will gain the trust of rights holders and create an incentive for more rights holders to enter into rights trust agreements.

[0687] (8) Social effects and contributions to industrial development

[0688] (8-1) Valuing Failure Cases The mechanisms for rights management and compensation distribution in this embodiment have significant social effects, particularly in valuing failure cases.

[0689] Traditionally, failure stories have been less likely to be shared because disclosing them carries significant reputational risks for those involved, and no compensation is usually offered.

[0690] In this embodiment, the sharing of failure cases is promoted as follows.

[0691] (a) Provision of consideration Even in cases of failure, compensation is distributed to rights holders based on actual usage, creating an incentive for disclosure.

[0692] (b) Anonymization options At the request of the rights holder, it is possible to publish the work anonymously, while keeping the author's name confidential. This allows for reduced reputational risk while still receiving compensation.

[0693] (c) Providing opportunities to learn from failures By extracting 302 failure transition patterns, failure cases can be systematically reused and will prove valuable as a learning resource for society as a whole (see Figure 7).

[0694] By valuing the aforementioned failure cases, it becomes possible to prevent similar failures from being repeated and contribute to the development of industry in society.

[0695] (8-2) Generalization of knowledge The rights management mechanism in this embodiment also contributes to the generalization of knowledge.

[0696] Traditionally, knowledge possessed only by experts (such as know-how for success and lessons learned from failures) has been difficult for a wide range of users to utilize, even when published in the form of books, papers, etc., due to access barriers (price, difficulty of obtaining, etc.).

[0697] In this embodiment, knowledge generalization is achieved as follows.

[0698] (a) Low-cost access Users can access the necessary knowledge through this information processing method. Since the fee is calculated based on actual usage, costs are incurred only to the extent necessary.

[0699] (b) Distillation of knowledge In Example 4, the distillation know-how extracted from publicly available information is generalized by the calibration means 103 and made available to a wide range of users (see Figures 2 and 9).

[0700] (c) Integration of knowledge Knowledge from different sources is integrated into the same latent space 210, making comparison and referencing easier (see Figure 5).

[0701] The generalization of the aforementioned knowledge will lower barriers to entry in knowledge-intensive industries (IT, biotechnology, consulting, etc.), promote competition, and accelerate the overall development of the industry.

[0702] (8-3) Enhancing the creative motivation of rights holders Appropriate compensation and rebates will boost the creative motivation of rights holders, creating a virtuous cycle in which more useful knowledge is generated and made public.

[0703] The aforementioned contribution to industrial development is one of the important social significances of this invention.

[0704] (9) Preprocessing and harmonization

[0705] (9-1) Purpose of harmonization If the publicly available information differs from the representation format of the latent space 210, the information processing method includes a harmonization step to convert the information into a format that can be placed in the latent space 210 (see Figure 5).

[0706] The objectives of the aforementioned harmonization are as follows: to enable comparison of information of different forms and scales on a common latent space 210; to establish a correspondence with the asset element system (201-205) of the intangible asset 200 defined in Example 1; and to convert the information into a format that can be supplied to the calibration process in Example 4 (see Figures 3 and 9).

[0707] (9-2) Methods of harmonization The aforementioned harmonization may include one or more of the following processes in the state representation means 100 (see Figure 2).

[0708] (a) Factor extraction Factors related to the creation and deterioration of intangible assets were extracted from natural language literature and mapped to the asset elements (elements 1-5: symbols 201-205) in Example 1 (see Figure 3).

[0709] (b) Time series The temporal progression is extracted from the case description and converted into a representation on the time axis 320 in Example 2 (see Figures 6 and 7).

[0710] (c) Extraction of causal structure The causal relationships between the factors are extracted and represented as the combination state 206 in Example 1 (see Figure 3).

[0711] (d) Feature quantity Using a pre-trained model (e.g., LLM), the literature description is converted into features in the latent space 210. The information processing model mentioned in Example 2 may also be used for this purpose.

[0712] (e) Normalization of the scale This process converts information described using different scales and units into coordinates on a common reference space.

[0713] (f) Adjustment with the eigencoordinate system of the evaluation body The publicly available information is projected onto the coordinate system of the intrinsic value 413 of the evaluation entity defined in Example 4 (see Figures 5 and 9).

[0714] (9-3) Adding metadata In this embodiment, the following metadata may be attached to the harmonization result.

[0715] (a) Source identifier Source information such as the title of the document, author, publication year, and URL.

[0716] (b) At the time of update The time when the information was written or acquired. This is recorded as update history 208 (see Figure 3).

[0717] (c) Reliability An index indicating the reliability of the information source, the consistency of the information, or the uncertainty in the harmonization process. The confidence score 601 may be calculated using the same method as the uncertainty evaluation of the external observer model 104 in Example 4 (see Figures 2 and 11).

[0718] (d) Difficulty level of harmonization An indicator that shows whether or not there is missing information, the degree of ambiguity, etc.

[0719] (e) Rights trust information The rights trust information as defined in (4-3) above.

[0720] The metadata may be reflected on the observation space 220 as visual attributes such as line thickness, transparency, hue, and brightness in the display process described later (see Figure 6).

[0721] (10) Placement into latent space

[0722] (10-1) Principles of Placement The publicly available information after the harmonization is placed in the latent space 210, as detailed in Example 3, by the state representation means 100 (see Figures 2 and 5).

[0723] The important points in this embodiment are as follows:

[0724] (a) Neutral arrangement The labels of success and failure do not affect the arrangement itself in the latent space 210. The information is arranged neutrally based on the extracted asset elements (201-205) and the combination state 206 (see Figure 3).

[0725] (b) Elimination of criterion dependence The arrangement in the latent space 210 does not presuppose any specific criteria (norms, targets, etc.). The evaluation is calculated in a subsequent calibration process as the distance to the reference module 410 (see Figure 9).

[0726] (c) Integration with the eigenspace of the evaluator Publicly available information is placed in the same latent space 210 as the intangible assets 200 directly owned by the user. This makes it possible to compare and cross-reference the two (see Figure 5).

[0727] (10-2) Implementation of the layout The aforementioned configuration may be implemented in any of the following forms: a state vector representation (coordinates in a multidimensional space), a graph structure (a network representing the relationships between asset elements), a state transition matrix (a probabilistic representation representing changes over time), or a combination thereof.

[0728] (11) Calibration process for evaluation criteria

[0729] (11-1) The role of calibration The calibration process in this embodiment is a direct application of the concept introduced in Embodiment 4 and is performed by the calibration means 103 (see Figures 2 and 9).

[0730] The publicly available intangible asset information is supplied to the following calibration process described in Example 4.

[0731] (a) Calibration of market value based on the attributes of the evaluator The possibility of combining publicly available case studies with the valuation entity's existing asset portfolio is evaluated, and the valuation entity's unique value of 413 is calculated (see Figure 9).

[0732] (b) Calibration of credit and risk assessments based on normative standards The distance between the behavioral patterns in the publicly available cases and the standard module 410 is evaluated to calculate credibility or risk (see Figure 9).

[0733] (11-2) Public information as an external observation instrument function In this embodiment, the publicly available intangible asset information is treated as a group of historical documents that constitute the external observer function in Embodiments 2 and 4, and is input into the external observer model 104 (see Figure 2).

[0734] Specifically, the following processes will be performed.

[0735] (a) Calculation of the similarity ratio The similarity ratio defined in Example 3 is used to evaluate the similarity between the user's current state and the published example.

[0736] (b) Construction of a state transition model The state transition model in Example 3 is reinforced by the future state generation means 102 using time-series information included in the publicly available examples (see Figure 2).

[0737] (c) Support for generating branching scenarios Refer to the decision-making branching points in the published examples and use them to generate the state transition branch 301 of the future state 300 in Example 3 (see Figure 7).

[0738] (11-3) Evaluation by standard module The publicly available information is supplied to the reference module 410 defined in Example 4, and the following evaluation is performed (see Figure 9).

[0739] (a) Calculation of distance from the reference point The distance between the state or behavioral patterns of intangible assets 200 in publicly available examples and standard module 410 (standard and normative documents such as the UN Charter, national laws and regulations, and industry guidelines) is calculated.

[0740] (b) Generation of evaluation results Based on the aforementioned distance, it is determined whether the published case is "consistent" or "deviant" with respect to the criteria. It is important to note that this determination is generated not as a label attached to the input information itself, but as a relative relationship with the criteria module 410.

[0741] (c) Reflection in the value specific to the evaluator Using the aforementioned evaluation results, we estimate the change in the intrinsic value of the evaluator 413 when a user adopts a strategy similar to the publicly available example (see Figure 9).

[0742] (12) Use of information after correction

[0743] (12-1) Application to future prediction The calibrated public information may be used by the future state generation means 102 to generate the future state 300 in Example 3 (see Figures 2 and 7).

[0744] Specifically, the following processes can be considered: Refer to the state transition patterns in the publicly available examples to correct the probability of the user's future states occurring, and refer to the branching points in the publicly available examples to present the decision-making options the user may face as state transition branch 301 (see Figure 7).

[0745] (12-2) Comparison display This information processing method may also compare and display the user's current status and publicly available examples on the observation space 220 in Example 3 using the visualization means 101 (see Figures 2 and 6).

[0746] In the comparative display described above, the following visual distinctions may be used: distinguish between user-specific information and publicly available information by line type (solid / dashed), line thickness, and color tone; express the confidence level of publicly available information (metadata in (9-3) above) by transparency; express the elapsed time since the information was updated by brightness; and highlight trusted information by a trusted badge.

[0747] (12-3) Integration with explanatory functions and legally appropriate presentation This information processing method may be integrated with the explanation request function introduced in Example 3.

[0748] If a user requests an explanation for state transition branch 301, this information processing method may present the publicly available examples referenced by the scenario and explain the similarity (similarity rate) (see Figure 7).

[0749] In this case, the following process is performed in accordance with the implementation of rights-compliant information presentation described in (3-4) above.

[0750] (a) Performing a conformity assessment For the publicly available cases presented, we will assess the risk of rights infringement.

[0751] (b) Selection of presentation method Based on the assessment results, one of the following methods will be selected: legal citation method, summary presentation method, structured information presentation method, link reference method, or full text presentation of entrusted information method.

[0752] (c) Attribution of source In accordance with Article 32 of the Copyright Act, the source of the quoted material is clearly indicated.

[0753] (d) Records of actual usage The usage record described in (5-1) above is executed as update history 208 and used as the basis for calculating the consideration (see Figure 3).

[0754] Through the aforementioned rights-compliant presentation, useful information can be provided to users without infringing on the rights of the rights holders.

[0755] (13) Reconstruction of historical conditions and counterfactual analysis

[0756] (13-1) Recreating the state at a past point in time In this embodiment, if the publicly available information describes the state at a specific point in the past, the information processing method may use that point in time as an initial condition and generate the future state 300 in Embodiment 3 using the future state generation means 102 (see Figures 2 and 7).

[0757] This allows for the following analysis.

[0758] (a) Historical verification We will compare the actual results from publicly available case studies with the prediction results obtained using this information processing method to verify the prediction accuracy.

[0759] (b) Counterfactual analysis We assume alternative options that were not adopted in the published examples and explore the possibility of different outcomes as state transition branches 301 (see Figure 7).

[0760] (13-2) Reuse as training data The results of the counterfactual analysis described above may be reused for the following purposes: to be stored as training data for the external observer model 104 in Example 4 (see Figure 2), to be used to improve the accuracy of the state transition model in Example 3, and to be used as the target for extracting distillation know-how in Example 4.

[0761] (14) User input of evaluations and updating of reliability levels

[0762] (14-1) Evaluation input function In this embodiment, the information processing method may also provide a function that allows the user to input an evaluation of publicly available information from the input unit 22 (see Figure 1).

[0763] The aforementioned evaluation input may include: expressions of agreement / doubt, provision of comments, and evaluations of the user's contribution or usefulness to the results (star ratings, numerical ratings, etc.).

[0764] (14-2) Reliability update The aforementioned evaluation input may also be used when updating the confidence level of the publicly available information (metadata in (9-3) above) (see Figure 11).

[0765] Specifically, the following processes are possible.

[0766] (a) Confidence update based on aggregation We will collect evaluations from multiple users and update the confidence score (601) of the publicly available information.

[0767] (b) Weighting based on predictability In Example 4, evaluations from users with a high predictability score are given higher weight.

[0768] (c) Calculation of provider's contribution We will compile evaluations of publicly available information provided by third-party providers and calculate the contribution level of those providers.

[0769] (d) Reflection in compensation Based on the evaluation-linked system described in (5-2) above, higher compensation will be calculated for the rights holders of information that receives a high evaluation.

[0770] (14-3) Visualization of evaluation The evaluation input and confidence score 601 may be visualized by the visualization means 101 on the observation space 220 detailed in Example 1 (see Figures 2 and 6).

[0771] For example, the following representations are possible: displaying the distribution of ratings as a heatmap; representing highly-rated public information as a stronger structure in the "fortress wall" metaphor of Example 2; and lowering the display priority of poorly-rated public information by increasing its transparency.

[0772] (15) Control of disclosed particle size

[0773] (15-1) Definition of Disclosure Levels In this embodiment, the information processing method may also provide a function to control the granularity of disclosure of publicly available information.

[0774] The aforementioned disclosure granularity may be defined at the following levels:

[0775] (a) Overview level Only summaries or key findings extracted from publicly available information are displayed.

[0776] (b) level of distillation This section displays the generalized knowledge extracted as distillation know-how in Example 4.

[0777] (c) Level of detail (only information already in trust) This displays the original or detailed description of publicly available information. Only information based on rights trust agreements is included.

[0778] (d) Factor level Only the factors extracted from publicly available information (asset elements 201-205 in Example 1) are displayed, and specific descriptions are hidden (see Figure 3).

[0779] (e) Anonymization level The source of the information is concealed, and only the 207 derived indicators are displayed.

[0780] (15-2) User selection The disclosed granularity may be dynamically selectable by the user from the input unit 22 (see Figure 1).

[0781] This offers the following benefits: Reduce cognitive load by displaying only an overview when detailed information is not needed; gain deeper insights by selecting the level of detail of the trusted information when detailed analysis is required; and utilize knowledge while protecting privacy by selecting the level of anonymization when the confidentiality of the source needs to be protected.

[0782] (15-3) Classification of information presentation formats based on whether or not compensation is involved The information processing method according to this embodiment may include a step of separating the form of information presentation depending on whether or not the presentation of publicly available intangible asset information to the user involves compensation to the rights holder.

[0783] Forms that do not involve compensation include the presentation of statistical trends, generalized insights, or anonymized aggregate indicators generated as a result of this information processing method learning and generalizing publicly available information.

[0784] Forms that involve the payment of consideration include quoting, summarizing, or detailing specific publicly available information, clearly indicating the source of such publicly available information, or providing the full text based on a rights trust agreement.

[0785] This separation ensures that the rights of rights holders are properly protected, and users can choose the level of detail of the information while being aware of whether or not there is a fee involved.

[0786] (16) Providing information to external systems

[0787] (16-1) API provided In this embodiment, calibrated public information or evaluation results based on such information may be provided to an external system via an API (Application Programming Interface) through the communication interface unit 13 (see Figure 1).

[0788] The API may provide the following information: a summary of publicly available examples for a specific intangible asset category, a list of publicly available examples similar to the user's current status, the results of future statuses based on the publicly available examples, and a flag for rights-cleared information (trusted information) (see Figure 7).

[0789] (16-2) Consideration of regression effects caused by external entities When an external entity uses the output of this information processing method to make a decision, a feedback loop may occur where that decision affects the entire market, and consequently, also affects the evaluation results of this information processing method.

[0790] In this embodiment, when considering the regression effect, the information processing method may estimate the impact of providing information to an external system on the market and reflect this in the evaluation results. This reflection may also take importance into account (see Figure 10).

[0791] (17) State update via external trigger

[0792] In this embodiment, updates, additions, or deletions of publicly available intangible asset information are treated as external trigger information 500 and may be integrated into the state update mechanism triggered by an external trigger, as detailed in Embodiment 7 (see Figures 4 and 10).

[0793] Specifically, the following processes will be performed.

[0794] (a) Obtaining external trigger information The publication of new public information, the updating of existing information, or the deletion of information are obtained from the external information source 30 as external trigger information 500 (see Figures 1 and 10).

[0795] (b) Assessment of importance For the acquired external trigger information 500, the importance 502 to the intangible asset 200 or right to be evaluated is assessed (see Figure 10).

[0796] (c) Condition judgment If importance level 502 exceeds a predetermined threshold, this information processing method updates the coordinate values ​​of the related index 207 in the latent space 210.

[0797] (d) Generating and displaying differences The difference of 501 between the indicator 207 before and after the update is calculated and displayed on the observation space 220 by the visualization means 101 (see Figures 2 and 10).

[0798] This integration ensures that dynamic updates to publicly available information are reflected in real time, improving the overall evaluation accuracy of this information processing method.

[0799] (18) Clarification of the relationships between the examples This embodiment integrates and expands upon the concepts of Examples 1 to 5, as described below.

[0800] Relationship with Example 1: The asset element system (201-205) defined in Example 1 is used as the criteria for classifying and aligning publicly available information in this embodiment (see Figure 3). Furthermore, the concept of latent space 210 in Example 1 is extended in this embodiment to represent the space in which publicly available information is placed (see Figure 5).

[0801] Relationship with Example 2: The concept of standard documents introduced in Example 2 is extended in this embodiment to serve as a reference base for evaluating publicly available intangible asset information. Furthermore, the time-series transition observation function in Example 2 is used in this embodiment to reproduce past state changes in publicly available examples. The external observation function in Example 2 forms the basis for utilizing publicly available information as a historical document set in this embodiment.

[0802] Relationship with Example 3: The latent space 210 detailed in Example 3 functions in this embodiment as a space for uniformly arranging publicly available information (see Figure 5). Furthermore, the similarity rate and state transition model in Example 3 are applied in this embodiment to evaluate the similarity between publicly available examples and the user's current state and to predict future trends.

[0803] Relationship with Example 4: The calibration concept introduced in Example 4 forms the core of this embodiment (see Figure 9). Publicly available information is treated as a set of historical documents constituting the external observer function in Example 4 and is used for calculating the intrinsic value 413 of the evaluator and for evaluating the distance from the reference module 410. Furthermore, the regression learning of the decision path in Example 4 is applied in this embodiment to extract decision-making patterns in publicly available cases. The external observer model 104 in Example 4 forms the basis of the function that dynamically learns publicly available information in this embodiment (see Figure 2).

[0804] Relationship with Example 5: The concept of disclosure control detailed in Example 5 is applied in this embodiment to control the scope of use and display granularity of publicly available information. Furthermore, the audit function in Example 5 can be used in this embodiment as a means to verify the confidence level 601 of publicly available information, and as a means to ensure transparency in the calculation and distribution of consideration (see Figure 11).

[0805] Uniqueness of this embodiment: In this embodiment, we provide a technical means for handling not only intangible assets 200 directly owned by the user, but also knowledge contained in publicly available literature, case studies, or trained models, on a unified latent space 210 (see Figure 5).

[0806] Furthermore, this embodiment achieves the following by introducing a new technical configuration consisting of a rights management mechanism, a trust management mechanism, and a consideration calculation and distribution mechanism: lawful information presentation considering copyright law, unfair competition prevention law, publicity rights, portrait rights, etc.; creation of an incentive for information provision through appropriate compensation return to rights holders; promotion of effective sharing of not only success stories but also failure stories; and contribution to the democratization of knowledge and the development of industry.

[0807] This will enable even individual users with limited experience to evaluate and predict the future of intangible assets 200 by referencing the entire body of knowledge accumulated by humanity, and will also ensure that knowledge providers receive appropriate compensation, thereby forming a sustainable knowledge-sharing ecosystem.

[0808] (19) Effects As described above, the following effects can be obtained according to this embodiment.

[0809] (a) Generalization of knowledge By integrating publicly available intangible asset information and unifying it in the latent space 210, knowledge previously held only by specific experts becomes accessible to a wide range of users (see Figure 5).

[0810] (b) Consistency of evaluation By placing publicly available information and user-specific information on the same latent space 210 and evaluating them using a common standard module 410, consistency of evaluation criteria is ensured (see Figure 9).

[0811] (c) Improvement of prediction accuracy By referring to the state transition patterns in the published examples, the accuracy of generating the future state 300 in Example 3 is improved (see Figure 7).

[0812] (d) Generalization of experience By generalizing the insights extracted from individual publicly available case studies as distillation know-how in Example 4, a knowledge base is formed that can be referenced by users facing similar situations.

[0813] (e) Making value from failure cases Even publicly available cases that resulted in failure are valued as knowledge about "under what conditions failure occurs" through distance evaluation against standard module 410, contributing to supporting users' decision-making (see Figure 9). Furthermore, the compensation distribution mechanism provides economic incentives to providers of failure cases, leading to the active sharing of lessons learned from failures that were previously difficult to share.

[0814] (f) Transparency and verifiability Evaluation results based on publicly available information can be verified by users if the source of the information is clearly indicated.

[0815] (g) Continuous improvement By reusing user-generated evaluations and counterfactual analysis results as training data for the external observer model 104, the accuracy of this information processing method can be continuously improved (see Figure 2).

[0816] (h) Visualization of reliability By managing the confidence level of publicly available information (601) as metadata and visually displaying it in the observation space (220), users can make decisions while considering the reliability of the information (see Figures 6 and 11).

[0817] (i) Protection of rights holders The rights of rights holders are appropriately protected by a rights management mechanism that takes into account copyright law, unfair competition prevention law, publicity rights, portrait rights, etc.

[0818] (j) Appropriate return of compensation A compensation calculation and distribution mechanism based on actual usage ensures that appropriate compensation is returned to rights holders, and that incentives for knowledge provision are sustainably maintained.

[0819] (k) Promoting information provision The incentive of reciprocal payment leads to the integration of more useful information (both success and failure stories) into this information processing method, creating a virtuous cycle that improves evaluation and prediction accuracy.

[0820] (l) Contribution to industrial development The effective sharing of success and failure stories reduces the cost of trial and error for society as a whole, accelerates the pace of innovation, and promotes the development of knowledge-intensive industries.

[0821] (m) Integration with external triggers By treating updates to publicly available information as external trigger information 500 and displaying differences 501 based on importance 502, users can grasp the impact of changes in publicly available information in real time (see Figure 10).

[0822] The information processing method according to this embodiment builds upon the technological foundation established in Examples 1 to 5, adding new technological value such as the integration of publicly available knowledge, rights management, trust management, consideration distribution, and external trigger linkage. This expands the knowledge base for the valuation of intangible assets 200 and rights, and contributes to the development of industry in society through the formation of a sustainable knowledge-sharing ecosystem (see Figures 1, 2, 3, 5, 9, and 10). [Examples]

[0823] (Examples of intent change detection, state update via external triggers, evaluation function correction based on user behavior, inter-stakeholder interaction, and collective state output)

[0824] The following describes another embodiment of the information processing method according to the present invention.

[0825] This embodiment is based on the configuration described in Example 1, which places information regarding intangible assets 200 and associated rights in a latent space 210 (see Figures 3 and 5), the observation and manipulation of state transitions along the time axis 320 described in Example 2 (see Figure 6), the generation of future states 300 and presentation of branching scenarios described in Example 3 (see Figure 7), the calibration based on evaluation subject attributes and normative criteria described in Example 4 (see Figure 9), the disclosure control of decision path attributes described in Example 5 (see Figure 11), and the integration of publicly available intangible asset information described in Example 6. It further incorporates a configuration that dynamically updates the state transition model in response to changes in user intent, fluctuations in the external environment, and inter-entity interactions in the market.

[0826] (1) Detection of changes in intent and analysis of focus shifts

[0827] (1-1) Estimation of Intentional Vectors The information processing method according to this embodiment may include a step in which the processing unit 11 estimates the user's intent vector from the user's operation history, gaze information, transitions of selected objects, changes in comparison objects, or changes in condition inputs presented by the user from the input unit 22 (see Figures 1 and 2).

[0828] The intent vector is associated with at least one of the asset elements (elements 1-5: symbols 201-205) defined in Example 1, the asset properties of the latent space 210, or the evaluation subject attributes in Example 4, and is expressed as the dimension the user is currently focusing on, or the change in the degree of focus (see Figures 3 and 5).

[0829] (1-2) Time series changes in focal position This information processing method may also include a step of recording the time-series changes of the intention vector in the update history 208 as transitions along the time axis 320 detailed in Example 2 (see Figures 3 and 6).

[0830] This time-series change is treated as an indicator that the user's interest has shifted from one asset element to another, or that the evaluation subject attributes (static attributes or decision path attributes) in Example 4 have changed.

[0831] (1-3) Reflection in the state transition model The estimated intention vector is input to the state transition model detailed in Example 3 by the future state generation means 102 and may influence the weighting or selection of branching conditions in the generation of the future state 300 (see Figures 2 and 7).

[0832] Specifically, state transition branches 301 related to asset elements or market conditions that are of high interest to users may be calculated as having a higher probability of occurrence and displayed on the display unit 21 as "lines with a thickness or width corresponding to the probability of occurrence" in Example 3 (see Figures 1 and 7).

[0833] As a result, this information processing method can present branching scenarios that immediately reflect changes in the user's focus, even if those changes occur rapidly.

[0834] (2) State update via external trigger

[0835] (2-1) Obtaining external trigger information The information processing method according to this embodiment may include a step of obtaining external trigger information 500 such as policy changes, legal amendments, news reports, official statistics, corporate financial statements, international affairs, technical announcements, infrastructure development plans, court judgments, or market transaction information from an external information source 30 (see Figures 1 and 10).

[0836] The external trigger information 500 is treated as a type of external environmental information introduced in Example 2 and indicates the external conditions to be evaluated. However, it should be noted that the external trigger information 500 differs in nature from the publicly available intangible asset information (reference knowledge used for evaluation) in Example 6.

[0837] The acquired external trigger information 500 may be automatically obtained via the communication interface unit 13 from API integration, RPA (Robotic Process Automation), external databases, reports published by organizations, or user-specified information sources as mentioned in Example 2 (see Figure 1).

[0838] (2-2) Evaluation of the importance of trigger information This information processing method may include a step in which the processing unit 11 evaluates the importance 502 of the impact on the acquired external trigger information 500 on the intangible asset 200 or right to be evaluated (see Figures 2 and 10).

[0839] The aforementioned importance score 502 may be calculated based on the correspondence with past similar events using a method similar to the similarity rate defined in Example 3.

[0840] If the evaluation based on the impact and the corresponding severity 502 exceeds a predetermined threshold, this information processing method may push the external trigger information 500 to the display unit 21 as an "automatic notification when damage to intangible assets 200 is foreseeable due to an event unexpected by the user" as mentioned in Example 2 (see Figures 1 and 10).

[0841] (2-3) Coordinate updates in latent space The external trigger information 500 is treated as a variable that influences the coordinate values ​​of market elements, rights elements, or environmental elements in the latent space 210, as detailed in Example 3 (see Figures 5 and 10).

[0842] This information processing method may update the future state 300 by updating the arrangement of the index 207 in the latent space 210 using the state representation means 100, reflecting the external trigger information 500, when the importance 502 exceeds a predetermined threshold, and by re-executing the state transition model in Embodiment 3 using the future state generation means 102 (see Figures 2, 5, and 7).

[0843] This configuration ensures that risks or opportunities arising from rapid changes in the external environment are immediately reflected for users.

[0844] (3) Correction of evaluation function based on user behavior

[0845] (3-1) Acquisition of user behavior The information processing method according to this embodiment may include a step of acquiring the user's behavior history from the input unit 22 when the user selects, compares, adopts, saves, or rejects a specific future state 300 from among the presented state transition branches 301 (see Figures 1 and 7).

[0846] The aforementioned behavioral history is treated as part of the decision-making path attributes defined in Example 4 and may be subject to disclosure control in Example 5 (see Figure 11).

[0847] (3-2) Construction of the evaluation function The evaluation function in this information processing method may be configured as a weighting of at least one of the following: the importance of the asset elements (elements 1 to 5: symbols 201 to 205) defined in Example 1, the credibility evaluation based on the distance to the reference module 410 in Example 4, or the degree of responsiveness to the branching conditions of the state transition in Example 3 (see Figures 3 and 9).

[0848] (3-3) Correction based on behavioral history This information processing method may include a step of correcting the weighting of the evaluation function using the calibration means 103 based on the acquired user behavior history (see Figure 2).

[0849] Specifically, if a user repeatedly adopts the same type of scenario (for example, a scenario that emphasizes a particular asset element), the weighting coefficient corresponding to that asset element may be increased.

[0850] This correction may be implemented in the calculation of the subject-specific value 413 in Example 4 as a process that reflects the judgment path attributes (see Figure 9).

[0851] (3-4) Individualized branching presentation By using the corrected evaluation function, the state transition model is re-executed by the future state generation means 102, and in the subsequent generation of future states 300, branching conditions that are closer to the user's values ​​may be preferentially presented to the display unit 21 (see Figures 1, 2, and 7).

[0852] This allows users to more efficiently identify state transition branches 301 that align with their strategic intentions.

[0853] (4) Reflection of mutual influence between entities

[0854] (4-1) Definition of inter-subject dependency The information processing method according to this embodiment may include a step of representing the relationships in which multiple entities in the market (companies, organizations, nations, investor groups, or a collection thereof) can influence each other as dependencies between coordinates in the latent space 210 (see Figure 5).

[0855] The aforementioned dependency may include the following elements:

[0856] (a) Impact and importance based on subject attributes The impact and importance of attributes possessed by the entity, such as the history of exercise of rights, bargaining power, investment scale, market share, or element 5 (brand value: symbol 205) in Example 1 (see Figures 3 and 10).

[0857] (b) Causal correlation based on past cases Causal correlation based on records of past inter-subject interactions included in the reference document sets (standard document sets and historical document sets) in Examples 2 and 6.

[0858] (c) Regression impact on the overall market An interaction model that includes "regression effects when external entities make decisions using the output of this information processing method," and further includes a model that reflects the trends of the overall market, reflecting the trends of both external entities and the overall market.

[0859] (4-2) Identification of subject response using an external observer model This information processing method may use the external observer model 104 detailed in Example 4 to identify the input / output characteristics of the third-party entity being observed and estimate how that entity will respond to specific external trigger information 500 (see Figures 2 and 10).

[0860] The external observation instrument model 104 is continuously updated by sequential search and collection in Example 4, which can improve the estimation accuracy.

[0861] (4-3) Integration into state transition models The inter-subject dependency relationships described above may be integrated into the state transition model in Example 3 and considered by the future state generation means 102 in the generation of the future state 300 (see Figures 2 and 7).

[0862] Specifically, changes in the behavior of large entities, exercise of rights by risk-generating entities, or changes in partnership relationships with collaborating entities may be reflected in the state transition branch 301 as an impact on the state of the user's intangible asset 200 (see Figures 3 and 7).

[0863] (4-4) Visualization of Interactions The mutual influence between the entities may be visualized in the observation space 220 by the visualization means 101 on the display unit 21 using the following visual representation (see Figures 1, 2, 5, and 6).

[0864] This includes arrows or lines connecting the influencing entity and the affected entity, the thickness, color, or brightness of lines indicating the magnitude of the influence and its evaluation based on its importance, the selection of colors indicating the direction of the influence (positive or negative influence), and the representation of the invasion route in the "insect damage" metaphor exemplified in Example 2.

[0865] (5) Generation of aggregate state and API output

[0866] (5-1) Definition of set state The information processing method according to this embodiment may include a step in the processing unit 11 to generate a set state by integrating state vectors of multiple entities or future states 300 (see Figure 2). 【08...

Claims

1. An information processing method that arranges indicators representing the state of intangible assets in a multidimensional latent space and generates indicators corresponding to future states based on a state transition model, A step of storing the arrangement state of the indicator at a predetermined point in the past as past state data that can be reused as input to the state transition model, A step of replacing at least a portion of the external environmental parameters at a predetermined point in time with current or virtual external environmental parameters in the aforementioned past state data, A step of regenerating an index corresponding to a future state based on the state transition model under the replaced external environmental parameters, A step of displaying the regenerated indicator on the observation space by superimposing it with a future state generated from the current arrangement of the indicator, An information processing method characterized by including

2. An information processing method that arranges indicators representing the state of intangible assets in a multidimensional latent space, generates indicators corresponding to future states based on a state transition model, and displays them in an observation space, The process of setting out possible actions that the entity to which the aforementioned indicator belongs can take, For each of the aforementioned candidate actions, a step is to calculate a market impact coefficient that represents the magnitude of the influence of that action on the preconditions for state transitions in the latent space. A step of correcting the transition parameters of the state transition model based on the market impact coefficient, A step of generating an index corresponding to a future state that reflects the autoregressive effect caused by at least one of the candidate actions, based on the corrected state transition model, A step of displaying a future state that reflects the autoregressive effect and a future state that does not reflect the autoregressive effect in a comparative manner on the observation space, An information processing method characterized by including

3. An information processing method that calculates an index representing the state of an intangible asset, places it in a multidimensional latent space, and generates an index corresponding to a future state based on a state transition model, A step of generating an index corresponding to the future state based on the state transition model, or performing at least a part of the calculation of the index, using an externally provided computation model or analysis module; A step of obtaining feedback metadata representing evaluation behavior by multiple users regarding the calculation of the indicator or the generation of the future state using the output of the calculation model or analysis module, The process includes aggregating the acquired feedback metadata and calculating or updating the reliability for each computation model or analysis module, A step of reflecting the updated confidence level in the weighting of the output of the calculation model or analysis module in the calculation of the index or the generation of the future state, An information processing method characterized by including

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