Organizational Value Improvement Support System

Abstract

An organizational value improvement support system 1000 that supports increasing the value of intangible assets (IACC) created by members of an organization, such as corporate employees, and is capable of appropriately managing the intangible assets to contribute to the operation of the organization, has at least one processor and memory, the memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control an input information generation unit 130, a first inference request generation unit 110, and a first transmission / reception unit 120 by executing the instructions, and the first input information data group has IACC information data, intangible resource information data, and performance-related information data, and the input information generation unit 130 has an IACC information reading unit 131, an intangible resource information reading unit 132, and a performance-related information reading unit 133.

Description

[Technical Field]

[0001] The present disclosure belongs to the fields of organizational management support information technology and artificial intelligence technology, and relates to a system for improving the value of an organization such as a company. Specifically, the present disclosure relates to a system for supporting increasing the value of intangible assets created by organizational members such as company employees, and for supporting improving the value of an organization by clarifying the capital classification of the intangible assets and appropriately managing them within the organization. Note that in this disclosure, "organizational management" includes "corporate management" as a subordinate concept, and "company" includes "listed stock company" as a subordinate concept. [Background technology]

[0002] As an artificial intelligence technology, Non-Patent Document 1 describes the effect of large-scale language models in learning by several examples. In recent years, there has been rapid progress in technology related to business support using pre-trained generative models such as large-scale language models. In this specification, "pre-trained generative models" may be abbreviated as "generative models." Specific examples of generative models include the above-mentioned large-scale language models that generate text, as well as music generation models, image generation models, video generation models, and multimodal models that can handle multiple data formats.

[0003] Patent Document 1 discloses a matching system that performs a matching process between human resources and projects, which acquires text data representing the content of the project, identifies one or more project skills related to the project based on the text data, acquires one or more attributes for each of multiple human resources, calculates a score for each attribute of each human resource based on each of the project skills and each of the attributes, calculates the score for each human resource based on the score for each attribute of the human resource, ranks the multiple human resources based on each score of each human resource, and outputs each score as a numerical value, the matching system associates one or more normalized skills with each attribute, and calculates the score for each attribute based on the normalized skills associated with the attribute. Such a matching system may input information including the text data into a large-scale language model and acquire the one or more project skills from the large-scale language model.

[0004] Patent Document 2 discloses a real estate management support device that includes a property management unit that manages property information for rental properties in real estate rental, a notification unit that notifies rental agency staff of rental work based on the property information, and a policy proposal unit that proposes measures to encourage people to move into rental properties based on the property information, where the property management unit manages property information including work deadlines, rental conditions, and occupancy status for the rental property, and further includes a guidance unit that displays a guide who will show people around the rental property in a virtual space, where the property management unit manages property information including the characteristics of the rental property, and the guidance unit generates a response to be conveyed by the guide to the person being guided by processing using a language model that includes questions from the person being guided and the characteristics as input, and this language model has been pre-trained using training data that includes the questions and the characteristics as explanatory variables and the responses as target variables.

[0005] Patent Document 3 discloses a program to be executed by a computer having a processor and a memory, the program causing the processor to execute the following steps: accepting input of an episode experienced by a user and numerical information that quantifies the degree of a predetermined emotion the user felt when the experience was experienced; storing the accepted episode and the numerical information in the memory; generating a second prompt for causing a large-scale language model to generate a question to enrich the episode, using the episode stored in the memory; inputting the generated second prompt into the large-scale language model to generate the question; outputting the question; accepting the episode modified by an operation of the user; storing the accepted modified episode in the memory;

[0006] Patent Document 4 discloses an information processing system that is characterized by providing a generator that generates answers based on a trained language model with instructions to generate character strings extracted from report data including activity amounts of activities that emit greenhouse gases, as well as the type of activity amount to be extracted and a scope corresponding to the activity amount, and having a generation processing unit that causes the generator to generate the type and scope.

[0007] Patent Document 5 discloses a back office business support device that includes a business information management unit that manages business information that associates company attributes with back office business operations performed by the company, a target attribute acquisition unit that acquires target attributes that are attributes of a target company, and a business proposal unit that proposes back office business to the target company based on multiple business information managed in the business information management unit and the target attributes, wherein the business information management unit is configured to manage business information related to company attributes including company size and business information related to company attributes including the company's fiscal year end, the target attribute acquisition unit is configured to acquire company target attributes including company size and target attributes including the company's fiscal year end, and the business proposal unit proposes the back office business that is associated with the attributes in the business information that match the target attributes.

[0008] Patent document 6 discloses an information providing device that includes a question receiving unit that receives a question, an answer generating unit that causes a language model to generate an answer to the question, a report generating unit that generates a report including the answer generated by the answer generating unit, an information acquiring unit that acquires various information related to the question, and a ratio setting unit that sets the ratio of the various information related to the question in the report generated by the report generating unit, and the report generating unit generates a report including the various information at the ratio set by the ratio setting unit.

[0009] Patent Document 7 discloses a control system comprising a control device that controls a controlled object and a language model, wherein the control device comprises a transmission means that transmits metadata of a function for controlling the controlled object to the language model and a notification means that notifies the language model of a prompt for controlling the controlled object, the language model comprises a determination means that determines whether to respond to the prompt using the function based on the metadata transmitted by the transmission means and the prompt notified by the notification means, and a presentation means that presents answer information to the control device that includes identification information that identifies the function when the determination means determines that the prompt will be answered using the function, the control device further comprising control means that controls the controlled object using the answer information presented by the presentation means, and the controlled object includes any one of a robot, a sensor, and a plant.

[0010] Patent Document 8 describes a large-scale language processing system having an operation log acquisition unit that acquires operation logs of monitored terminals used for business, an operation log classification unit that compares the operation logs acquired by the operation log acquisition unit with business elements that serve as classification indicators for the business for each predetermined time frame and classifies them, and a document creation unit that creates business support documents including daily reports based on the classification results obtained by the operation log classification unit, in which the document creation unit combines one or more types of processing such as morphological analysis, syntactic analysis, semantic analysis, context analysis, and intention analysis in natural language processing to probabilistically predict how likely words and sentences given in a prompt are to occur in natural language, analyze the prompt, and predict and generate documents based on the content of the analyzed prompt. A business support document creation device is disclosed, which has a model unit, a document creation prompt generation unit that outputs to the large-scale language model unit the prompt whose content instructs the creation of the business support document as the document based on the classification result, a business pattern formation unit that forms a business pattern for a certain period of time of business performed on the monitored terminal based on the classification result in the operation log classification unit, a business change detection unit that detects a qualitative change in the business performed on the monitored terminal based on the amount of change between the business patterns repeatedly formed at each certain period, and a warning information output unit that, when a qualitative change in the business is detected, outputs the monitored terminal in which the qualitative change occurred to an administrator terminal as warning information together with the content of the qualitative change.

[0011] Non-Patent Document 2 provides principles-based guidance for companies and other organizations preparing integrated reports, <ir>A framework is provided. <ir>In the framework, the resources and relationships that an organization uses and influences are collectively referred to as "capitals." <ir>In the framework, this capital is classified into "financial capital," "manufactured capital," "intellectual capital," "human capital," "social and relationship capital," and "natural capital." In Non-Patent Document 2, the roles of capital are defined as follows: · Serve as part of the theoretical underpinnings for the concepts of value creation, preservation or destruction. Serve as a guideline to ensure that organizations consider all forms of capital that they use or have an impact on.

[0012] Furthermore, Non-Patent Document 2 contains the following description regarding tissues: The core of an organization is its business model. In a business model, various capitals are used as inputs and transformed into outputs (products, services, by-products, and waste) through business activities. The organization's business activities and outputs result in outcomes that affect the capitals. The ability of a business model to adapt to changes (e.g., in terms of the availability, quality, and economics of inputs) can affect the organization's long-term viability. [Prior art documents] [Patent documents]

[0013] [Patent Document 1] Patent No. 7574522 [Patent Document 2] Patent No. 7573923 [Patent Document 3] Patent No. 7573330 [Patent Document 4] Patent No. 7569125 [Patent Document 5] Patent No. 7557812 [Patent Document 6] Patent No. 7519138 [Patent Document 7] Patent No. 7576729 [Patent Document 8] Patent No. 7572760 [Non-patent literature]

[0014] [Non-Patent Document 1] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei, Language Models are Few-Shot Learners, https: / / arxiv.org / pdf / 2005.14165, arXiv:2005.14165, 22 Jul 2020. [Non-patent document 2] IntegratedReporting#Framework#061024.pdf(https: / / integratedreportingsa.org / ircsa / wp-content / uploads / 2021 / 01 / InternationalIntegratedReportingFramework.pdf) DISCLOSURE OF THE INVENTION [Problem to be solved by the invention]

[0015] The present disclosure aims to provide an organizational value improvement support system that uses a pre-trained generative model such as a large-scale language model to support increasing the value of intangible assets created by organizational members such as company employees, and that enables appropriate management of the intangible assets so as to contribute to the operation of the organization. In this disclosure, from the perspective of appropriately managing the above-mentioned intangible assets, we have provided a system for supporting organizational value improvement that uses a pre-trained generative model such as a large-scale language model to support increasing the value of intangible assets created by organizational members such as company employees, and that enables appropriate management of the intangible assets so as to contribute to the operation of the organization. <ir>This disclosure also aims to clarify the relationship between the five capitals (human capital, intellectual capital, manufactured capital, social and relationship capital, and natural capital; hereafter, in this disclosure, these capitals will be collectively referred to as "non-financial capital") excluding financial capital out of the six capitals defined in the Framework, and the intangible assets created by members. This disclosure also aims to provide a quantitative means of evaluating non-financial capital. [Means for solving the problem]

[0016] The present disclosure, provided to solve the above problems, includes the following aspects.

[0017] (1) The present disclosure includes an organizational value improvement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control, by executing the instructions, an input information generation unit that collects data constituting a first input information data group, a first inference request generation unit that automatically generates first inference request data using as input a plurality of pieces of information data included in the first input information data group, and a first transceiver unit that transmits the first inference request data to a first generative model that is a pre-trained generative model and receives first inference result data from the first generative model. The first input information data group includes: data stored in an IACC information database, which is IACC information data including information on IACC, which is an intangible asset created by members belonging to an organization; data stored in an intangible resource information database, which is intangible resource information data including information on intangible resources belonging to the same type of non-financial capital as the type of non-financial capital to which the IACC belongs, among intangible resources owned by the organization to which the members belong; and data stored in a performance-related information database, which is performance-related information data including target values ​​for financial and performance indicators of the organization, and the first inference request included in the first inference request data is for improving the financial and performance indicators included in the performance-related information data based on the IACC information data and the intangible resource information data. The method includes generating a first proposal, and the input information generation unit includes an IACC information reading unit that identifies which of the IACC information data stored in the IACC information database is to be read and reads the identified IACC information data from the IACC information database, an intangible resource information reading unit that identifies which of the intangible resource information data stored in the intangible resource information database is to be read based on the identified IACC information data and reads the identified intangible resource information data from the intangible resource information database, and a performance-related information reading unit that identifies the financial and performance indicators that have been determined to be targets for improvement and generates the performance-related information data including target values ​​for the identified financial and performance indicators.

[0018] In this disclosure, an "organization" refers to a group of people and resources formed to achieve a specific purpose, and includes a variety of forms, such as a company such as a joint stock company, a non-profit organization such as a hospital, and a public institution such as a national university corporation. In this disclosure, "constituent members" refers to the personnel belonging to an organization, and if the organization is a company, includes employees and directors, and may also include external contractors under contract with the organization.

[0019] In this disclosure, the knowledge, skills, abilities, etc. possessed by constituent members (also referred to herein as "KSAs"), as well as the intellectual assets created by constituent members, such as ideas conceived based on these KSAs, are referred to as "individual skills." Furthermore, intangible assets created by constituent members, such as intangible assets possessed by employees with useful properties, or ideas conceived based on these intangible assets, are referred to as "intangible assets created by constituent members," abbreviated as "IACC" in this disclosure, and refer to the creator of an IACC if it is an invention, the inventor if it is a utility model, or the creator if it is a design or copyrighted work. However, these are collectively referred to as the "creator" in this disclosure.

[0020] In this disclosure, "intangible resources" refers to non-physical information, knowledge, etc. (specific examples include technical information, know-how, data, algorithms, design information, brands, etc.) that an organization uses to create value, regardless of whether they are recognized as intangible assets in accounting. Many of these intangible resources consist of IACC created by members of the organization, but they may also be acquired through licensing-in or purchase. Such intangible resources with clear internal valuations (monetary conversion indicators) that are recognized as "intangible assets" in accounting can be included in the explanation of financial capital in the capital classification of Non-Patent Document 2.

[0021] In this disclosure, "intangible resource information" refers to an intangible entity that represents an intangible resource, and is an information object that may include information describing the organization's contents, information indicating the type of non-financial capital to which the intangible resource belongs, and metadata such as identifier, origin, version, authority, and confidentiality level. An example of the display of intangible resource information is shown in Figure 7. "Intangible resource information data" is a data structure that allows intangible resource information to be mechanically stored, transmitted, and processed. Intangible resource information can be directly referenced, created, updated, and deleted (CRUD) as this data structure, and can be recorded on a storage medium, transmitted over a communication channel, and operated on a processing device.

[0022] (2) The input information generation unit may have a data group generation unit that generates the first input information data group including the read IACC information data, the read intangible resource information data, and the read performance-related information data. The first inference request generation unit may have a first information identification unit that receives the first input information data group generated by the data group generation unit as an input and identifies the plurality of pieces of information included in the first input information data group.

[0023] (3) The first inference request generation unit may include a first prompt generation unit that automatically generates the first inference request data, including automatically placing each of the plurality of pieces of information included in the first input information data group in a predetermined target field and executing a process to generate management data.

[0024] (4) The IACC information reading unit may include an authority confirmation unit that identifies an operator attempting to access the IACC information data and confirms the access authority of the identified operator, an access control unit that enables the operator to access the IACC information data stored in the IACC information database within the scope of the access authority confirmed by the authority confirmation unit, and a reading unit that identifies the IACC information data that the operator can access and that has been determined to be included in the first input information data group, and reads the identified IACC information data from the IACC information database.

[0025] (5) In the organizational value improvement support system described in (4) above, the access control unit may generate display filter instruction data that allows the IACC information database to display to the operator the self-IACC created by the identified operator and other IACCs that the identified operator did not create but that the operator is allowed to view, and transmit the display filter instruction data to the IACC information database.

[0026] (6) In the organizational value improvement support system described in (5) above, the input information generation unit may include a citation notification generation unit that generates citation notification data to notify the creator of the other IACC of the decision made by the operator to include the other IACC in the first input information data group.

[0027] (7) The organization value improvement support system of (6) above may further include a quotation notification counting unit that records how many quotation notification data items have been generated for each of the IACC information data items.

[0028] (8) The IACC information data may include information indicating the type of non-financial capital to which the IACC belongs.

[0029] (9) In the organizational value improvement support system described in (8) above, the intangible resource information reading unit may use the identified IACC information data as input to identify the type of non-financial capital to which the IACC belongs, and read from the performance-related information database the performance-related information data stored in the performance-related information database that is of the same type as the identified type of non-financial capital.

[0030] (10) The first input information data group may include personal emotion information data including information indicating a first emotion, which is an emotion expected by the member when performing their work. The first inference request may include generating a proposal for a behavioral indicator that is in line with the first emotion and contributes to improving the financial / performance indicator. The input information generation unit may include an emotion information identification unit that identifies the first emotion included in the personal emotion information data. The first inference request generation unit may receive as input data including the first emotion identified by the emotion information identification unit.

[0031] (11) In the organization value improvement support system of (10) above, the emotion information identification unit may include an emotion display unit that generates emotion display data that selectably displays a predetermined number of emotions, and an emotion determination unit that determines which of the multiple emotions displayed in the emotion display unit has been selected as the first emotion.

[0032] (12) The first input information data group may further include auxiliary information data stored in an auxiliary information database, the auxiliary information data including current affairs information about the organization. The first inference request may include referencing the auxiliary information data when generating the first proposal. The input information generation unit may include an auxiliary information reading unit that identifies which of the auxiliary information data stored in the auxiliary information database is to be read and reads the identified auxiliary information data from the auxiliary information database.

[0033] (13) The auxiliary information reading unit described in (12) above may include a read request generating unit that determines which of the auxiliary information data stored in the auxiliary information database is to be read, generates a read request to the auxiliary information database, and sends the read request to the auxiliary information database, and a data reading unit that receives the auxiliary information data prepared in accordance with the read request from the auxiliary information database.

[0034] (14) In the organizational value improvement support system described in (13) above, the IACC information data may include data indicating the type of non-financial capital to which the IACC belongs, and the read request generation unit may determine that the auxiliary information data containing auxiliary information of the same type as the type of non-financial capital to which the IACC belongs, included in the IACC information data, is the target of the read request.

[0035] (15) The present disclosure includes an organizational value improvement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control a first inference request generation unit that generates first inference request data using information data included in a first input information data group as input, a first transceiver unit that transmits the first inference request data to a first generative model that is a pre-trained generative model and receives first inference result data from the first generative model, and a comparison display generation unit. The first input information data group includes IACC information data that includes information on IACC, an intangible asset created by an organization's members, written in a predetermined first format. The first inference request included in the first inference request data includes generating a first proposal to improve financial and performance indicators of the organization to which the member belongs, based on the first input information data group. The comparison display generation unit receives the first inference result data and the IACC information data as input, displays the information contained in the first inference result data in the first format, generates comparison display data that allows comparison with the information contained in the IACC information data, and performs processing that includes outputting the comparison display data.

[0036] (16) The present disclosure includes an organizational value improvement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control a first inference request generation unit that generates first inference request data using information data included in a first input information data group as input, a first transceiver unit that transmits the first inference request data to a first generative model that is a pre-trained generative model and receives first inference result data from the first generative model, and a first IACC information registration unit. The first input information data group includes IACC information data stored in an IACC information database, the IACC information data including information on IACC, an intangible asset created by an organization's members, written in a predetermined first format. The first inference request included in the first inference request data includes generating a first proposal to improve financial and performance indicators of the organization to which the member belongs, based on the first input information data group. The first IACC information registration unit generates draft IACC information data in which the information contained in the first inference result data is written in the first format, and performs processing including replacing the draft IACC information data with the IACC information data and storing it in the IACC information database, provided that an acceptance instruction for the draft IACC information data is input.

[0037] (17) In the organizational value improvement support system described in (16) above, when a correction instruction is input for the first inference result data, the first IACC information registration unit reflects the correction instruction in the information contained in the first inference result data, generates IACC information data under correction in which the reflected result is written in a format corresponding to the display of the IACC information data, and, provided that an approval instruction for the IACC information data under correction is input, replaces the IACC information data with the IACC information data and sets it as the draft IACC information data to be saved.

[0038] (18) In the organization value improvement support system described in (16) above, the first inference request may include generating publicly known proposal information including the result of an investigation into whether the first proposal is publicly known. The system may further include a secret management setting unit that receives the IACC information data to be replaced and stored in the IACC information database, generated by the first IACC information registration unit, and the first inference result data as input, determines whether the first proposal is publicly known or not based on the publicly known proposal information, and, if it is determined that the first proposal is not publicly known, executes processing including setting the management attribute of the IACC information data to data including a candidate object of industrial property right.

[0039] (19) In the organizational value improvement support system described in (18) above, the IACC information database may further include an information management unit that generates information management instruction data for classifying and managing the IACC information data and transmits the information management instruction data to the IACC information database. The information management instruction data may include a first instruction for classifying and managing the IACC information data based on the management attribute.

[0040] (20) In the organizational value improvement support system described in (19) above, the IACC information data may include data indicating the type of non-financial capital to which the IACC belongs, and the information management instruction data may also include a second instruction for classifying and managing the IACC based on the type of non-financial capital to which the IACC belongs.

[0041] (21) In the organizational value improvement support system described in (20) above, the first instruction may be an instruction to classify the IACC in terms of a secrecy management level, and the second instruction may be an instruction to classify the IACC in terms of a type of the non-financial capital to which the IACC belongs.

[0042] (22) In the organizational value improvement support system described in (21) above, the first instruction may include classification in terms of whether the information is a candidate object of industrial property rights, a trade secret, or information that does not require confidentiality protection.

[0043] (23) The organizational value improvement support system described in (8) above may further include an initial inference request generation unit that automatically generates initial inference request data using IACC basic information data as input, and an initial transmission / reception unit that transmits the initial inference request data to an initial generative model that is a pre-trained generative model and receives initial inference result data from the initial generative model. The IACC basic information data may be basic data of the IACC information data and may include multiple partial information pieces that constitute the IACC information. The initial inference request included in the initial inference request data may include generating a proposal regarding the type of non-financial capital to which the IACC should belong as a result of analyzing the IACC basic information data. The initial inference request generation unit may include an IACC partial information identification unit that receives the IACC basic information data and identifies the multiple partial information pieces, and an initial prompt generation unit that automatically generates the initial inference request data, including executing a process of automatically placing the multiple partial information pieces corresponding to the multiple partial information pieces in each of the target fields of an initial format that is pre-specified format data.

[0044] (24) In the organization value improvement support system described in (23), the initial inference request generation unit may further include an input display generation unit that executes a process including generating input display data having a plurality of input fields corresponding to the plurality of partial information, and outputs the input display data. The IACC partial information identification unit may receive as input data including the plurality of partial information entered in the plurality of input fields, generate a plurality of data corresponding to each of the plurality of partial information, and output a group of data sets including the plurality of data to the initial prompt generation unit.

[0045] (25) In the organizational value improvement support system described in (1) above, the intangible resource information data may include data indicating the type of non-financial capital to which the intangible resource belongs, and the system may further include an intangible resource inference request generation unit that generates intangible resource inference request data using intangible resource basic information data that is basic information of the intangible resource information data and includes information on the intangible resources as input, and an intangible resource transceiver unit that transmits the intangible resource inference request data to an intangible resource generation model that is a pre-trained generation model and receives intangible resource inference result data from the intangible resource generation model. The intangible resource inference request included in the intangible resource inference request data may include generating a proposal regarding the type of non-financial capital to which the intangible resource should belong as a result of analyzing the intangible resource basic information data. The intangible resource inference request generation unit may include an intangible resource information input unit that accepts the intangible resource basic information data and identifies the information of the intangible resource included in the intangible resource basic information data, and an intangible resource prompt generation unit that automatically generates the intangible resource inference request data, including executing a process to automatically place the information of the intangible resource in a target field of an intangible resource format, which is format data in which the target field into which the information of the intangible resource should be placed is specified in advance.

[0046] (26) In the organizational value improvement support system described in (8) above, the first inference request may include, when the IACC information data includes human capital-related information related to systems and systems for developing the knowledge, skills, and abilities of the members (first case), including an improvement proposal with a view to increasing human capital in the first proposal, and when the IACC information data does not include the human capital-related information (second case), adding an improvement proposal with a view to increasing at least one of manufactured capital, social and relationship capital, and natural capital to the first proposal. In the first case above, the first proposal may include an improvement proposal with a view to increasing non-financial capital other than human capital (intellectual capital, manufactured capital, social and relationship capital, and natural capital), or an improvement proposal with a view to increasing the organization's management assets, such as know-how including trade secrets.

[0047] (27) In the organizational value improvement support system described in (1) above, the first inference request may include generating publicly known proposal information including the results of a review of whether the first proposal is publicly known.

[0048] (28) In the organizational value improvement support system described in (27) above, the first inference request may include indicating publicly known information that is determined to include the first proposal if the publicly known proposal information includes a proposal that the publicly known proposal information is publicly known, and indicating publicly known information that is determined to be closest to the first proposal if the publicly known proposal information includes a proposal that the publicly known proposal information is not publicly known.

[0049] (29) In the organizational value improvement support system described in (1) above, at least a portion of the data stored in the IACC information database may be positioned as data stored in the intangible resource information database.

[0050] (30) In the organization value improvement support system described in (29) above, the IACC information database and the intangible resource information database may be integrated.

[0051] (31) In the organizational value improvement support system described in (29) above, the IACC information database may further include an information management unit that generates information management instruction data for classifying and managing the IACC information data and transmits the information management instruction data to the IACC information database. The information management instruction data may include an applicability instruction indicating whether the IACC information data may be stored in the intangible resource information database as the intangible resource information data.

[0052] (32) In the organizational value improvement support system described in (31) above, the information management instruction data may include confidentiality management instructions indicating the degree of confidentiality management of the information contained in the intangible resource information data in the intangible resource information database.

[0053] (33) The organizational value improvement support system described in (1) above may include an intangible resource information management unit that generates disclosure management instruction data indicating whether the intangible resource information data stored in the intangible resource information database may be disclosed outside the organization and the extent of disclosure, and transmits the disclosure management instruction data to the intangible resource information database.

[0054] (34) The present disclosure includes an organizational value improvement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control a first inference request generation unit that generates first inference request data using information data included in a first input information data group as input, a first transceiver unit that transmits the first inference request data to a first generative model that is a pre-trained generative model and receives first inference result data from the first generative model, and a public knowledge display generation unit. The first input information data group includes IACC information data stored in an IACC information database, the IACC information data including information about IACC, an intangible asset created by an organization's members, described in a predetermined first format. The first inference request included in the first inference request data includes generating a first proposal to improve financial and performance indicators of the organization to which the member belongs, based on the first input information data group, and generating public knowledge proposal information including a review result of whether the first proposal is public knowledge. The public knowledge display generation unit receives the first inference result data as input, determines whether the first proposal is public or non-public from the public knowledge proposal information, and if it determines that the first proposal is non-public, performs processing that includes generating non-public knowledge display data that includes a notification indicating that the first proposal may be confidential information.

[0055] (35) The present disclosure includes an organizational value enhancement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control an initial IACC information registration unit that performs processing including storing IACC information data, including information about an IACC, an intangible asset created by an organization's members, in an IACC information database, in response to the execution of the instructions; an initial inference request generation unit that generates initial inference request data using IACC basic information data, which is basic information of the IACC information data and includes information about the IACC; an initial transceiver unit that transmits the initial inference request data to an initial generative model that is a pre-trained generative model and receives initial inference result data from the initial generative model; and an initial public knowledge display generation unit that receives the initial inference result data as input. The initial inference request included in the initial inference request data may generate initial public knowledge proposal information including a result of an investigation into whether the IACC is public knowledge. The initial public knowledge indication generation unit determines whether the IACC is public or non-public from the initial public knowledge proposal information, and if it determines that the IACC is non-public, performs processing including generating initial non-public knowledge indication data including a notification indicating that the IACC may be confidential information.

[0056] (36) In the organizational value improvement support system described in (35) above, if the initial publicly known proposal information includes a proposal that the information is publicly known, publicly known information determined to include the IACC may be displayed, and if the initial publicly known proposal information includes a proposal that the information is not publicly known, publicly known information determined to be closest to the IACC may be displayed.

[0057] (37) The present disclosure includes an organizational value enhancement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control an initial IACC information registration unit that performs processing including storing IACC information data, including information about an IACC, an intangible asset created by an organization's members, in an IACC information database, in response to the execution of the instructions; an initial inference request generation unit that generates initial inference request data using IACC basic information data, which is basic information of the IACC information data and includes information about the IACC; an initial transmission / reception unit that transmits the initial inference request data to an initial generative model that is a pre-trained generative model and receives initial inference result data from the initial generative model; and an initial secret management setting unit that receives the initial inference result data as input. The initial inference request included in the initial inference request data may generate initial public knowledge proposal information including a result of an investigation into whether the IACC is public knowledge. The initial secret management setting unit determines whether the IACC is publicly known or not from the initial publicly known proposal information, and if it determines that the IACC is not publicly known, performs processing including setting the management attribute of the IACC information data to data including a candidate object of industrial property rights.

[0058] (38) The present disclosure includes an organizational value enhancement support system including at least one processor and a memory. The memory stores non-transitory instructions, which, when executed by the processor, are configured to control an initial IACC information registration unit that performs processing including storing, in an IACC information database, IACC information data including information describing, in a predetermined first format, IACC, an intangible asset created by organizational members, and information indicating the type of non-financial capital to which the IACC belongs; an initial inference request generation unit that uses, as input, IACC basic information data that is basic information of the IACC information data and includes information about the IACC, and generates initial inference request data; an initial transceiver unit that transmits the initial inference request data to an initial generative model that is a pre-trained generative model and receives initial inference result data from the initial generative model; and a capital display generation unit that uses, as input, the initial inference result data. The initial inference request included in the initial inference request data includes generating a proposal regarding the type of non-financial capital to which the IACC should belong as a result of analyzing the IACC basic information data. The capital display generation unit executes a process including identifying the proposed type of non-financial capital from the initial inference result data and generating capital display data for displaying the identified type of non-financial capital. The initial IACC information registration unit executes a process including identifying the proposed type of non-financial capital from the initial inference result data and generating the IACC information data based on the identified type of non-financial capital and the IACC basic information data.

[0059] (39) The present disclosure includes an organizational value enhancement support system including at least one processor and a memory. The memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to control an intangible resource information registration unit that performs processing including storing intangible resource information data, including information on intangible resources possessed by an organization and data indicating the type of non-financial capital to which the intangible resources should belong, in an intangible resource information database through execution of the instructions; an intangible resource inference request generation unit that uses intangible resource basic information data, which is basic information of the intangible resource information data and includes information on the intangible resources, as input, to generate intangible resource inference request data; and an intangible resource transceiver unit that transmits the intangible resource inference request data to an intangible resource generation model, which is a pre-trained generative model, and receives intangible resource inference result data from the intangible resource generation model. The intangible resource inference request included in the intangible resource inference request data includes generating a proposal for the type of non-financial capital to which the intangible resource should belong as a result of analyzing the intangible resource basic information data. The intangible resource information registration unit receives the intangible resource inference result data and the intangible resource basic information data as input, identifies the proposed type of non-financial capital from the intangible resource inference result data, and performs processing that includes generating the intangible resource information data based on the identified type of non-financial capital and the intangible resource basic information data.

[0060] (40) In the organization value improvement support system described in (39) above, the intangible resource information data may include information on SDG targets related to the intangible resources. The intangible resource inference request may include generating a proposal for an SDG target related to the intangible resources as a result of analyzing the intangible resource basic information data.

[0061] (41) In the organizational value improvement support system described in (40) above, the intangible resource information registration unit may identify the proposed SDGs target from the intangible resource inference result data and include information on the identified SDG target in the intangible resource information data.

[0062] (42) The present disclosure includes an organizational value improvement support system including at least one processor and a memory. The memory stores non-transient instructions, and when the instructions are executed by the processor, the processor is configured to control an intangible resource information utilization management unit and manage the internal utilization of intangible resource information included in intangible resource information data by executing the instructions. The intangible resource information is an intangible entity that represents intangible resources held by the organization and includes information describing the contents of the intangible resources and attribute information indicating whether the intangible resources belong to non-financial capital, and the intangible resource information data has a data structure that allows the intangible resource information to be mechanically stored, transmitted, and processed.

[0063] (43) In the organizational value improvement support system described in (42) above, the intangible resource information data may be stored in an intangible resource information database, and the intangible resource information usage management unit may obtain the intangible resource information data to be managed from the intangible resource information database.

[0064] (44) In the organizational value improvement support system described in (42) above, the intangible resource information usage management unit may be provided with a history management unit that executes processing including generating usage history data including the usage history of the intangible resource information.

[0065] (45) In the organizational value improvement support system described in (44) above, the history management unit may include a history recording unit that performs processing including recording the intangible resource information used as contributed intangible resource information, and a contribution amount calculation unit that performs processing including calculating the contribution amount of the contributed intangible resource information using the organization's activity record data.

[0066] (46) In the organizational value improvement support system described in (45) above, the contribution amount calculation unit may calculate the contribution amount based on the intangible resource information-derived performance that has occurred and / or is expected to occur with the involvement of the contributing intangible resource information, and the contribution rate of the contributing intangible resource information in the intangible resource information-derived performance.

[0067] (47) In the organizational value improvement support system described in (45) above, the contribution amount calculation unit may further include a contribution inference request unit, which may execute processing including: using the contribution intangible resource information and the organization's activity record data as input, creating calculation inference request data including an inference request to a calculation generation model, sending the calculation inference request data to the calculation generation model, and receiving contribution inference result data including a contribution amount proposal.

[0068] (48) In the organizational value improvement support system described in (47) above, the calculation generation model may, at the time of inference, refer to a performance-related information database in which data including information related to the organization's performance is accumulated, and / or an intangible resource information database in which the intangible resource information data is stored, in accordance with instructions from the contribution inference request unit.

[0069] (49) In the organizational value improvement support system described in (44) above, the intangible resource information usage management unit may further include a value calculation unit that executes processing including calculating the intangible resource value, which is the internal valuation amount of the intangible resource information, based on the usage history data.

[0070] (50) In the organizational value improvement support system described in (49) above, the value calculation unit may calculate the intangible resource value by allocating an allocation amount set based on the difference between the organization's market capitalization and identifiable net assets to each intangible resource information.

[0071] (51) In the organizational value improvement support system described in (50) above, if the organization is a joint-stock company, the total value of shares may be used as the market capitalization, and the most recently published balance sheet may be referenced as the identifiable net assets.

[0072] (52) In the organizational value improvement support system described in (49) above, the intangible resource information utilization management unit may include a classification setting unit that performs processing including classifying the intangible resource information into multiple categories, and a category value calculation unit that performs processing including calculating a category value, which is a total internal evaluation amount, for each of the categories based on the intangible resource value calculated by the value calculation unit.

[0073] (53) In the organizational value improvement support system described in (52) above, the classification setting unit may perform classification based on the degree of confidentiality management of the intangible resource information data and the type of non-financial capital to which the intangible resource information belongs.

[0074] (54) In the organizational value improvement support system described in (53) above, the classification setting unit may classify the intangible resource information data into a discloseable information category and a non-discloseable information category based on the degree of secret management.

[0075] (55) In the organizational value improvement support system described in (54) above, the intangible resource information utilization management unit may further include an organizational value financial statement creation unit. The organizational value financial statement creation unit may execute processing including generating OV-B / S data for creating an organizational value balance sheet (OV-B / S) defined by displaying a sustainable assets section after an assets section and a non-financial capital section after an equity section in a balance sheet. The sustainable assets section may indicate the category values ​​of the discloseable information categories and the category values ​​of the non-discloseable information categories, and the non-financial capital section may indicate the category values ​​of the categories corresponding to each type of non-financial capital.

[0076] (56) In the organizational value improvement support system described in (42) above, the intangible resource information utilization management unit may include a PBR information provision creation unit that executes processing including creating PBR information provision data for notifying a user when the price-book-value ratio (PBR) of the organization is less than 1, when the organization is a stock company. In this case, when the price-book-value ratio (PBR) exceeds 1, the intangible resource information utilization management unit may indicate the non-financial capital and / or the sustainable assets that contribute greatly to increasing the price-book-value ratio (PBR).

[0077] (57) In the organizational value improvement support system described in (55) above, the intangible resource information utilization management unit may include an improvement proposal inference request unit. The improvement proposal inference request unit may execute processing including: using the OV-B / S data as input, generating improvement inference request data including an inference request to an improvement generative model; transmitting the improvement inference request data to the improvement generative model; and receiving improvement inference result data including proposals to increase the sustainable assets and / or the non-financial capital. The improvement inference result data may include proposals indicating the non-financial capital and / or the sustainable assets that have a large contribution to increasing the price-to-book ratio (PBR).

[0078] (58) In the organizational value improvement support system described in (57) above, the improvement generation model may, upon instruction from the improvement proposal inference request unit, refer to a performance-related information database containing performance information and value information of the organization, and / or an intangible resource information database storing the intangible resource information data, during inference.

[0079] (59) In the organizational value improvement support system described in (57) above, when the organization is a corporation, the inference request included in the improvement inference request data may include a request for proposals to increase the price-to-book ratio of the organization.

[0080] (60) In the organizational value improvement support system described in (42) above, the intangible resource information utilization management unit may be provided with a utilization suggestion unit that executes processing including proposing utilization of the intangible resource information to an operator of the utilization input / output device.

[0081] (61) In the organizational value improvement support system described in (60) above, the utilization suggestion unit may include a contribution candidate inference request unit. The contribution candidate inference request unit may generate proposal inference request data using data including information on the organization's planned activities as input. The proposal inference request data may include an inference request for requesting a proposal generative model to propose contribution candidate intangible resource information, which is the intangible resource information expected to contribute to improving the value of the organization. Processing may be performed that includes transmitting the proposal inference request data to the proposal generative model and receiving inference result data including the contribution candidate intangible resource information.

[0082] (62) In the organizational value improvement support system described in (61) above, the proposed generation model may, upon instruction from the contribution candidate inference request unit, refer to a performance-related information database that accumulates data including information on the organization's planned activities, and / or an intangible resource information database that stores the intangible resource information data, at the time of inference.

[0083] (63) In the organizational value improvement support system described in (61) above, the intangible resource information utilization management unit may be configured to generate internal management instruction data indicating the degree of confidentiality management of the intangible resource information data, and to manage the intangible resource information data confidentially based on the internal management instruction data.

[0084] (64) In the organization value improvement support system described in (63) above, the utilization suggestion unit may include a proposal restriction setting unit. The proposal restriction setting unit may execute a process including identifying organization members involved in the activity schedule, determining a range of the intangible resource information data accessible to the identified organization members based on the internal management instruction data, and enabling the utilization suggestion unit to select the contribution candidate intangible resource information from a population of intangible resource information included within the range.

[0085] (65) In the organization value improvement support system described in (61) above, the intangible resource information utilization management unit may include a contribution candidate selection unit. The contribution candidate selection unit may execute processing including generating utilization display data for displaying the contribution candidate intangible resource information on the utilization input / output device, transmitting the utilization display data to the utilization input / output device, inputting the intangible resource information selected by the operator from the utilization input / output device, generating utilization history management data indicating that the selected intangible resource information is to be utilized as a utilization history, and outputting the utilization history management data to a history management unit that executes processing including generating utilization history data including utilization history of the intangible resource information.

[0086] (66) The present disclosure includes an organizational value improvement support system including at least one processor and a memory, wherein the memory stores non-transitory instructions, and when the instructions are executed by the processor, the processor is configured to execute the following processes (i) to (iii) as a value allocation calculation unit by executing the instructions: (i) Identifying the operator requesting access to the IACC information data stored in the IACC information database, including information about the IACC, which is an intangible asset created by members of the organization; (ii) for a third-party IACC not created by the operator and accessible to the operator, determining the number of citations of the third-party IACC in response to input data indicating that the operator cites the third-party IACC to create a new IACC; (iii) The value points of the other IACC are determined by adding 1 to the number of citations, and the value allocation of the other IACC is calculated by allocating the total internal evaluation amount of the IACC in accordance with the proportion of the value points to the total value points of the IACCs stored in the IACC information database.

[0087] (67) In the organization value improvement support system described in (66) above, the processor may be configured to further execute the following process (iv) as the value allocation calculation unit: (iv) Creating display data for displaying the other IACC and the value allocation of the other IACC, and outputting the data to a display device.

[0088] (68) In the organizational value improvement support system described in (66) above, when the processor executes the process (iii) as the value allocation calculation unit, it may execute a process of setting the total internal valuation amount of the IACC to the total internal valuation amount of non-financial capital obtained by subtracting the total amount of financial assets from the market capitalization of the organization.

[0089] (69) In the organization value improvement support system described in (68) above, the processor may be configured to further execute the following process (v) as the value allocation calculation unit: (v) Dividing said Total Internal Valuation of Non-Financial Capital by a Total Value Point Sum, which is the sum of said Value Points of all said IACC Information Data for which calculation is possible, to determine an Average Value Allocation.

[0090] (70) In the organization value improvement support system described in (69) above, the processor may be configured to further execute the following process (vi) as the value allocation calculation unit: (vi) creating and outputting to a display device display data for displaying said average value allocation and said total internal valuation of non-financial capital; [Effects of the Invention]

[0091] According to the present disclosure, a system for supporting organizational value improvement is provided that increases the value of IACC, which is an intangible asset created by organizational members, supports organizational members in acting based on the intangible asset, and enables appropriate management of the intangible asset. Furthermore, according to a preferred embodiment of the present disclosure, it is possible to reduce the stress that organizational members may experience when using a pre-trained generative model, make organizational members feel that they are acting independently despite using a pre-trained generative model, and allocate and manage intangible assets (intangible resources) within the organization to appropriate non-financial capital. [Brief explanation of the drawings]

[0092] [Figure 1] 1 is a block diagram illustrating an organization value improvement support system having an organization value improvement support device according to an embodiment of the present disclosure. [Figure 2] 1 is a flow diagram (part 1) illustrating information processing performed by an organization value improvement support device according to an embodiment of the present disclosure. [Figure 3] FIG. 10 is a flow diagram (part 2) illustrating information processing performed by the organization value improvement support device according to an embodiment of the present disclosure. [Figure 4] FIG. 10 is a flow diagram (part 3) illustrating information processing performed by the organization value improvement support device according to an embodiment of the present disclosure. [Figure 5] FIG. 10 is a flow diagram (part 4) illustrating information processing performed by an organization value improvement support device according to an embodiment of the present disclosure. [Figure 6] FIG. 10 is a diagram showing an example of display of IACC information. [Figure 7] FIG. 10 is a diagram showing an example of display of intangible resource information. [Figure 8] This is a diagram showing a specific example in which the information contained in the first inference result data and the information contained in the IACC information data are displayed in a comparable manner on an image display unit, which is the output unit of the first input / output device. [Figure 9] FIG. 1 is an explanatory diagram (part 1) of an example of an organization value improvement support program according to an embodiment of the present disclosure. [Figure 10] FIG. 2 is an explanatory diagram (part 2) of an example of an organization value improvement support program according to an embodiment of the present disclosure. [Figure 11] FIG. 10 is an explanatory diagram (part 3) of an example of an organization value improvement support program according to an embodiment of the present disclosure. [Figure 12] FIG. 4 is an explanatory diagram (part 4) of an example of an organization value improvement support program according to an embodiment of the present disclosure. [Figure 13] FIG. 5 is an explanatory diagram (part 5) of an example of an organization value improvement support program according to an embodiment of the present disclosure. [Figure 14] FIG. 6 is an explanatory diagram (part 6) of an example of an organizational value improvement support program according to an embodiment of the present disclosure. [Figure 15] FIG. 7 is an explanatory diagram (part 7) of an example of an organizational value improvement support program according to an embodiment of the present disclosure. [Figure 16] FIG. 10 is a block diagram illustrating an organization value improvement support system having an organization value improvement support device according to another embodiment of the present disclosure. [Figure 17] FIG. 10 is a diagram illustrating the functions of an intangible resource information management unit. [Figure 18] FIG. 10 is a diagram illustrating the function of an auxiliary information management unit. [Figure 19] FIG. 1 is a diagram illustrating an organization value improvement support device that executes an entry process. [Figure 20] FIG. 1 is a diagram illustrating an organization value improvement support device that executes an intangible resource management process. [Figure 21] FIG. 3 is a block diagram illustrating the function of an input information generating unit. [Figure 22] FIG. 2 is a block diagram illustrating the function of an initial inference request generation unit. [Figure 23] FIG. 10 is a block diagram illustrating the function of an intangible resource inference request generation unit. [Figure 24] FIG. 10 is a block diagram illustrating an organization value improvement support system having an organization value improvement support device according to another embodiment of the present disclosure. [Figure 25] FIG. 1 is an explanatory diagram of organizational value financial statements. [Figure 26] FIG. 10 is a diagram showing a specific example in which the display of intangible resource information data includes the value allocation of the corresponding IACC as a display element. [Figure 27] FIG. 1 is a diagram showing a specific example of an enterprise value financial statement. [Figure 28] FIG. 10 is an explanatory diagram (part 1) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 29] FIG. 10 is an explanatory diagram (part 2) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 30] FIG. 10 is an explanatory diagram (part 3) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 31] FIG. 10 is an explanatory diagram (part 4) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 32] FIG. 5 is an explanatory diagram (part 5) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 33] FIG. 6 is an explanatory diagram (part 6) of another example of the organizational value improvement support program according to an embodiment of the present disclosure. [Figure 34] FIG. 7 is an explanatory diagram (part 7) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 35] FIG. 8 is an explanatory diagram (part 8) of another example of the organizational value improvement support program according to an embodiment of the present disclosure. [Figure 36] FIG. 9 is an explanatory diagram (part 9) of another example of the organization value improvement support program according to an embodiment of the present disclosure. [Figure 37] FIG. 10 is an explanatory diagram (part 10) of another example of the organizational value improvement support program according to an embodiment of the present disclosure. [Figure 38] FIG. 11 is an explanatory diagram (part 11) of another example of the organizational value improvement support program according to an embodiment of the present disclosure. BEST MODE FOR CARRYING OUT THE INVENTION

[0093] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0094] 1 is a block diagram illustrating an organization value improvement support system including an organization value improvement support device according to an embodiment of the present disclosure. In the following description of the embodiment, a large-scale language model will be described as an example of a pre-trained generative model.

[0095] As shown in FIG. 1, an organizational value improvement support system 1000 according to one embodiment of the present disclosure includes an organizational value improvement support device 100, a first large-scale language model (first generative model 910), an initial large-scale language model (initial generative model 920), and an intangible resource large-scale language model (intangible resource generative model 930), each of which is a pre-trained generative model, an IACC (Intangible Assets created by Constituent Members) information database 300 (in FIG. 1, "database" is abbreviated to "DB") that stores IACC information, an intangible resource information database 310 that stores intangible resource information, a performance-related information database 320 that stores performance-related information, an auxiliary information database 330 that stores auxiliary information such as current affairs information, an SDGs database 340 that stores SDG target information, a first input / output device 400 used by members such as employees, and an initial input / output device 410.

[0096] The organization value improvement support system 1000 includes at least one processor and memory, and non-transient instructions are stored in the memory. When the instructions are executed by the processor, the processor is configured to perform information processing by the first inference request generation unit 110 (described later) and the like. In this embodiment, as a specific example of the organization value improvement support system 1000, the organization value improvement support device 100 includes a processor and a memory.

[0097] The organization value improvement support device 100 has the following parts in relation to information processing for brushing up IACC information data and storing it in the IACC information database 300 (this information processing is also referred to as the "brushing up process" in this specification). a first inference request generating unit 110 for generating first inference request data; A first transceiver 120 for transmitting and receiving data to and from the first generative model 910 An input information generation unit 130 that generates a first input information data group to be input to the first inference request generation unit 110 A comparison display generation unit 140 that generates data for displaying the inference results of the first generative model 910 A publicly known representation generating unit 150 that generates non-publicly known representation data based on the inference result of the first generative model 910. A secret management setting unit 160 that performs information processing related to secret management based on the inference result of the first generation model 910. A first IACC information registration unit 170 that performs processing to store IACC information data in the IACC information database 300 based on the judgment results of the members regarding the inference results of the first generative model 910.

[0098] A specific example of the organization value improvement support device 100 is a computer device having a semiconductor device and a control unit that processes information, and the control unit is equipped with each of the above-mentioned units. For example, the semiconductor device that processes information may be a processor. The processor may be one or more single-chip or multi-chip microprocessors designed and / or manufactured by Intel Corporation, Advanced Micro Devices, Inc. (AMD), Arm Holdings (Arm), Apple Computer, Inc., etc. Examples of microprocessors include CELERON (registered trademark), PENTIUM (registered trademark), CORE i3, CORE i5, and CORE i7 from Intel Corporation, AMD OPTERON (registered trademark), PHENOM, ATHLON, TURION, and RYZEN (registered trademark) from AMD, and CORTEX-A, CORTEX-R, and CORTEX-M from Arm ("CORTEX" is a registered trademark of Arm).

[0099] The first inference request generating unit 110 receives as input the information data included in the first input information data group generated by the input information generating unit 130 and generates first inference request data.

[0100] In this embodiment, the input information generation unit 130 collects specific IACC information data from the IACC information database 300 based on designation data input from the first input / output device 400. The designation data includes a command that designates that the specific IACC information data stored in the IACC information database 300 is to be the target of information processing performed by the organization value improvement support device 100.

[0101] The IACC information data includes IACC information, which is information about IACC, an intangible asset created by a member, written in a predetermined first format. In this embodiment, the data also includes information indicating the type of non-financial capital to which the IACC belongs (also referred to herein as "first non-financial capital"). Because IACC is a useful intangible asset created by a member, it is used in the organization's activities (business activities). Therefore, IACC can also be considered capital within the organization. When IACC is used within an organization, it can be classified into one of five types of non-financial capital. Note that if IACC is used as a subject for licensing out or sale, it will have the properties of financial capital.

[0102] The first format may be in various forms. A non-limiting example of the first format is a format having a plurality of target fields in which text information or image / video information is placed. Non-limiting examples of the plurality of target fields are as follows: (A) Strengths and selling points: This target field contains text that briefly describes the characteristics of the IACC, such as the advantages of the IACC over other IACCs. (B) Summary: This target field contains text describing the specific content of the IACC. (C) Purpose and Objective: This target field contains text describing the purpose and objective that was set when the IACC was conceived. (D) Results or Effects: This target field contains text describing the results or effects that the IACC will bring about. (E) Concerns and Issues: This target field contains text describing the concerns and issues that were recognized when conceiving the IACC. (F) Type of Capital: This target field contains text describing the type of non-financial capital to which the IACC belongs. (G) Status: This target field contains text or the like that describes information regarding the secret management of the IACC. An example of display of IACC information written in the first format having target fields according to the above specific example is shown in FIG.

[0103] The creator of the IACC information data (the "initial operator" described below) and the first operator who creates the designated data, i.e., the person who is trying to brush up the IACC information, may or may not be the same person. Each IACC information data has a management attribute that sets the level of secret management.

[0104] Since the IACC included in any of the IACC information data has a certain degree of usefulness in organizations such as companies, the organizational value improvement support device 100 according to this embodiment performs confidentiality management to the extent that it prevents leakage outside the organization. As a result, if the IACC satisfies the requirement of non-publicity, it can be protected as a trade secret as defined in the Unfair Competition Prevention Act.

[0105] If the IACC is useful and not publicly known, the IACC should be protected as a trade secret and may be a candidate for the object of industrial property rights such as patent inventions. In this case, the organization value improvement support device 100 according to this embodiment performs a high level of confidentiality management on the IACC information data.

[0106] One example of such high-level secrecy management is a management system that limits access (viewing and modification) to IACC information data related to the IACC to a specific range, such as the creator of the IACC information data (initial operator) and its administrator, and requires that the creator of the IACC information data (initial operator) and the creator of the designated data (first operator) are the same. Note that in the following explanation, it is assumed that the creator of the IACC and the creator of the IACC information data (initial operator) that includes that IACC are the same.

[0107] In addition, if the IACC is not private and strict confidentiality management is not required, the organizational value improvement support device 100 according to this embodiment allows an organization member to browse IACC information data created by other members of the organization, select specific IACC information data, and create new IACC information based on the selected data. This activity constitutes the utilization of an organization's IACC by members other than its creator. In other words, this constitutes the active utilization of intangible resources, using an existing IACC as input to produce a new intangible (intangible resource) with financial value as output. Such activities involving multiple members to grow the IACC increase the value, particularly the usefulness, of the IACC. If the IACC continues to grow and become private as a result of these activities, the IACC becomes a potential target for trade secret or industrial property rights protection. In other words, the organizational value improvement support device 100 of this embodiment supports the active utilization of intangible assets such as intellectual property within an organization, thereby increasing the financial value of the intangible assets, and as a result, intangible resources are strengthened, making it possible to realize an improvement in organizational value.

[0108] In this embodiment, the input information generation unit 130 identifies the type of first non-financial capital, specifically, one of the five non-financial capitals (natural capital, human capital, intellectual capital, manufactured capital, and social and relationship capital), from the information included in the IACC information data collected from the IACC information database 300. Then, it collects intangible resource information data of the same type as the identified first non-financial capital from the intangible resource information database 310.

[0109] In this embodiment, the input information generation unit 130 collects performance-related information data, including target values ​​for the organization's financial and performance indicators, from the performance-related information database 320. Examples of financial and performance indicators include indicators that make up the ROIC tree, and in particular, the nine indicators that make up the ends of the tree (sales growth rate, cost of sales ratio, R&D ratio to sales, labor cost ratio to sales, accounts receivable turnover, accounts payable turnover, inventory turnover, tangible fixed asset turnover, and intangible fixed asset turnover) are specific examples. Target values ​​for these indicators are set, for example, on a fiscal year basis, and data including information indicating the latest target values ​​is stored in the performance-related information database 320.

[0110] Financial and performance indicators are not limited to indicators that directly affect business performance, such as the nine indicators listed above, which are set as so-called KPIs (Key Performance Indicators), but may also be indicators that affect future performance, such as EIOFs (Early Indicators of Future Success). Furthermore, if an organization is not a corporation but a non-profit organization, appropriate performance indicators, such as the degree of objective achievement, results, and activity results, as well as indicators for evaluating such performance, are set according to the organization's activities, and performance-related information data includes target values ​​for the set indicators.

[0111] In this embodiment, the input information generation unit 130 collects auxiliary information data from the auxiliary information database 330. The auxiliary information includes current information about the organization, trends (industry trends) in the organization's field of activity (if the organization is a company, the field of business the company conducts), organizational philosophy such as the company philosophy, and basic information about management and operation within the organization such as KGI (Key Goal Indicator).

[0112] In one example, current affairs information is collected by being provided by news providers on a regular or irregular basis. The collected current affairs information data is stored in the auxiliary information database 330. In another example, an information processing unit (e.g., auxiliary information management unit 330A described later in FIG. 18) associated with the auxiliary information database 330 may have a search function for an external information network ExNW such as the Internet, search for information useful for the brush-up process, and store the search results. When this search function is provided, the IACC to be the target of the brush-up process may be input from the IACC information database 300, and search conditions may be created from the input IACC.

[0113] In this embodiment, the input information generation unit 130 inputs personal emotion information data from the first input / output device 400 operated by the first operator. A specific example of the first input / output device 400 is a personal computer. The personal emotion information data includes information indicating a first emotion, which is an emotion that the member (first operator) who created the designated data expects when performing a task. Specific examples of the first emotion include "feeling happy," "feeling a sense of accomplishment," and "excited." The first emotion can be any emotion, but from the perspective of improving the stability of subsequent information processing, particularly the stability of the analysis results in the first generative model 910, it may be preferable for the result to be one of a plurality of emotions that have been preset.

[0114] The input information generation unit 130 may generate the first input information data group by selecting only the data to be input to the first inference request generation unit 110 at the stage of collecting data from each database and the first input / output device 400, and only combining the input data in the input information generation unit 130. Alternatively, the input information generation unit 130 may generate the first input information data group by performing processing such as selection and combination on the information included in the data input from each database and the first input / output device 400 in the input information generation unit 130.

[0115] The first inference request generation unit 110 generates first inference request data using as input the information data included in the first input information data group output by the input information generation unit 130. The first inference request data includes first inference requirements, which are requests for causing the first generation model 910 to generate first inference result data.

[0116] The first transmission / reception unit 120 transmits the first inference request data generated by the first inference request generation unit 110 to the first generation model 910, and receives first inference result data including the first proposal generated by the first generation model 910 that received the first inference request data. Note that the data transmitted and received by the first transmission / reception unit 120 may be transmitted via a storage device (not shown). That is, the first inference request data generated by the first inference request generation unit 110 may be stored in a storage device (not shown), and the first transmission / reception unit 120 may read this stored first inference request data from the storage device (not shown) and transmit it to the first generation model 910. In this specification, the input and output of data to and from each unit includes the exchange of data via such a storage device.

[0117] The first generative model 910 may or may not be a component of the organizational value improvement support system 1000 according to this embodiment. In the former case, considerable costs will be incurred for acquisition and maintenance, but the first generative model 910 can be managed confidentially on its own initiative. In the latter case, the burden of acquisition and maintenance is alleviated, but it will be difficult to manage the first generative model 910 independently, and therefore the confidentiality management level of the first generative model 910 will be set by a contract with the owner of the first generative model 910.

[0118] In this embodiment, the first inference request includes generating a first proposal for improving financial and performance indicators included in the performance-related information data based on the IACC information data and the intangible resource information data. For example, if one of the target values ​​of the financial and performance indicators is set to increase the sales growth rate to 15%, the first proposal includes the result of improving the IACC with a view to achieving this target.

[0119] There are many different ways to improve the IACC, but including these requirements in the first inference requirement can help direct the IACC improvement efforts toward achieving a 15% sales growth rate. Improving financial and performance indicators can be achieved not only by achieving set targets, but also by exceeding them. Setting higher targets can increase the likelihood that more effective proposals will be included in the first proposal.

[0120] Here, when refining the IACC, the first inference requirement includes comparing the intangible resources and supporting information of the same type as the first type of non-financial capital. This allows for the utilization of the organization's intangible resources and the latest trends indicated by the supporting information to be taken into consideration when generating specific proposals for improving the IACC. As a result, the first proposal will include proposals that bring the direction of IACC improvement closer to the organization's growth direction. In other words, this corresponds to aligning the IACC creation vectors of each organization's members with the organization's growth vector, thereby increasing the efficiency of the organization's human capital utilization and, as a result, promoting the improvement of organizational value.

[0121] Furthermore, because financial and performance indicators are parameters related to financial capital, improving the IACC, which belongs to non-financial capital, from the perspective of improving financial and performance indicators means increasing the relevance between financial and non-financial capital. Therefore, such improvements will enable investors who are familiar with information related to financial capital to more directly grasp the capabilities of non-financial capital (visualizing non-financial capital), and will further strengthen its utilization and economic evaluation, making it easier for the capabilities of non-financial capital to be reflected in financial capital.

[0122] In this embodiment, the first inference request includes generating a behavioral indicator proposal that contributes to improving financial and performance indicators in line with the first emotion included in the personal emotion information data. Aligning the IACC creation vector with the organizational growth vector is desirable from the perspective of organizational growth, but if appropriate consideration is not given to the emotional burden of members, there is a concern that the organization's growth may result in placing excessive, unwanted burdens on members. Therefore, by including a behavioral indicator proposal incorporating the first emotion as an element in the first inference request, the first proposal generated by the first generative model 910 includes content that achieves members' emotional satisfaction, making it less likely that a proposal that places excessive, unwanted burdens on members will be generated. Having an organization as a place where members can obtain emotional satisfaction increases members' engagement with the organization and contributes to improving the stability of the organization's growth.

[0123] In this embodiment, the first inference request includes, if the IACC information data includes human capital-related information, which is information about systems and institutions for developing knowledge, skills, and abilities (KSAs) of members (first case), including an improvement proposal with a view to increasing human capital in the first proposal; and, if the IACC information data does not include human capital-related information (second case), adding an improvement proposal with a view to increasing at least one of manufactured capital, social and relationship capital, and natural capital to the first proposal. In the first case described above, if the first proposal includes an improvement proposal related to human capital, the first proposal may also include an improvement proposal with a view to increasing non-financial capital other than human capital (intellectual capital, manufactured capital, social and relationship capital, and natural capital). Alternatively, the first proposal may include an improvement proposal with a view to increasing the organization's management assets, such as know-how including trade secrets.

[0124] As will be described later, the organizational value improvement support device 100 according to this embodiment utilizes an initial large-scale language model (initial generative model 920, sometimes referred to herein as a "second large-scale language model") when setting the type of non-financial capital (first non-financial capital) to which the IACC belongs. Even if such information with a certain degree of objectivity is used, it is not easy in reality to appropriately set the first non-financial capital. In particular, because the IACC is an intangible asset including intellectual assets and the entity that creates the IACC is "people," it is common for the first operator to set intellectual capital or human capital as the first non-financial capital.

[0125] Therefore, in this embodiment, the first inference request includes a request to propose improvements in a direction that can be determined to be non-financial capital other than intellectual capital and human capital, that is, natural capital, manufactured capital, or social and relationship capital, among the five types of non-financial capital. This optimizes the allocation of IACC to the appropriate type of non-financial capital, making it easier to more appropriately evaluate the value of an organization, such as corporate value.

[0126] In addition, if the IACC includes human capital-related information on the systems and institutions for developing the knowledge, skills, and abilities (KSAs) of its members, the IACC should be determined to belong to human capital, and therefore the first generation model 910 is to propose improvements to the mechanisms for improving the IACC from the perspective of increasing human capital.

[0127] Therefore, the first inference result will include improvement proposals for IACC that belong to either natural capital, manufactured capital, or social and relationship capital, except for those that should be determined to belong to human capital, and there will tend to be fewer IACC that belong to intellectual capital. IACC that are proposed to belong to intellectual capital in this process may have characteristics that differ from other IACC, such as affecting multiple types of non-financial capital and making them difficult to classify as a specific type of non-financial capital. Aggregating such IACC into intellectual capital may make it easier to manage (allocate) non-financial capital.

[0128] In this embodiment, the first inference result data received by the first transmission / reception unit 120 is input to the comparison display generation unit 140. As mentioned above, the case where the first inference result data received by the first transmission / reception unit 120 is stored in a storage device (not shown) before being input to each unit of the organization value improvement support device 100, and this stored data is read out by each unit such as the comparison display generation unit 140, is included in the case where "the first inference result data is input to the comparison display generation unit 140."

[0129] In addition to the first inference result data, the comparison display generation unit 140 also receives IACC information data as input. The comparison display generation unit 140 may directly access the IACC information database 300 to obtain the IACC information data, or may obtain the IACC information data from the input information generation unit 130. The IACC information data collected by the input information generation unit 130 may be stored in a storage device (not shown), and the comparison display generation unit 140 may read this data.

[0130] The comparison display generation unit 140 executes processing including generating comparison display data that displays the information included in the first inference result data in a first format and allows comparison with the information included in the IACC information data. By displaying the two pieces of information in a manner that allows comparison, members can easily confirm how the first generative model 910 has been brushed up. Furthermore, by displaying the information before and after the brush-up in a manner that allows comparison, it becomes easier to determine the validity of the information after the brush-up.

[0131] The comparative display data may be a signal, such as an image display signal or a print signal, that is directly displayed on the output unit of the first input / output device 400 operated by the member, or the data required for the comparative display may be input to the first input / output device 400, and the first input / output device 400 may generate a display signal for the output unit. The comparative display data may be encrypted data. Figure 8 is a diagram showing a specific example in which information included in the first inference result data and information included in the IACC information data are displayed in a comparative manner on an image display unit, which is the output unit of the first input / output device 400.

[0132] In this embodiment, the first inference request includes generating publicly known proposal information including the result of examining whether the first proposal is publicly known. Upon receiving the first inference request, the first generative model 910 may, for example, perform an Internet search to check whether the first proposal generated by the first generative model 910 is publicly known on the Internet.

[0133] If the publicly known proposal information includes a proposal that is publicly known, the first inference request may further include information indicating the publicly known information that is determined to include the first proposal. In this case, the first inference result data includes the publicly known information that is determined to have the same content as the first proposal. A specific example of the publicly known information is the URL of a web page.

[0134] If the publicly known proposal information includes a proposal that the information is not publicly known, the first inference request may further include information indicating publicly known information (nearest publicly known information) that is determined to be closest to the first proposal. In this case, the first inference result data includes publicly known information that is not the same as the first proposal but that the first generative model 910 determines to be closest to the first proposal among the search result information.

[0135] In this way, when the information that serves as the basis for the public knowledge judgment is included in the first inference result data, it becomes easy for the first operator or other members of the organization to consider whether the public knowledge proposal information included in the first inference result data is valid, which makes it less likely that information from the first generation model 910 will be believed without consideration.

[0136] The publicly known indication generator 150 receives the first inference result data from the first generative model 910, checks the content of the publicly known proposal information included in the data, and determines whether the first proposal is publicly known or not. If it determines that the first proposal is not publicly known, it executes a process that includes generating non-publicly known indication data including a notification indicating that the first proposal may be confidential information. The non-publicly known indication data is data that can display text such as "This IACC may be non-publicly known information and will be managed as confidential information. Please be careful not to disclose it to anyone outside the organization" on the image display unit, which is the output unit of the first input / output device 400. In this way, by informing members that the information they are currently handling may be confidential information and encouraging them to manage it confidentially, it is possible to prevent problems such as the loss of novelty or non-publicity of IACC information. This contributes to the appropriate protection of potential industrial property rights, such as patent inventions, and trade secrets within an organization.

[0137] Figure 2 is a flow diagram (part 1) explaining the information processing (brush-up process) performed by an organization value improvement support device according to one embodiment of the present disclosure, specifically, a flow diagram of the information processing performed by the first inference request generation unit 110, the first transmission / reception unit 120, the input information generation unit 130, the comparison display generation unit 140, and the public knowledge display generation unit 150 provided in the organization value improvement support device 100 described above.

[0138] First, the input information generation unit 130 collects input information, including accessing each database and obtaining data from the first input / output device 400 (step S101), and generates a first input information data group (step S102). Next, the first inference request generation unit 110 uses the first input information data group as input to generate first inference request data (step S103), and outputs the first inference request data to the first transmission / reception unit 120. Subsequently, the first transmission / reception unit 120 transmits the first inference request data to the first generation model 910 (step S104).

[0139] The first inference result data generated by the first generative model 910 is received by the first transmitting / receiving unit 120 (step S105). Based on the first proposal included in the received first inference result data, the comparison display generating unit 140 generates comparison display data (step S106).

[0140] Furthermore, the public knowledge display generation unit 150 checks the public knowledge proposal information included in the first inference result data (step S107), and determines whether the first proposal is unknown based on the information (step S108). If the public knowledge display generation unit 150 determines that the first proposal is unknown, it generates unknown display data (step S109), and the comparison display data and the unknown display data are sent (output) to the first input / output device 400 as data for display (step S110). If the public knowledge display generation unit 150 determines that the first proposal is known, the comparison display data is sent (output) to the first input / output device 400 as data for display (step S110).

[0141] In this embodiment, the first IACC information registration unit 170 generates draft IACC information data in which the information included in the first inference result data is written in a format corresponding to the display of IACC information data, i.e., the first format, and then executes processing including replacing the draft IACC information data with the IACC information data and storing the IACC information data in the IACC information database 300, on the condition that an instruction to accept the draft IACC information data has been input.

[0142] As described above, the first generative model 910 generates suggestions for improving the IACC information data included in the first input information data group. By replacing the original IACC information data with the IACC information data based on these suggestions, consistency with organizational growth is improved, and the IACC information with increased asset value is accumulated as an intangible resource within the organization. Furthermore, because consideration is given to individual feelings when generating the improved IACC information data, it is expected that the motivation of the IACC creator to participate in activities within the organization will increase.

[0143] On the other hand, even if consideration is given to the personal feelings of members, there is a concern that using the IACC information data generated by the first generative model 910 as the refined IACC information data without considering the intentions of the members who created the IACC associated with that data may affect the motivation of members. In particular, because the IACC information data contains information such as behavioral indicators that could potentially constrain members' subsequent activities within the organization, there is a concern that members may feel that their actions are being controlled by so-called "AI." Such feelings could lead to a decline in engagement with the organization and even with society. For example, if a person intends to refine their IACC in order to feel "excited," but ultimately feels that they are being manipulated by a cold, calculated machine, this could lead to a strong distrust of the IACC refinement process.

[0144] Therefore, in the organizational value improvement support device 100 of this embodiment, the IACC information data based on the proposal of the first generation model 910 is not simply replaced and stored in the existing IACC information database 300, but is updated in a state that appropriately reflects the intentions of the first operator operating the first input / output device 400.

[0145] Figure 3 is a flow diagram (part 2) explaining the information processing (brush-up process) performed by an organizational value improvement support device according to one embodiment of the present disclosure, and is a flow diagram of information processing in which the first IACC information registration unit 170 uses the brushed-up IACC information proposed by the first generation model 910 to store the IACC information in the IACC information database 300.

[0146] Specifically, the first IACC information registration unit 170 first collects the first inference result data generated by the first generative model 910 (step S201), generates draft IACC information data written in a first format based on the collected data (step S202), and performs a transmission process to display the draft IACC information data on the first input / output device 400 (step S203). This draft IACC information data may be based on data generated by the comparison display generation unit 140. Alternatively, that data may be used as the draft IACC information data as is. In this case, the comparison display generation unit 140 is responsible for part of the processing to be performed by the first IACC information registration unit 170, specifically, for executing step S220 surrounded by a dotted line in FIG. 3.

[0147] In this way, the draft IACC information data is displayed on the first input / output device 400 and can be recognized by the first operator. The first operator checks the contents of the draft IACC information data and determines whether the contents of the draft IACC information data are acceptable as his / her own IACC information or whether there are any items that need to be corrected. If there are any items that need to be corrected in the contents of the draft IACC information data, he / she sends a correction instruction to the first IACC information registration unit 170 via the first input / output device 400.

[0148] The first IACC information registration unit 170 determines whether or not a correction instruction is included in the input signal from the first input / output device 400 (step S204). If it is determined in step S204 that a correction instruction has been input, the first IACC information registration unit 170 reflects the correction instruction in the information included in the first inference result data, generates IACC information data under correction in which the reflected result is written in a format corresponding to the display of IACC information data, i.e., in the first format (step S205), and performs a transmission process to display the data on the first input / output device 400 (step S206).

[0149] In this way, the IACC information data under correction is displayed on the first input / output device 400 and can be recognized by the first operator. The first operator checks the contents of the IACC information data under correction and determines whether the contents of the IACC information data under correction are acceptable as his / her own IACC information, or whether there are any further corrections that need to be made. If the first operator determines that there are no corrections that need to be made to the contents of the IACC information data under correction, he / she sends an approval instruction to the first IACC information registration unit 170 via the first input / output device 400.

[0150] The first IACC information registration unit 170 determines whether or not an acceptance instruction is included in the input signal from the first input / output device 400 (step S207). If it is determined in step S207 that an acceptance instruction has been input, the first IACC information registration unit 170 sets the IACC information data being corrected as the IACC information data to be saved in place of the existing IACC information data (step S208).

[0151] If it is determined in step S204 that no correction instruction has been input, the first IACC information registration unit 170 determines whether or not an acceptance instruction is included in the input signal from the first input / output device 400 (step S209). If it is determined in step S209 that an acceptance instruction has been input, the first IACC information registration unit 170 sets the draft IACC information data as the IACC information data to be saved by replacing the existing IACC information data (step S210). If it is determined in step S209 that an acceptance instruction has not been input, the process returns to step S204. This puts the process into a state of waiting for input from the first input / output device 400. A predetermined interval (for example, one second) may be provided between step S209 and step S204, which is executed thereafter.

[0152] If it is determined in step S207 that an acceptance instruction has not been input, the first IACC information registration unit 170 determines whether or not there is another correction instruction in the input signal from the first input / output device 400 (step S211). If there is a correction instruction in step S211, step S205 is executed to generate IACC information under correction that reflects the new correction instruction, and step S206 and subsequent steps are executed again. If it is determined in step S211 that a re-correction instruction has not been input, the process returns to step S207. This puts the process into a state of waiting for input from the first input / output device 400. A predetermined interval (for example, one second) may be provided between step S211 and the subsequently executed step S207.

[0153] The above processing realizes setting the data reflecting the intention of the first operator as the IACC information data to be replaced and saved in the IACC information database 300. Processing may be performed to save this data as is in the IACC information database 300, but the organization value improvement support device 100 according to this embodiment further determines whether or not the data should be managed as confidential information, and if it should be managed as confidential information, performs processing to do so.

[0154] As described above, in the organization value improvement support device 100 according to this embodiment, the first inference request included in the first inference request data generated by the first inference request generation unit 110 includes generating publicly known proposal information including the result of an investigation into whether the first proposal is publicly known. Therefore, the first inference result data includes the publicly known proposal information. Therefore, the organization value improvement support device 100 includes a secret management setting unit 160 that receives as input the IACC information data to be stored in the IACC information database 300 generated by the first IACC information registration unit 170 and the first inference result data, determines whether the first proposal is publicly known or not based on the publicly known proposal information, and, if it determines that the first proposal is not publicly known, sets the management attribute of the IACC information data to be stored in the IACC information database 300 to data including a candidate industrial property object.

[0155] IACC information data whose management attribute is set to indicate that it contains potential industrial property rights objects is managed differently in the IACC information database 300 than IACC information data whose management attribute is not set to such. Specifically, to ensure novelty and non-publicity, high levels of access restrictions are imposed to prevent unexpected information leaks. Furthermore, when changes are made to IACC information data, a log is recorded that includes the person who made the change and the details of the change. Furthermore, as necessary, measures are implemented to prevent data loss even if an unexpected malfunction occurs in the storage device that makes up the database, such as redundant data storage.

[0156] Steps S212 and S213 in Fig. 3 show information processing performed by the secret management setting unit 160. In step S212, the secret management setting unit 160 determines whether the first proposal is not publicly known. If it is determined to be not publicly known in step S212, the secret management setting unit 160 sets management attributes in step S213, and the first IACC information registration unit 170 performs a transmission process to store the IACC information data, for which the management attributes have been set, in the IACC information database 300 (step S214). In Fig. 3, as a specific example, the information management unit 260 generates information management instruction data including information on the management attributes of the data, separately from the transmission of the IACC information data, and performs a transmission process for the data (step S215). The IACC information database 300 stores the IACC information data while appropriately managing it in accordance with the management attributes included in the information management instruction data.

[0157] The process of step S212 is performed in step S108 (FIG. 2) by the publicly known indication generation unit 150. Therefore, if the determination result of step S108 is stored in a storage device (not shown), the secret management setting unit 160 may read the determination result of step S108 from the storage device (not shown) instead of performing the process of step S212, and perform subsequent processes based on the determination result.

[0158] If it is determined in step S212 that the information is publicly known, the first IACC information registration unit 170 performs processing to store the IACC information data in the IACC information database 300 without setting any special management attributes (step S214). In this case, the management attribute information included in the information management instruction data generated in step S215 includes, for example, access restrictions equivalent to organizational confidentiality. The IACC information database 300 stores the IACC information data while managing it appropriately in accordance with the management attributes.

[0159] The organizational value improvement support device 100 has the following parts related to information processing (in this specification, this information processing is also referred to as the "entry process") for generating IACC information data that can also be brushed up and storing it in the IACC information database 300. An initial inference request generation unit 180 (second inference request generation unit) that generates initial inference request data (second inference request data) An initial transmission / reception unit 190 (second transmission / reception unit) that transmits and receives data to and from an initial large-scale language model (initial generation model 920, sometimes referred to as a "second large-scale language model" in this specification). A capital display generator 200 that generates data for displaying the inference results of the initial generative model 920 An initial IACC information registration unit 210 (second information registration unit) that performs processing to store the IACC information data to which the inference results of the initial generative model 920 have been added in the IACC information database 300.

[0160] An initial operator (second operator), who is a member who intends to execute the entry process and create IACC information data, inputs IACC basic information data, which is basic information of the IACC information data and includes information about the IACC, into the organizational value improvement support device 100 via the initial input / output device 410 (second input / output device). The initial inference request generation unit 180 generates initial inference request data as this IACC basic information data. The initial inference requirements included in the initial inference request data include generating a proposal for the type of non-financial capital to which the IACC should belong as a result of analyzing the IACC basic information data. Note that the initial operator may be the same as or different from the first operator who performs the brush-up process. Furthermore, the initial input / output device 410 may be physically the same as the first input / output device 400. In this specification, the device used in the brush-up process (first input / output device 400) and the device used in the entry process (initial input / output device 410) are described separately merely to facilitate understanding of the disclosed content.

[0161] As described above, in one specific example, the IACC information data includes information indicating the type of non-financial capital (first non-financial capital) to which the IACC belongs. However, it is not easy for members without particular knowledge to determine which type of non-financial capital their own IACC belongs to. Therefore, in the organizational value improvement support device 100 according to this embodiment, the initial generation model 920 analyzes IACC basic information data based on information input by the initial operator and proposes the type of first non-financial capital.

[0162] Initial inference result data, which is the result of analyzing the initial inference request data transmitted from the initial transmission / reception unit 190, is received by the initial transmission / reception unit 190. The capital display generation unit 200 executes processing including identifying the type of non-financial capital proposed and generating capital display data for displaying the identified type of non-financial capital based on the initial inference result data received from the initial transmission / reception unit 190. The capital display data is output to the initial input / output device 410, and the initial operator can use this capital display data to determine the type of non-financial capital (first non-financial capital) to which his or her IACC belongs, taking into consideration the type of non-financial capital proposed by the initial generation model 920, and complete the IACC information.

[0163] Like the comparison display data, the capital display data may be a signal, such as an image display signal or a print signal, that is directly displayed on the output unit of the initial input / output device 410, or data required for display may be input to the initial input / output device 410, and a display signal for the output unit may be generated in the initial input / output device 410. The capital display data may be encrypted data.

[0164] As an example, the organizational value improvement support device 100 according to this embodiment includes an initial IACC information registration unit 210 that performs processing including automatically generating IACC information data from the non-financial capital proposed by the initial generation model 920 and the IACC basic information data, and storing the data in the IACC information database 300.

[0165] The initial IACC information registration unit 210 identifies the type of proposed non-financial capital from the initial inference result data, and generates IACC information data based on the identified type of non-financial capital and the IACC basic information data. The initial inference result data may be input to the initial IACC information registration unit 210 directly from the initial transmission / reception unit 190, or may be input via a storage device (not shown). The IACC basic information data may be stored in a storage device (not shown) when input to the initial inference request generation unit 180. In this case, the initial IACC information registration unit 210 reads out this stored data.

[0166] Figure 4 is a flow diagram (part 3) explaining the information processing performed by the organizational value improvement support device according to one embodiment of the present disclosure, and is a flow diagram of information processing in which the initial IACC information registration unit 210 performs processing to store IACC information in the IACC information database 300 using the proposed types of non-financial capital proposed by the initial generation model 920.

[0167] Specifically, the initial inference request generation unit 180 first collects IACC basic information data from the initial input / output device 410 or the like (step S301), and generates initial inference request data based on the collected data (step S302). Next, the initial transmission / reception unit 190 transmits the initial inference request data to the initial generation model 920 (step S303).

[0168] The initial transmission / reception unit 190 receives the initial inference result data generated by the initial generative model 920 (step S304). Based on the initial proposal included in the received initial inference result data, the capital display generation unit 200 confirms the type of proposed non-financial capital (step S305), generates capital display data including information on the confirmed non-financial capital, and transmits the data to the initial input / output device 410 (step S306).

[0169] The initial IACC information registration unit 210 generates IACC information data from the non-financial capital information confirmed by the capital display generation unit 200 and the IACC basic information data read from a storage device not shown (step S307), and performs a transmission process to store the generated IACC information data in the IACC information database 300 (step S308).

[0170] 3, the information management unit 260 generates information management instruction data for the IACC information database 300 to classify and manage the IACC information data, and transmits the information management instruction data to the IACC information database 300. Below, an example of the information processing that the information management unit 260 can perform will be described.

[0171] The information management instruction data generated by the information management unit 260 may include a first instruction for classifying and managing the IACC information data based on its management attributes. A specific example of the first instruction is an instruction to classify the IACC information based on whether it is a potential object of industrial property rights, a trade secret, or information not requiring confidentiality protection.

[0172] If the IACC is classified as a potential subject of industrial property rights such as patent inventions, it will be treated as strictly confidential information to prevent loss of novelty. Persons who can access the IACC will be limited even within the organization, and those who can have exceptional access by contract will be bound by strong confidentiality obligations.

[0173] If the IACC is classified as a trade secret, it will be kept confidential so as to meet the requirements for trade secrets set forth in the Unfair Competition Prevention Act. Furthermore, a notice will be provided to enable viewers of the IACC to recognize that the information they are viewing is confidential. If the IACC is classified as a trade secret, the level of confidentiality management may be further classified according to factors such as the economic value of the IACC (profit loss if lost). The first instruction may contain information regarding this classification.

[0174] If the IACC is classified as information not requiring confidentiality, it is expected that its value as an intangible asset will increase as described above by being actively shared with other members of the organization. Therefore, it may be preferable to make the IACC accessible to members who will be the first operators. Even if sharing within the organization is preferable, it may be preferable to appropriately set the scope of sharing depending on the importance of the information, etc. The first instruction may include information regarding classification from this perspective.

[0175] The information management instruction data generated by the information management unit 260 may include a second instruction for classifying and managing the IACC included in the IACC information data based on the type of non-financial capital to which it belongs. A specific example of the second instruction is an instruction to classify the IACC based on whether it belongs to human capital, intellectual capital, manufactured capital, social and relationship capital, or natural capital. This type of management makes it easy to identify IACC within an organization that belong to a specific type of non-financial capital, and enables more detailed classification management within that scope. For example, among IACC belonging to social and relationship capital, it is possible to classify and manage IACC related to customers from IACC related to shareholders, creditors, society, and other stakeholders.

[0176] When the IACC information data includes the first instruction and the second instruction, the IACC included in the IACC information data is classified from two perspectives: the level of confidentiality management and the type of non-financial capital, and as a result, the IACC information data is managed in the form of a two-dimensional matrix classified from the above two perspectives in the IACC information database 300. In this case, the elements of the matrix are basically managed according to different rules, and are therefore independent of each other.

[0177] In other words, the IACC information database 300 properly manages potential objects of industrial property rights, such as patent inventions, separately from other information, and also properly manages trade secrets separately from other information. Therefore, since it properly manages this information, the IACC information database 300 is positioned as a database for managing patents, etc. and trade secrets.

[0178] In this regard, if the IACC contained in the IACC information data stored in the IACC information database 300 has appropriate usefulness, it will be stored as a management resource in the intangible resource information database 310. In other words, the IACC information database 300 and the intangible resource information database 310 are related databases. Figure 1 shows that, of the IACC information data stored in the IACC information database 300, data that should become intangible resource information data is output to the intangible resource information database 310 and also stored in that database.

[0179] The intangible resource information database 310 may be part of the IACC information database 300. In this case, the classification based on the level of confidentiality management in the IACC information database 300 can be further subdivided based on the level of usefulness and non-publicity (novelty) that form the basis of the classification, thereby making it possible to extract data that should be stored in the intangible resource information database 310.

[0180] As described above, the organization value improvement support device 100 according to this embodiment realizes brushing up of IACC, and therefore some of the IACC included in the IACC information data stored in the IACC information database 300 are appropriately brushed up and become targets for accumulation as intangible resources in the intangible resource information database 310. From this perspective, the IACC information database 300 is positioned as a cradle of intangible resources.

[0181] In addition, among the IACCs contained in the IACC information data stored in the IACC information database 300, those that are candidates for industrial property rights may be transferred to a database managed, for example, by the intellectual property department within the organization, and may become the subject of specific business processes for obtaining rights, such as patent applications.

[0182] The organizational value improvement support device 100 comprises the following parts in relation to information processing for generating intangible resource information data and storing it in the intangible resource information database 310 (in this specification, this information processing is also referred to as the "intangible resource management process"). An intangible resource inference request generation unit 230 (third inference request generation unit) that generates intangible resource inference request data (third inference request data) An intangible resource transceiver 240 (third transceiver) that transmits and receives data to and from an intangible resource large-scale language model (intangible resource generation model 930, sometimes referred to as a “third large-scale language model” in this specification). An intangible resource information registration unit 250 that generates intangible resource information data based on the inference result of the intangible resource generation model 930 and processes the data to be stored in the intangible resource information database 310. These parts are part of the information processing performed by the intangible resource management process, specifically related to the generation of intangible resource information data.

[0183] As described above, in this embodiment, from the viewpoint of aligning the vector of IACC information refinement with the organizational growth vector, the first inference request included in the first inference request data requests that intangible resources that belong to the same non-financial capital as the first non-financial capital be taken into consideration when generating the first proposal. Therefore, the intangible resource information data stored in the intangible resource information database 310 includes information indicating to which non-financial capital the intangible resources included in the data belong.

[0184] There are various methods for preparing intangible resource information data. One of them is, as described above, converting the IACC information data stored in the IACC information database 300 into intangible resources. In this case, the non-financial capital to which the IACC included in the IACC information data belongs is identified when the IACC information data is generated, so the non-financial capital to which the intangible resource belongs can be easily identified. However, in cases other than this process, it may not be clear to which non-financial capital the intangible resource belongs. In such cases, as will be explained below, a pre-trained generative model such as a large-scale language model can be made to suggest to which non-financial capital the intangible resource included in the intangible resource information data belongs.

[0185] The intangible resource inference request generator 230 receives intangible resource basic information data, which is basic information of intangible resource information data and includes information on intangible resources, and generates intangible resource inference request data. In this embodiment, as an example, the intangible resource information database 310 stores the intangible resource basic information data, but the intangible resource basic information data is not limited thereto and may be stored in another database.

[0186] The creator of the intangible resource basic information data is not limited. The creator of the data may belong to the organization. In such a case, a specific example would be a person with management responsibilities at the manager level or above. The creator of the data may not belong to the organization. In such a case, a specific example would be a business operator such as a management consultant commissioned by the organization and their members. The method for creating the intangible resource basic information data is arbitrary, and the creator may input the intangible resource basic information data into the organization value improvement support device 100 or the intangible resource information database 310 from an input / output device (not shown). Alternatively, as described below, IACC information data stored in the IACC information database 300 that meets certain conditions may be stored in the intangible resource information database 310 as intangible resource basic information data.

[0187] The intangible resource inference requirements included in the intangible resource inference requirement data include generating a proposal for the type of non-financial capital to which the intangible resource should belong as a result of analyzing the intangible resource basic information data.

[0188] The intangible resource transceiver 240 transmits the intangible resource inference request data generated by the intangible resource inference request generator 230 to the intangible resource generation model 930 and receives the intangible resource inference result data from the intangible resource generation model 930 .

[0189] The processing performed by the intangible resource information registration unit 250 includes the following: The intangible resource inference result data and the intangible resource basic information data are input, and the type of proposed non-financial capital is identified from the intangible resource inference result data. Intangible resource information data is generated based on the identified type of non-financial capital and the intangible resource basic information data. The generated intangible resource information data is stored in the intangible resource information database 310.

[0190] The intangible resource information data may include information on SDG targets related to the intangible resource. In this case, the intangible resource inference request generated by the intangible resource inference request generation unit 230 includes generating a proposal for an SDG target related to the intangible resource as a result of analyzing the intangible resource basic information data. Furthermore, the intangible resource information registration unit 250 identifies the proposed SDG target from the intangible resource inference result data and includes information on the identified SDG target in the intangible resource information data.

[0191] To facilitate the setting of information about SDG targets, an SDG database 340 may be prepared, in which information about SDG targets is accumulated. In this case, the intangible resource inference request may include, for example, text representation of the information accumulated in the SDG database 340, or may include an instruction to take the information accumulated in the SDG database 340 into consideration when generating a proposal for an SDG target related to the intangible resource. The information about SDG targets may also be provided by the intangible resource generation model 930. In this case, there is no need to prepare the SDG database 340 specifically; it is sufficient that the intangible resource inference request generation unit 230 generates a proposal for an SDG target related to the intangible resource inference request.

[0192] Figure 5 is a flow diagram (part 4) explaining the information processing (intangible resource management process) performed by the organizational value improvement support device according to one embodiment of the present disclosure, and is a flow diagram of information processing in which the intangible resource information registration unit 250 performs processing to store IACC information in the intangible resource information database 310 using the proposed types of non-financial capital proposed by the intangible resource generation model 930.

[0193] Specifically, first, the intangible resource inference request generation unit 230 collects intangible resource basic information data from the intangible resource information database 310 or the like (step S401), and collects SDGs data related to SDG targets from the SDGs database 340 (step S402). The intangible resource inference request generation unit 230 generates intangible resource inference request data based on the collected data (step S403). Next, the intangible resource transmission / reception unit 240 transmits the intangible resource inference request data to the intangible resource generation model 930 (step S404).

[0194] The intangible resource inference result data generated by the intangible resource generation model 930 is received by the intangible resource transmitting and receiving unit 240 (step S405). Based on the intangible resource proposal included in the received intangible resource inference result data, the intangible resource information registration unit 250 confirms the type of proposed non-financial capital (step S406) and also confirms the proposed SDG targets (step S407). The intangible resource basic information data is added with information on the confirmed non-financial capital and SDG targets to generate intangible resource information data (step S408). The intangible resource information registration unit 250 performs a transmission process to store the generated intangible resource information data in the intangible resource information database 310 (step S409).

[0195] Another embodiment of the present disclosure includes a program executed by the organization value improvement support device 100. The program according to this embodiment is an organization value improvement support program that transmits first inference request data generated based on a first input information data group to a first generative model 910 and receives first inference result data from the first generative model 910. Figures 9 to 15 are explanatory diagrams of an example of an organization value improvement support program according to an embodiment of the present disclosure.

[0196] Specifically, the program according to this embodiment includes the following processes: First, collect data to be included in the first input information data group; Next, generate first inference request data based on the first input information data group; Next, send the first inference request data to the first generative model 910; Furthermore, receive first inference result data from the first generative model 910.

[0197] The first input information data group handled by the program according to this embodiment includes IACC information data including information on the member's IACC (in one example, IACC information in which the IACC is written in a predetermined first format), intangible resource information data including information on intangible resources belonging to the same type of non-financial capital as the non-financial capital to which the IACC belongs among intangible resources possessed by the organization to which the member belongs, and performance-related information data including target values ​​for financial and performance indicators of the organization. The first inference request included in the first inference request data generated by the program according to this embodiment includes generating a first proposal for improving the financial and performance indicators included in the performance-related information data based on the IACC information data and the intangible resource information data.

[0198] Another embodiment of the present disclosure is an organizational value improvement support system including a plurality of devices as components.

[0199] One specific example of such a system is an organizational value improvement support system that includes a first device having input / output functions, an initial device (second device) consisting of at least one device and having data storage functions, and an intangible resource device (third device) having a control unit, and the first device is capable of communicating with the initial device and the intangible resource device.

[0200] The initial device of the system includes an IACC information database 300, an intangible resource information database 310, and a performance-related information database 320. The IACC information database 300 stores IACC information data including information on the IACC of members, for example, IACC information in which the IACC is written in a predetermined first format. The intangible resource information database 310 stores data including information on intangible resources owned by the organization to which the member belongs. The performance-related information database 320 stores performance-related information data including target values ​​for the organization's financial and performance indicators.

[0201] The control unit of the intangible resource device provided in the system has a first inference request generation unit 110 that generates first inference request data to be input to the pre-trained generative model based on data input from the first device.

[0202] The first device of the system includes an input / output unit and a transceiver unit. The input / output unit receives a first input information data group including IACC information data, intangible resource information data, and performance-related information data from the initial device, and outputs the first input information data group to the intangible resource device. Here, the intangible resource information data is part of the data accumulated in the intangible resource information database 310 and includes information on intangible resources that belong to the same type of non-financial capital as the non-financial capital to which the IACC belongs. The transceiver unit transmits first inference request data input from the intangible resource device to the pre-trained generative model and receives first inference result data from the pre-trained generative model.

[0203] The first inference request included in the first inference request data generated by the first inference request generation unit 110 of the intangible resource device includes generating a first proposal to improve financial and performance indicators included in performance-related information data based on IACC information data and intangible resource information data.

[0204] In the above system, at least two of the first device, the initial device, and the intangible resource device may be integrated. The aforementioned organizational value improvement support device 100 is an integrated device of the first device and the intangible resource device. In the above system, each of the first device, the initial device, and the intangible resource device may be composed of multiple devices. For example, the multiple databases provided in the initial device may each be composed of a separate storage device, and even a single database may be composed of multiple storage devices. As a specific example, when the aforementioned IACC information database 300 is used as a patent and trade secret management database, it is easier to appropriately manage each information group by storing the information groups that form each element of the matrix in independent storage devices.

[0205] Another example of such a system is an organization value improvement support system having a plurality of computer devices forming a peer-to-peer network.

[0206] At least one of the multiple computer devices has a first inference request generation unit 110 that generates first inference request data to be input to the pre-trained generative model based on input data. At least one of the multiple computer devices has a transceiver unit that sends the first inference request data to the pre-trained generative model and receives first inference result data from the pre-trained generative model. At least one of the multiple computer devices has an input information generation unit 130 that collects data from multiple databases and generates a first input information data group. The computer device having the first inference request generation unit 110, the computer device having the transceiver unit, and the computer device having the input information generation unit 130 may be different from one another, or any two of them may be the same computer device.

[0207] In this system, the multiple databases include an IACC information database 300 that stores IACC information data including information on the IACC of members, in one example, IACC information in which the IACC is written in a predetermined first format, an intangible resource information database 310 that stores data including information on intangible resources owned by the organization to which the member belongs, and a performance-related information database 320 that stores performance-related information data including target values ​​for the organization's financial and performance indicators.

[0208] The first inference request included in the first inference request data generated by the computer device having the first inference request generation unit 110 includes generating a first proposal to improve financial and performance indicators included in the performance-related information data based on the IACC information data and the intangible resource information data. As described above, the intangible resource information data is part of the data accumulated in the intangible resource information database 310, and includes information on intangible resources that belong to the same type of non-financial capital as the non-financial capital to which the IACC belongs.

[0209] In the brush-up process, the IACC information data is the essential data to be input to the input information generation unit 130, and the input of other information data is optional. Therefore, the first inference request included in the first inference request data can be expressed as generating a first proposal to improve the financial and performance indicators of the organization to which the member belongs, based on a first input information data group that includes at least the IACC information data.

[0210] Another embodiment of the present disclosure will be described below. Fig. 16 is a block diagram illustrating an organization value improvement support system including an organization value improvement support device according to another embodiment of the present disclosure.

[0211] 16 , an organization value improvement support system 1000A according to another embodiment of the present disclosure has a configuration common to the organization value improvement support system 1000 according to one embodiment of the present disclosure. Specifically, the organization value improvement support system 1000A can execute a brush-up process using a first generative model 910, can execute an entry process using an initial generative model 920, and can execute an intangible resource management process using an intangible resource generation model 930.

[0212] The differences in the configuration of the organization value improvement support system 1000A shown in FIG. 16 from the organization value improvement support system 1000 shown in FIG. 1 are as follows. Configuration 1: An organization value improvement support device 100A included in an organization value improvement support system 1000A shown in FIG. 16 includes a pre-trained generative model including a large-scale language model as a component. Configuration 2: The organization value improvement support device 100A includes an intangible resource information management unit 251 that manages the intangible resource information data stored in the intangible resource information database 310. Configuration 3: The organization value improvement support system 1000A has an auxiliary information management unit 330A that manages the auxiliary information database 330.

[0213] The configurations relating to the differences in the organization value improvement support system 1000A will be described in detail below.

[0214] Regarding configuration 1, in the organizational value improvement support device 100, the first generative model 910, the initial generative model 920, and the intangible resource generative model 930 were not components of the organizational value improvement support device 100, but the organizational value improvement support device 100A, as a non-limiting example, includes at least one of these pre-trained generative models as a component.

[0215] With advances in hardware technology, such as increased functionality in semiconductor devices (smaller size, lower power consumption, and faster processing speeds), and advances in software technology, such as federated learning, it is now possible to implement pre-trained generative models at a practical level in organizational value improvement support devices, such as personal computers and smartphones. The embodiments of the present disclosure include such cases. The organizational value improvement support device 100A includes all of the first generative model 910, the initial generative model 920, and the intangible resource generative model 930 as components, but is not limited to this, and may also include at least one pre-trained generative model as a component.

[0216] Configuration 2 will be described with reference to Figure 17. Figure 17 is a diagram illustrating the function of the intangible resource information management unit of an organization value improvement support system having an organization value improvement support device according to another embodiment of the present disclosure, and is composed of a block diagram illustrating a partial configuration of the organization value improvement support system 1000A shown in Figure 16, and above it a diagram (data management relationship diagram) illustrating the relationship between the IACC information data stored in the IACC information database 300 and the intangible resource information data stored in the intangible resource information database 310, and further the relationship between the intangible resource information data and guidelines for disclosing information outside the organization. In the explanation using Figure 17, a company that is a for-profit organization may be used as a specific example.

[0217] In the organizational value improvement support system 1000A, at least a portion of the data (such as IACC information data) stored in the IACC information database 300 is positioned as data (intangible resource information data) stored in the intangible resource information database 310. Data positioned as intangible resource information data includes all of the information contained in the corresponding IACC information data, and therefore includes information on the type of non-financial capital to which the corresponding IACC information data belongs. Therefore, the intangible resource information data also includes information on the type of non-financial capital to which the intangible resource information contained in the data belongs.

[0218] The specific method for changing the position of data is not limited. For example, a copy of the data stored in the IACC information database 300 may be stored in the intangible resource information database 310, so that the IACC information database 300 and the intangible resource information database 310 store data that is equivalent in content, or, on the condition that data has been stored in the intangible resource information database 310, the data stored in the IACC information database 300 may be deleted, thereby essentially transferring data between the databases (IACC information database 300 → intangible resource information database 310).

[0219] In addition, if the IACC information database 300 and the intangible resource information database 310 are essentially composed of a single database, and the database to which the data belongs is determined by setting the data management classification, it is possible to position the IACC information data as intangible resource information data by changing the management classification from the IACC information database 300 to the intangible resource information database 310.

[0220] At this time, the IACC information database 300 may be retained as a management category for the IACC information data that has become intangible resource information data. In this case, information indicating that the data has been stored in both the IACC information database 300 and the intangible resource information database 310 may be added to the data.

[0221] In this way, IACC information database 300 and intangible resource information database 310 only need to be functionally separated, and the two databases may or may not be physically separated. In other words, IACC information database 300 is established primarily for the purpose of managing the creation of IACC by its members, while intangible resource information database 310 is established primarily for the purpose of safely utilizing (including disclosing) intangible resources (with appropriate confidentiality management). Therefore, as long as each database can achieve its respective purpose, there are no limitations on the physical configuration of the two databases.

[0222] The information management instruction data generated by the information management unit 260 may include an applicability instruction indicating whether or not the IACC information data may be stored as intangible resource information data in the intangible resource information database 310. The data indicating the applicability instruction may be attached to each individual IACC information data, or may exist as data that manages the applicability instructions for all IACC information data.

[0223] As shown in the data management relation diagram of Fig. 17, the first instructions (classification instructions in terms of secret management levels) of the information management instruction data generated by the information management unit 260 included in the organization value improvement support device 100A have more types than those of the organization value improvement support device 100. Specifically, in the organization value improvement support device 100, the IACC secret management levels in the first instructions were three types: industrial property right object candidate, trade secret, and information not requiring secret management, but the organization value improvement support device 100A has the following three types in addition to these three types.

[0224] · Undisclosed filed information: Information about a potential object of industrial property rights that has been filed or has undergone application procedures with an institution, such as the Patent Office, to which the application for the right is to be issued, after the necessary requirements for the procedure have been met. The level of confidentiality management does not require as high a level as that required for potential objects of industrial property rights before the application procedure or trade secrets. However, unless the information is actively disclosed, the confidentiality will remain, and each time it is disclosed, the information will leak outside the organization.

[0225] Published but unpatented information: This is information that has been filed with the Patent Office, etc., and subsequently made public by the Patent Office, etc. Since it is publicly known, the level of confidentiality management is low and it is possible to disclose much of the information. However, since the corresponding industrial property right has not yet been created, i.e., it is in the process of being patented, it is important to note that if information that falls under the category of published but unpatented information is actively disclosed, unpublished information (such as technical know-how) associated with that information may also be disclosed.

[0226] ·Private information: Information that has been registered as an object of industrial property rights by the Patent Office, etc., and does not actually need to be kept secret.

[0227] If the IACC information data includes information classified as a candidate for an industrial property right object, the applicability instruction corresponding to the IACC information data is an instruction not to allow the IACC information data to be stored in the intangible resource information database 310. This reliably prevents the candidate for an industrial property right object from leaking out of the intangible resource information database 310. Since the intangible resource information data stored in the intangible resource information database 310 should be actively utilized within the organization as an intangible asset within the organization, the intangible resource information database 310 is accessed more frequently by members of the organization than the IACC information database 300. Therefore, from the perspective of information confidentiality management, information stored in the intangible resource information database 310 is more likely to be leaked to the outside than information stored in the IACC information database 300.

[0228] If the IACC information data does not include information classified as a candidate for the object of industrial property rights, but includes information classified as trade secrets, and information classified as filed but unpublished information, published but unpatented information, or patented information, the applicability instruction corresponding to the IACC information data will be an instruction to allow the IACC information data to be accumulated in the intangible resource information database 310, provided that the IACC information data does not include information highly related to information on a candidate for the object of industrial property rights. This makes it possible to utilize useful information within an organization as an intangible asset.

[0229] If the IACC information data contains only information that does not require confidentiality management out of the six types classified by the first instruction, that information does not reach the level of an organizational intangible asset and is therefore outside the scope of management in the intangible resource information database 310. Therefore, the applicability instruction corresponding to that IACC information data is an instruction not to allow storage in the intangible resource information database 310. This prevents data containing only information that is worthless or of low value from being stored in the intangible resource information database 310. Furthermore, while information contained in data stored in the intangible resource information database 310 is an organizational asset (intangible asset), preventing worthless or low-value information from being classified as an intangible asset makes it easier to evaluate intangible assets within an organization and to increase the validity of the evaluation results.

[0230] Furthermore, the information management instruction data generated by the information management unit 260 may include a confidentiality management instruction indicating the level of confidentiality management of the information included in the intangible resource information data in the intangible resource information database 310. The confidentiality management instruction may be set in accordance with the first instruction regarding the IACC confidentiality management level included in the IACC information data positioned as intangible resource information data. Specifically, the intangible resource information data may be classified into four types: trade secrets, filed but unpublished information, published but unpatented information, and patented information.

[0231] The organization value improvement support device 100A includes an intangible resource information management unit 251 that generates disclosure management instruction data indicating whether or not intangible resource information data stored in the intangible resource information database 310 can be disclosed outside the organization and the extent of disclosure, and transmits the disclosure management instruction data to the intangible resource information database 310. Since the information contained in the intangible resource information data stored in the intangible resource information database 310 is an intangible asset of the organization, members belonging to the organization can access such information relatively freely.

[0232] However, because this information includes confidential information, it is necessary to manage whether or not it can be disclosed outside the organization and the extent of disclosure. The intangible resource information management unit 251 is involved in this management. The intangible resource information database 310 receives the disclosure management instruction data generated by the intangible resource information management unit 251 and manages the information stored in the intangible resource information database 310 based on the instructions contained in the disclosure management instruction data.

[0233] Under the management of the intangible resource information management unit 251, the intangible resource information database 310 outputs external disclosure information. If the organization is a corporation, external disclosure information is disclosed externally, for example, as information included in an integrated report. An integrated report discloses financial statements such as balance sheets and income statements, and it is being considered to add intangible resources to these financial statements, i.e., to include them on the books. For example, a corporate value balance sheet has been proposed, which would include a column for intangible assets following the asset column on the balance sheet, and a column for non-financial capital below the capital column to the right of the intangible asset column, thereby showing intangible assets and capital that cannot be displayed in a conventional balance sheet. If the organization is a corporation, the total internal valuation of intangible resources (a monetary indicator) is calculated by subtracting net assets from the company's market capitalization. Here, rather than managing (market capitalization) - (net assets) as a single unit under "goodwill," clarifying the objects that make it up makes it possible to individually evaluate the value of intangible resources that were previously impossible to evaluate, which is expected to lead to a fair evaluation of a company's true value.

[0234] In this way, the existence of intangible resources needs to be disclosed to stakeholders, but some, such as trade secrets, lose value if they are disclosed to the extent that they can no longer be kept secret. Therefore, it is extremely important to properly classify and manage intangible resources according to the level of secrecy.

[0235] In addition, by indicating which of the five non-financial capitals included in non-financial information intangible resources corresponds, it becomes possible to specifically evaluate the operational (or management, if it is a company) policies and operational (or management, if it is a company) status of an organization such as a company.

[0236] An example of information management by the intangible resource information management unit 251 is shown at the left end of the data management relationship diagram in Figure 17. If the intangible resource information data includes trade secrets, disclosing the contents outside the organization would eliminate the value of the intangible resources, so the contents are not disclosed. When disclosing trade secrets, one specific example would be to disclose only the type of capital to which the trade secrets belong, specifically, to the extent of disclosing the number of trade secrets belonging to a specific capital (such as manufacturing capital).

[0237] If intangible resource information data only includes patent-filed, unpublished information, industrial property rights can still be established based on the patent-filed, unpublished information even if the confidentiality is lifted by disclosing the contents. However, if the patent-filed, unpublished information becomes public information, the legal stability of industrial property rights based on priority applications related to the patent-filed, unpublished information may be reduced. Therefore, when disclosing patent-filed, unpublished information, if the potential subject matter is an invention, one example is to disclose only the name of the invention.

[0238] If intangible resource information data includes only publicly available, unlicensed information and / or licensed information, it is publicly known information, and there is no need to restrict disclosure. Rather, it is preferable to actively disseminate this information and widely inform stakeholders of the existence of valuable intangible resources based on this information. Therefore, in this case, one specific example is to disclose the content as well.

[0239] In addition, information including potential trade secret objects before an application is filed is stored only in the IACC information database 300 and is not stored in the intangible resource information database 310, thereby reliably preventing the problem of such information being accidentally disclosed from the intangible resource information database 310.

[0240] The process executed by the intangible resource information management unit 251 described above may be positioned as part of the intangible resource management process.

[0241] Here, we will explain the relationship between the brush-up process and IACC information data shown on the right side of the data management relationship diagram in Figure 17. If an IACC included in the IACC information data includes a candidate for industrial property rights, access to that IACC information data is strictly restricted. Specifically, for example, access is restricted only to the creator of that IACC and the intellectual property manager. IACC information data that includes an IACC classified as a candidate for industrial property rights may basically be exempt from the brush-up process; in that case, the creator of that IACC can simply brush up the data under appropriate confidentiality management during the process leading up to the filing procedure, etc.

[0242] IACC information data including IACC classified as other IACC, i.e., IACC classified as one or more types selected from the group consisting of trade secrets, information not requiring confidentiality management, filed but unpublished information, published but unpatented information, and patented information, may also be subject to the brush-up process. When IACC information data including IACC classified as trade secrets is subject to the brush-up process, it is preferable to display on the first input / output device 400, such as a member's PC, which serves as an interface for the brush-up process, that the data is subject to advanced confidentiality management. When IACC information data including IACC classified as information not requiring confidentiality management is subject to the brush-up process, it is preferable to actively subject the data to the brush-up process and increase its value by, for example, making it non-public. When IACC information data including IACC classified as filed but unpatented information is subject to the brush-up process, it may be preferable to display on the first input / output device 400 that the data is confidential to the organization to prevent information leaks. When IACC information data including IACC classified as published but unpatented information or patented information is subject to the brush-up process, it may be displayed on the first input / output device 400 that the data is in the process of being patented or that the data has been patented, respectively.

[0243] Configuration 3 will be described with reference to Fig. 18. Fig. 18 is a diagram illustrating the function of an auxiliary information management unit of an organization value improvement support system having an organization value improvement support device according to another embodiment of the present disclosure. The organization value improvement support system 1000A has an auxiliary information management unit 330A that manages the auxiliary information database 330.

[0244] The auxiliary information management unit 330A has an IACC information analysis unit 331 that inputs the IACC information data that is the target of the brush-up process from the IACC information database 300 and analyzes the IACC information included in the IACC information data. Since the IACC information is information written in the first format in a specific example, the IACC information analysis unit 331 can automatically extract an overview of the IACC, the type of capital, and the like from the IACC information data by extracting predetermined target fields in the first format from the IACC information data.

[0245] The auxiliary information management unit 330A has a search condition generation unit 332 that automatically generates search conditions based on information obtained from the IACC information analysis unit 331. When the IACC information is information written in a first format, the search conditions can be automatically generated by configuring the search conditions in a format consisting of multiple target fields and associating the target fields with the target fields in the first format. In this case, as surrounded by a dashed line in Figure 18, the IACC information analysis unit 331 and the search condition generation unit 332 function essentially as a single unit.

[0246] Specific examples of search conditions generated by the search condition generation unit 332 include an overview of the IACC, information about the same type of non-financial capital as the non-financial capital to which the IACC information belongs, information about the organization or department to which the member who created the IACC information belongs, information about trends in the field (technical field, industry, etc.) to which the IACC belongs, current information from the time the IACC information was created to the time the brush-up process is carried out, internal organizational information that is different from financial and performance indicators such as the management and operation philosophy, management and operation policy, and KGI of an organization such as a company, and combinations of these.

[0247] The auxiliary information management unit 330A has an external information search unit 333 that executes a search on an external information network ExNW such as the Internet using the search criteria generated by the search criteria generation unit 332, and an internal information search unit 334 that executes a search on the auxiliary information database 330. In one example, the search results of the external information search unit 333 are stored in the auxiliary information database 330. Both the external information search unit 333 and the internal information search unit 334 may be executed, or only one of them may be executed. Depending on the search criteria, only one of them may be executed. For example, if the search criteria consist only of internal organization information, only the internal information search unit 334 is executed.

[0248] The auxiliary information management unit 330A has a search result generation and transmission unit 335 that integrates the search results of the external information search unit 333 and / or the internal information search unit 334 and automatically generates data to be transmitted to the input information generation unit 130. When integration of the search results is not required, the search result generation and transmission unit 335 only transmits the search results to the input information generation unit 130.

[0249] The entry process including the execution of the initial IACC information registration unit 210 and the intangible resource management process including the execution of the intangible resource information registration unit 250 may be configured independently of the brush-up process.

[0250] 19 is a diagram illustrating an organization value improvement support device that executes an entry process according to another embodiment of the present disclosure. As shown in FIG. 19, the organization value improvement support device 100B according to this embodiment includes, as minimum components, an initial IACC information registration unit 210, an initial inference request generation unit 180, an initial transmission / reception unit 190, and a capital display generation unit 200.

[0251] The initial IACC information registration unit 210 performs processing including storing, in the IACC information database 300, IACC information data including information on the IACC, in one example, IACC information in which the IACC is written in a predetermined first format, and information indicating which of the five non-financial capitals the IACC belongs to.

[0252] The initial inference request generating unit 180 receives as input IACC basic information data, which is basic information of IACC information data and includes information on the IACC, and generates initial inference request data.

[0253] The initial transceiver 190 transmits initial inference request data to the initial generative model 920 and receives initial inference result data from the initial generative model 920. The initial inference request included in the initial inference request data includes generating a proposal as to which of the five non-financial capitals the IACC should belong to as a result of analyzing the IACC basic information data.

[0254] The capital display generation unit 200 receives initial inference result data as input, and performs processing including identifying the type of proposed non-financial capital from the initial inference result data and generating capital display data for displaying the identified type of non-financial capital.

[0255] The initial IACC information registration unit 210 also performs processing including identifying the type of proposed non-financial capital from the initial inference result data and generating IACC information data based on the identified type of non-financial capital and the IACC basic information data.

[0256] The initial inference request generated by the initial inference request generation unit 180 of the organization value improvement support device 100B generates initial public knowledge proposal information including the examination result of whether the IACC is publicly known. In addition, the initial inference request indicates publicly known information determined to include the IACC when the initial public knowledge proposal information has a proposal that it is publicly known, and indicates publicly known information determined to be closest to the IACC when the initial public knowledge proposal information has a proposal that it is not publicly known.

[0257] In this way, the organization value improvement support device 100B judges whether information is publicly known at the entry process stage, facilitating the secret management of non-public information. Specifically, the organization value improvement support device 100B includes an initial publicly known display generation unit 280 and an initial secret management setting unit 290.

[0258] The initial public knowledge display generation unit 280 receives the initial inference result data and determines whether the IACC is publicly known or not from the initial public knowledge proposal information. If it determines that the IACC is not publicly known, it executes processing including generating initial non-public information display data including a notification indicating that the IACC may be confidential information.

[0259] The initial non-public display data generated by the initial public display generation unit 280 makes it possible for the initial operator performing the entry process to recognize that the IACC in question may be non-public information.

[0260] The initial secret management setting unit 290 receives the initial inference result data and determines whether the IACC is publicly known or not from the initial public knowledge proposal information. If it determines that the IACC is not publicly known, it executes a process including setting the management attribute of the IACC information data to data including a candidate for an industrial property right object.

[0261] The initial secret management setting unit 290 and the information management unit 260 realize appropriate secret management of the IACC that is the subject of the entry process.

[0262] The initial public knowledge display generating unit 280 and the initial secret management setting unit 290 may be included in the organization value improvement support device 100, 100A according to an embodiment other than this embodiment.

[0263] 20 is a diagram illustrating an organization value improvement support device that executes an intangible resource management process according to another embodiment of the present disclosure. The organization value improvement support device 100C according to this embodiment includes, as minimum components, an intangible resource information registration unit 250, an intangible resource inference request generation unit 230, and an intangible resource transmission / reception unit 240.

[0264] The intangible resource information registration unit 250 performs processing including storing intangible resource information data in the intangible resource information database 310, which includes information on intangible resources possessed by the organization and has data indicating the type of non-financial capital to which these intangible resources should belong.

[0265] The intangible resource inference request generating unit 230 receives as input intangible resource basic information data, which is basic information of intangible resource information data and includes information on intangible resources, and generates intangible resource inference request data.

[0266] The intangible resource transceiver 240 transmits intangible resource inference request data to the intangible resource generation model 930 and receives intangible resource inference result data from the intangible resource generation model 930 .

[0267] The intangible resource inference request included in the intangible resource inference request data has the effect of generating a proposal for the type of non-financial capital to which the intangible resource should belong as a result of analyzing the intangible resource basic information data.

[0268] The intangible resource information registration unit 250 receives as input the intangible resource inference result data and the intangible resource basic information data, identifies the type of proposed non-financial capital from the intangible resource inference result data, and performs processing that includes generating intangible resource information data based on the identified type of non-financial capital and the intangible resource basic information data.

[0269] The intangible resource basic information data may also include types of non-financial capital. If the intangible resource basic information data includes IACC information data stored in the IACC information database 300, the IACC capital types are included, and therefore the non-financial capital types of intangible resources are included. Even in this case, the IACC capital types are based on the proposal results of the first generative model 910 and the initial generative model 920. Therefore, if the intangible resource generative model 930 differs from these pre-trained generative models, different proposals may be made. Pre-trained generative models typically undergo continuous retraining, and their learning levels are constantly changing. Therefore, even if the pre-trained generative models appear identical, if the access timing differs, strictly speaking, an inference request is made to a pre-trained generative model with a different level of learning. Therefore, it is meaningful to check the non-financial capital types multiple times in this way.

[0270] In the organization value improvement support device 100C, similarly to the organization value improvement support device 100, the intangible resource information data may include information on SDG targets related to the intangible resources. Then, the intangible resource inference request may include generating a proposal of an SDG target related to the intangible resources as a result of analyzing the intangible resource basic information data.

[0271] When the intangible resource generation model 930 includes a proposal of an SDG target related to an intangible resource in the intangible resource inference result data, the information source of the SDG target is not limited. As shown in FIG. 20 , SDG data, which is data related to SDG targets, is stored in the SDG database 340. When the intangible resource inference request generator 230 generates an intangible resource inference request, the intangible resource inference request may use the SDG data stored in the SDG database 340 as the information source of the SDG target. Alternatively, the intangible resource inference request may not specify the information source of the SDG target. In this case, the intangible resource generation model 930 searches for information on the SDG target based on the inference request and generates a proposal of an appropriate SDG target.

[0272] As in the case of the organizational value improvement support device 100, the intangible resource information registration unit 250 identifies proposed SDG targets from the intangible resource inference result data, includes information on the identified SDG targets in the intangible resource information data, and accumulates the intangible resource information data with the added information in the intangible resource information database 310.

[0273] Fig. 21 is a block diagram explaining the function of the input information generation unit. As shown in Fig. 1, the input information generation unit 130 reads data from the IACC information database 300 or the like to generate a first input information data group. Fig. 20 is a block diagram explaining the process of generating this first input information data group and related processes.

[0274] The input information generation unit 130 includes an IACC information reading unit 131, an intangible resource information reading unit 132, a performance-related information reading unit 133, an emotional information identification unit 136, and an auxiliary information reading unit 137, as well as a data group generation unit 134, and further includes a quotation notification generation unit 135, and the organizational value improvement support device 100 includes a quotation notification counting unit 270 in association with the quotation notification generation unit 135.

[0275] The IACC information reading unit 131 reads data from the IACC information database 300, which stores data that is subject to advanced confidentiality management, and therefore has an authority confirmation unit 131A, an access control unit 131B, and a reading unit 131C in relation to access control.

[0276] The authority confirmation unit 131A identifies a first operator who is attempting to access the IACC information data stored in the IACC information database 300 for the purpose of brushing up, and confirms the access authority of the identified first operator. There are no limitations on the method for identifying the first operator, and in Fig. 21, operator identification data is received from the first input / output device 400, and the access authority is confirmed based on information contained in this data. A specific example of the operator identification data is a set of a user ID and a passcode.

[0277] For example, if the IACC information data to be accessed includes an IACC that is a candidate for an industrial property right object, the authority to access the data is granted only to the creator of the IACC and the manager of the candidate for the industrial property right object, such as the intellectual property department. Therefore, if the first operator is neither the creator of the IACC nor the manager of the candidate for the industrial property right object, the authority confirmation unit 131A confirms that the first operator does not have the authority to access the IACC information data to be accessed.

[0278] The access control unit 131B allows the first operator to access the IACC information data stored in the IACC information database 300 within the scope of the access authority confirmed by the authority confirmation unit 131A. Further explaining the previous example, if the first operator does not have access authority to the IACC information data to be accessed, the access control unit 131B does not permit access to that data. If the first operator has access authority to the IACC information data to be accessed, for example, because the first operator is the creator (initial operator) of the IACC, the access control unit 131B permits the first operator to access the data.

[0279] When the first operator desires to access multiple pieces of data in a single operation, the access control unit 131B sets the accessible data based on the confirmation result of the authority confirmation unit 131A. The multiple pieces of data permitted by the access control unit 131B become displayable on the first input / output device 400, and the first operator accesses the desired IACC information data by recognizing this display. In FIG. 21 , the first input / output device 400 receives IACC information display data from the IACC information database 300 and displays multiple pieces of data on the first input / output device 400 based on the IACC information display data. Here, the IACC information display data is instruction data for causing the first input / output device 400 to display information included in the IACC information data that has become displayable on the first input / output device 400. In this case, the predetermined information is displayed on the first input / output device 400 without directly leaking the IACC information data from the IACC information database 300, thereby reducing the possibility of information leakage.

[0280] The access control unit 131B may permit the first operator identified by the authority confirmation unit 131A to access multiple data. A specific example of such a case is when the access control unit 131B permits the first operator to display the own IACC, which is an IACC created by the first operator (in this case, the first operator and the initial operator are the same), and the other operator's IACC. Here, the other operator's IACC refers to an IACC that is not created by the first operator who is accessing, but is permitted to be viewed.

[0281] In this case, the access control unit 131B may generate display filter instruction data that is permitted in the IACC information database 300, and transmit the display filter instruction data to the IACC information database 300. The IACC information database 300 that receives this display filter instruction data generates IACC information display data including information on the own IACC and other IACCs based on the instructions of the display filter instruction data, and makes it possible to display predetermined information on the first input / output device 400.

[0282] The reading unit 131C identifies the IACC information data that the first operator has decided to include in the first input information data group from among the IACC information data that the first operator can access, and reads the identified IACC information data from the IACC information database 300.

[0283] As a specific example, as described above, a case where a predetermined plurality of pieces of information are displayed on the first input / output device 400 without directly outputting IACC information data from the IACC information database 300 is taken as an example. The first operator selects, for example, one piece of desired information from the plurality of pieces of information displayed on the first input / output device 400. Since the IACC information data corresponding to this selected information is the IACC information data determined to be included in the first input information data group, the reading unit 131C identifies this data and reads the identified IACC information data from the IACC information database 300. FIG. 21 shows, as an example, that a read request is output from the IACC information reading unit 131 including the reading unit 131C, and that the IACC information database 300, which has received this, outputs the predetermined IACC information data to the IACC information reading unit 131.

[0284] The intangible resource information reading unit 132 receives as input the IACC information data identified by the IACC information reading unit 131 and read from the IACC information database 300. Based on this IACC information data, the intangible resource information reading unit 132 identifies which of the intangible resource information data stored in the intangible resource information database 310 is to be read. The intangible resource information reading unit 132 then reads this identified intangible resource information data from the intangible resource information database 310. In this way, the IACC information reading unit 131 and the intangible resource information reading unit 132 perform information processing including data transmission and reception, thereby automatically generating a first inference request that includes information on performance-related information data that belongs to the same type of non-financial capital as the IACC information data that is the target of the brush-up process.

[0285] The performance-related information reading unit 133 identifies the financial and performance indicators that the first operator has decided to target for improvement, and reads performance-related information data including target values ​​for these identified financial and performance indicators from the performance-related information database 320. Specifically, as shown in FIG. 21 , the performance-related information reading unit 133 receives data indicating the financial and performance indicators to be targeted through input from the first input / output device 400 operated by the first operator, and creates read request data from the received data indicating the financial and performance indicators. The performance-related information reading unit 133 sends the read request data to the performance-related information database 320, generates specified performance-related information data based on the data received from the performance-related information database 320, and outputs the performance-related information data to the data group generation unit 134.

[0286] 13, data including all target values ​​of financial and performance indicators is pre-loaded and displayed in the organization value improvement support device 100. In this case, the performance-related information reading unit 133 does not need to create read request data, and can generate performance-related information data including target values ​​of desired financial and performance indicators from the pre-loaded data.

[0287] The input information generation unit 130 included in the organization value improvement support device 100 may have an emotion information identification unit 136 related to the first emotion. The emotion information identification unit 136 identifies the first emotion included in the personal emotion information data. This allows the first inference request generation unit 110 to receive as input data including the first emotion identified by the emotion information identification unit 136.

[0288] Various specific configurations are possible for the emotion information identification unit 136. For example, the emotion information identification unit 136 may be configured to directly receive the first emotion input by the first operator performing the brush-up process using the first input / output device 400. In this case, the first emotion may be expressed in a wide variety of ways, and therefore the influence of the first emotion on the first proposal may depend on the expression format of the first emotion, which may become a factor in instability of the first proposal.

[0289] Therefore, as a specific example, emotion information identification unit 136 may have emotion display unit 136A that generates emotion display data that selectably displays a predetermined number of emotions. For example, emotion display unit 136A prepares several expressions that express emotions such as "excited" and "sense of accomplishment," and displays these expressions selectably on first input / output device 400. The first operator selects one of these expressions as the first emotion, and data including the selection result is output from first input / output device 400 to emotion information identification unit 136.

[0290] Emotion display section 136A has emotion determination section 136B that determines which of the multiple emotions displayed in emotion display section 136A has been selected as the first emotion, thereby identifying the first emotion.

[0291] The input information generation unit 130 included in the organization value improvement support device 100 may have an auxiliary information reading unit 137 in relation to auxiliary information such as current affairs information. The auxiliary information reading unit 137 identifies which auxiliary information data stored in the auxiliary information database 330 is to be read, and reads the identified auxiliary information data from the auxiliary information database 330.

[0292] The auxiliary information reading unit 137 has a read request generating unit 137A. The read request generating unit 137A determines which auxiliary information data stored in the auxiliary information database 330 is to be read, generates a read request to the auxiliary information database 330, and transmits the read request to the auxiliary information database 330. As described above, there are various types of auxiliary information (current affairs information, industry trends, basic information on management, etc.), so the auxiliary information reading unit 137 may first identify what type of information to read, and then determine subsequent processing depending on the type of information to be read.

[0293] More specifically, the auxiliary information database 330 may be composed of multiple databases corresponding to different types of information. In this case, the read request determines which of the multiple databases to access depending on the type of information to be read. At this time, due to access restrictions, the access request of the read request may not be accepted. The access restriction processing may be handled by the access control unit 131B.

[0294] The auxiliary information reading unit 137 includes a data reading unit 137B that receives auxiliary information data prepared in response to a read request from the auxiliary information database 330.

[0295] When reading auxiliary information such as current affairs information from the auxiliary information database 330, it may be preferable to set a filter to some extent in order to reduce the load of subsequent processing (generation of the first input information data group and generation of the first inference request). From this perspective, the read request generation unit 137A may determine, as the target of the read request, auxiliary information data that is included in the IACC information data and includes auxiliary information that belongs to non-financial capital that is equal to the non-financial capital to which the IACC that is the target of the brush-up process belongs.

[0296] The input information generation unit 130 includes the loaded IACC information data, the loaded intangible resource information data, and the loaded performance-related information data, and in a preferred example, further includes a data group generation unit 134 that generates a first input information data group from data including a first emotion and the loaded auxiliary information data. The specific format of the first input information data group generated by the data group generation unit 134 is not limited. Any format may be used as long as the first inference request generation unit 110 can perform appropriate processing using the first input information data group as input data.

[0297] From the perspective of growing an IACC, it may be preferable to target the brush-up process at another person's IACC rather than one's own. When brushing up an IACC that one has created, it can be difficult to incorporate free ideas that go beyond the time of creation, due to the influence of the context at the time of creation as a limiting factor. In contrast, when targeting another person's IACC for the brush-up process, it is easier to incorporate free ideas, as one is less likely to be bound by the context at the time of creation.

[0298] From the viewpoint of promoting the combination of such ideas and increasing the incentive to create IACC, the input information generation unit 130 has a citation notification generation unit 135 that generates citation notification data to notify the creator of the other IACC of the decision when the first operator performing the brush-up process decides to include the other IACC in the first input information data group.

[0299] Specifically, for example, when a member who created an IACC accesses the organizational value improvement support device 100 as an initial operator via the initial input / output device 410 to create initial IACC information data, the citation notification generation unit 135 displays a message indicating that the IACC he or she previously created has been cited by other members as a target for a brush-up process. This allows the initial operator to know that the IACC he or she created was of a level that was also appreciated by other members. This gives the initial operator a sense of accomplishment by knowing that he or she has effectively contributed to increasing the organization's intangible assets. This feeling contributes to increasing the initial operator's level of engagement with the organization.

[0300] The citation notification generation unit 135 may generate data to display to the creator of the IACC not only the fact that other members have cited the IACC, but also information about the affiliation of the citing members, such as their departments. For example, if the creator of an IACC learns that the IACC he or she created has been cited as a subject of a brush-up process by members of a department unrelated to the creator's work, the creator may reevaluate the value of the IACC he or she created. As a result, the creator may realize that the limitations he or she felt when forming the IACC were not valid, and this realization may strongly contribute to the further growth of the IACC.

[0301] To particularly increase the incentive to create an IACC, the organizational value improvement support device 100 may include a citation notification counting unit 270 that records how many citation notification data have been generated for each IACC information data. By recording the number of citations by other members, it becomes possible to provide the creator with a quantitative incentive for citations. Furthermore, because the degree of citation can be compared with other IACCs (which may be other IACCs created by the creator or by others), a game element is incorporated into the brush-up process. This is expected to contribute to revitalizing the brush-up process.

[0302] A concrete example of the citation notification to the creator of the IACC by the citation notification generation unit 135 and the citation notification count unit 270 is the information (employee number, name, team) displayed in the lower left area of ​​Figure 6 and the number indicated by the ``selection point'' above it.

[0303] The processing performed by the first inference request generation unit 110 will be described in detail using Figure 21. The first inference request generation unit 110 has a first information identification unit 111 and a first prompt generation unit 112. The first information identification unit 111 receives as input the first input information data group generated by the data group generation unit 134 of the input information generation unit 130, and identifies multiple pieces of information included in the first input information data group. Note that even if the first input information data group is made up of IACC information data, the IACC information data includes multiple pieces of information, so the first input information data group includes multiple pieces of information.

[0304] The first prompt generation unit 112 automatically generates first inference request data, including automatically placing each of the multiple pieces of information included in the first input information data group in a predetermined target field and executing a process to generate management data.

[0305] 1, 21, and the like, the input information generation unit 130 and the first inference request generation unit 110 are independent functional blocks, the input information generation unit 130 forms a first input information data group, and the first inference request generation unit 110 receives the first input information data group output from the input information generation unit 130. However, this information processing is merely an example, and the present embodiment is not limited to this example. For example, the data constituting the first input information data group may not be bundled as the first input information data group in the input information generation unit 130, and the first information identification unit 111 or the first prompt generation unit 112 may individually collect desired data from each database. In this case, the first inference request generation unit 110 has the functionality of the data group generation unit 134 and is included in one specific example of the present embodiment. Therefore, the first inference request generation unit 110 may also include the functionality of the input information generation unit 130.

[0306] The process by which the first prompt generating unit 112 automatically generates first inference request data will be described below using a specific example.

[0307] In one example, first prompt generator 112 has the following automatically generated resources.

[0308] "In order to achieve (and improve) our " &〔RO〕Global-KPI Rate-Type & " goals, " && "Our [corporate selling points] (each point is separated by a line break)" && "Compare the team members' [specific behavioral indicators] with the following "current events"" && [RO] Reflection - Name & " specific action outline proposed by " && " " &〔RO〕Global-Action Items & "" are the following three capital types: " && Please write your answer as advice for brushing up on "manufactured capital," "social and relationship capital," and "natural capital." "If the specific actions proposed by the " & (RO) Reflection - Name & " to be analyzed are systems or institutions, " && "Human capital brush-up advice only when it concerns people's KSAs (knowledge, skills, abilities, etc.)" && "Please write your answer." "Finally, please state whether the content of your analytical answer is generally "publicly known" or "not publicly known." If the content is publicly known, please provide the web information (URL, etc.) where the content is described. If the content is not publicly known, please provide the web information (URL, etc.) where the content is most similar. When writing the analyzed answer, be sure to write the following at the very beginning (at the beginning of the line): ((α)·(β)·(γ)) "(α)" & "Improvement proposal" & [RO] Reflection - Name & "[Specific behavioral indicator]" && ["Strengths"] ●●●●●●●●●●●●●●●" && ["Purpose]●●●●●●●●●●●●●●" && ["Summary]●●●●●●●●●●●●●●●" & ["Results / Effects] ●●●●●●●●●●●●●●●" && ["Capital Type"] ●● Capital &&& (β)〔" &〔RO〕Global-emotion indicator & "〕Behavioral indicator incorporating elements "●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●" &&& "Is (γ) (the content of (α)) publicly known or not?" && "●●●●●●" &&& "When writing your answer, please be sure to write (α)·(β)·(γ) first." "Please write any other reasons after (α)·(β)·(γ)." "Now, I will provide you with the analysis information below. Thank you for your attention." "-------------------------------------------------------------" && "Hereinafter, ``Current Events Contents''" &&& "<Title>:" &〔 RO〕News Title-Rel & ¶ & "<Main text>:" &〔NE〕Current Affairs and News Library〔NE〕News Contents && "-------------------------------------------------------------" &&& "-------------------------------------------------------------" && "The following are the company's selling points" &&& [RO] For [S2] Setting - Company - Non-Financial Capital [S2] ● Part 2 - Aggregation - Capital Items - Trade Secrets · ROIC Analysis && "-------------------------------------------------------------"&&& "-------------------------------------------------------------" && "The following are the specific behavioral indicators for team members" [RO] Team Capital [S1] Settings - People - Non-Financial Capital [S1] Aggregation - ROIC Display - List - B - Overview - Capital Set && "-------------------------------------------------------------"

[0309] In the above automatically generated resource, a character or string enclosed in double quotation marks, such as "our" (in the above example, "our" applies), means that it is part of the text explaining the first inference request in the first inference request data. "&" is an instruction to connect the characters or strings before and after it seamlessly.

[0310] As in [RO] Global-KPI rate-type, [RO] and the string that follows it (in the above example, "Global-KPI rate-type") is the "target field," and is an instruction to place a specific string indicating the information entered by the first operator in this target field. The relationship (linking) between the target field and the information (string) is set separately.

[0311] Therefore, if the character string linked to the target field "(RO) Global-KPI rate-type" is "Sales growth rate", the text "Our sales growth rate" will be automatically generated by the auto-generation resource consisting of "Our " & (RO) Global-KPI rate-type".

[0312] "&&" is an instruction to insert a line break in the automatically generated text, and "&&&" is an instruction to insert a line break and one blank line in the automatically generated text.

[0313] First prompt generation unit 112 first reads the above-mentioned automatically generated resource from the storage device, and then prepares a character string corresponding to each target field. To prepare this character string, information may be obtained from first information identification unit 111, which has information generated by input information generation unit 130, or the necessary information may be collected by directly accessing IACC information database 300, etc. When information is obtained from first information identification unit 111, the information obtained is appropriately managed by access control unit 131B, whereas when IACC information database 300 is directly accessed, separate access management is performed.

[0314] An example of text automatically generated by first prompt generating unit 112 from the above-mentioned automatically generated resources is as follows:

[0315] In order to achieve our sales growth rate target (including improvement), Our company's [corporate selling points] (each point is separated by a line break) Team members' [specific behavioral indicators] are compared with the following "current events" A summary of specific actions proposed by Kunihiro Katagiri: Please answer by providing advice on how to brush up "01-B [Trade Secret Candidate / Personal Skills]★★★" into the following three types of capital: "manufactured capital," "social / relationship capital," and "natural capital." If the specific actions proposed by Kunihiro Katagiri, the subject of the analysis, are institutions or systems, Please write your answer as "human capital" brush-up advice only if it relates to a person's KSAs (knowledge, skills, abilities, etc.). Finally, please state whether the content of your analytical answer is generally "publicly known" or "not publicly known." If the information is publicly known, please also provide the web information (URL, etc.) where it is written, and if it is not publicly known, please provide the web information (URL, etc.) that provides the most similar information. When writing the analyzed answer, be sure to write the following at the very beginning (at the beginning of the line): ((α)·(β)·(γ)). (α) Improvement proposal by Kunihiro Katagiri [Specific behavioral indicators] [Strengths]●●●●●●●●●●●●●●● [Purpose]●●●●●●●●●●●●●● [Summary]●●●●●●●●●●●●●●● [Results / Effects]●●●●●●●●●●●●●●● [Capital type] ●● capital (β) Behavioral indicators incorporating the element of "feeling happy" "●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●" Is (γ) (the content of (α)) publicly known or not? "●●●●●●" When writing your answer, be sure to write (α), (β), and (γ) first. Other reasons should be listed after (α), (β), and (γ). So, I will provide you with the following analysis information. Thank you for your understanding. ------------------------------------------------------------- Below is "Current Events" <Title>:01 [Current affairs title regarding sales growth rate] <Main text>:01[Current affairs regarding sales growth rate]★★★ ------------------------------------------------------------- ------------------------------------------------------------- Below are the company's selling points: [Company selling point] ★★★ [Company selling point] ★★★ ------------------------------------------------------------- ------------------------------------------------------------- Below are the specific behavioral indicators for team members: [Specific behavioral indicators] ★★★ [Specific behavioral indicators] ★★★ -------------------------------------------------------------

[0316] In the automatically generated text above, the character string 01-B[Trade Secret Candidate KSAs]★★★ is actually followed by a character string indicating specific action summary information linked to the target field called [RO]Global-Action Item. The same is true for the other "★★★" characters in the automatically generated text, which means that in reality, a character string indicating specific information will be placed.

[0317] In the above example, the first inference request data automatically generated by the first prompt generation unit 112 includes text, but is not limited to this. The first inference request data may also include sonic / vocal information including voice and music, or may also include images and videos. In this way, when the first inference request data includes data other than text, other pre-trained generative models (such as a music generation model, a video generation model, or an image generation model) may be used as the generation data instead of or in addition to the large-scale language model, or a multimodal model may be used.

[0318] The following describes the functions of the initial inference request generation unit 180. Fig. 22 is a block diagram illustrating the functions of the initial inference request generation unit. The initial inference request generation unit 180 has an IACC partial information identification unit 181, an initial prompt generation unit 182, and an input display generation unit 183.

[0319] The IACC partial information identification unit 181 receives IACC basic information data, which is basic information data of the IACC information data, from the initial input / output device 410, and identifies multiple pieces of partial information included in the IACC basic information data. The multiple pieces of partial information are basic data of the IACC information data and constitute the information of the IACC.

[0320] The input display generation unit 183 executes processing including generating input display data having a plurality of input fields corresponding to a plurality of pieces of partial information to be included in the IACC information data, and outputs the input display data to the initial input / output device 410. An example of a display (graphical user interface) output to the initial input / output device 410 based on the input display data generated by the input display generation unit 183 is shown in FIG.

[0321] Specifically, the display (graphical user interface) shown in FIG. 6 has multiple input fields for text, etc. These input fields correspond to the multiple target fields of the first format, namely, (A) Strengths / Selling Points, (B) Summary, (C) Purpose / Objectives, (D) Results or Effects, (E) Concerns / Challenges, (F) Capital Type, and (G) Status. Text data, etc., entered into each input field by the initial input / output device 410 is saved as the value of the corresponding target field. In addition to these input fields, FIG. 6 also displays an input field for "improvement notes during AI analysis" in which a character string equivalent to the name of the IACC can be entered.

[0322] The (F) capital type included in the first format can be entered in an input field using the initial input / output device 410. However, when the entry process shown in FIG. 19 or the like is executed and a capital type is proposed from the initial generation model 920, the capital type related to the proposal is overwritten and displayed in this input field, allowing the first operator to confirm the capital type related to the proposal. The first operator may adopt the capital type related to the proposal or may enter a different capital type in the input field again. The capital type finally displayed in this input field becomes the value of the target field corresponding to the (F) capital type in the first format.

[0323] The (G) status included in the first format is also shown as an input field, but by executing the entry process, the processing results of the initial public knowledge display generation unit 280, which is executed based on the proposal of the initial generation model 920, are overwritten and displayed, allowing the first operator to confirm information regarding the public knowledge of the IACC.

[0324] When a first operator inputs multiple pieces of partial information into multiple input fields of the graphical user interface displayed on the initial input / output device 410, the IACC partial information identification unit 181 receives data including the multiple pieces of partial information as input, generates multiple pieces of data corresponding to each of the multiple pieces of partial information, and outputs a group of data sets including the multiple pieces of data to the initial prompt generation unit 182.

[0325] The initial prompt generation unit 182 receives a group of data sets output by the IACC partial information identification unit 181 and identifies multiple pieces of partial information from the data sets. The initial prompt generation unit 182 has an initial format, which is format data in which target fields into which each of the identified multiple pieces of partial information should be placed are specified in advance. The initial prompt generation unit 182 automatically generates initial inference request data, including executing a process to automatically place the corresponding multiple pieces of partial information in each of the target fields of the initial format. The automatically generated initial inference request data is output from the initial prompt generation unit 182 to the initial transmission / reception unit 190 and transmitted to the initial generation model 920.

[0326] An example of an automatically generated resource possessed by the initial prompt generation unit 182 is shown below. This automatically generated resource corresponds to the above-mentioned initial format. The rules for string manipulation in automatically generated resources are the same as those in the first prompt generation unit 112. In the automatically generated resource below, "(S1) ● Part 1-Cal-Capital Item" is a target field set in the initial format, and a string indicating partial information entered by the initial operator is automatically placed in this target field. The automatically generated text in this way constitutes at least a part of the initial inference request data.

[0327] "The following sentence [to be analyzed]" && [Compare [Analysis Material Text 1 (Team Member Capital Definition)] and [Analysis Material Text 2 (Our Company Capital Definition)], " && Please answer by determining which of the five non-financial capitals you are referring to: "human capital," "intellectual capital," "manufactured capital," "social capital," or "natural capital." Also, please determine whether the content is generally "publicly known" or "not publicly known." Please use the following format to write your answer: "Answer" &&& "Capital type: ●● capital / Patent characteristics: publicly known or not publicly known" &&& "Please start your answer with ", then explain your reason." "Please be sure to further describe your request and response policy after that." "Please speak entirely in Japanese." "──────────────────────────────────"&& ["Analysis target]↓" &&& [S1] Part 1-Cal-Capital Items && "──────────────────────────────────"&&& "──────────────────────────────────"&& ["Analysis material text 1 (team member capital definition)]↓" &&& Self-Relation (S1) Team - Analysis (S1) Part 2 - Aggregation - Capital Items - ROIC Analysis "──────────────────────────────────"&&& "──────────────────────────────────"&& ["Analysis material text 2 (our capital definition)]↓" &&& [S2] Setting - Company - Non-financial capital [S2] ● Part 2 - Aggregation - Capital items - Trade secrets · ROIC analysis && "────────────────────────────────"

[0328] An example of the text that the initial prompt generator 182 automatically generates from the above-mentioned automatically generated resources is as follows:

[0329] The following sentences [subject to analysis] Compare [Analysis Material Text 1 (Team Member Capital Definition)] and [Analysis Material Text 2 (Our Company Capital Definition)], Please answer by determining which of the five non-financial capitals (human capital, intellectual capital, manufactured capital, social capital, and natural capital) your case falls under, and whether the content is generally known or not. Please write your answer in the following format: answer Capital type: ●● Capital / Patent characteristics: Publicly known or not publicly known Always start your answer with this and then explain why. Please be sure to further describe the details of your request and your response policy afterwards. Please write everything in Japanese. ─────────────────────────────────── [Analysis target]↓ [Company selling points] 01-A [Trade secret candidate, personal skills] ★★★ <Summary> 01-B [Trade secret candidate, personal skills] ★★★ <Objective> 01-C [Trade secret candidate, personal skills] ★★★ Purpose, objective <Results, effects> 01-D [Trade secret candidate, personal skills] Results or effects ★★★ ─────────────────────────────────── ─────────────────────────────────── [Analysis material text 1 (team member capital definition)]↓ [Company selling point] ★★★ [Company selling point] ★★★ [Company selling point] ★★★ ─────────────────────────────────── ─────────────────────────────────── [Analysis material text 2 (our capital definition)]↓ [Company selling point] ★★★ [Company selling point] ★★★ [Company selling point] ★★★ ───────────────────────────────────

[0330] In the above example, the initial inference request data automatically generated by the initial prompt generating unit 182 includes text, but is not limited to this. The initial inference request data may include sonic / vocal information including voice and music, or may include images and videos.

[0331] The following describes the function of the intangible resource inference request generation unit 230. Figure 23 is a block diagram illustrating the function of the intangible resource inference request generation unit. The intangible resource inference request generation unit 230 has an intangible resource information input unit 231 and an intangible resource prompt generation unit 232.

[0332] The intangible resource information input unit 231 accepts intangible resource basic information data from the intangible resource information database 310 and identifies intangible resource information included in the intangible resource basic information data. The intangible resource prompt generation unit 232 automatically generates intangible resource inference request data, including executing a process of automatically placing intangible resource information in a target field of an intangible resource format, which is format data in which the target field into which the intangible resource information should be placed is specified in advance. The generated intangible resource inference request data is output to the intangible resource transmission / reception unit 240, which, upon receiving this data, transmits the intangible resource inference request data to the intangible resource generation model 930 and receives intangible resource inference result data from the intangible resource generation model 930.

[0333] An example of an automatically generated resource possessed by the intangible resource prompt generation unit 232 is shown below. This automatically generated resource corresponds to the intangible resource format described above. The rules for string manipulation in the automatically generated resource are the same as those in the first prompt generation unit 112. In the automatically generated resource below, "(S2) Capital - Description and Remarks" is the target field set in the intangible resource format, and a string corresponding to information contained in the intangible resource basic information data is automatically placed in this target field. The automatically generated text in this way constitutes at least a part of the initial inference request data.

[0334] Analyze the following text and identify the relevant items and targets in the SDGs. Please tell me the number. By the way, please tell me the reason and " && " Finally, please answer "Target1.1" (all in Japanese). Furthermore, Please tell us which of the following five applies to you: "Human capital," "Intellectual capital," "Manufactured capital," "Social capital," or "Natural capital." Please leave a line break at the end of your answer. "●●Capital / Target0.0" && " Please write it like this: " &&& [S2] Capital - Explanation and Notes

[0335] Analyze the following text and identify the relevant items and targets in the SDGs. Please tell me the number. By the way, please tell me the reason and Finally, please answer "Target 1.1" (all in Japanese). Furthermore, please tell us which of the five categories of "human capital," "intellectual capital," "manufactured capital," "social capital," and "natural capital" applies to you. Please leave a line break at the end of your answer. ●●Capital / Target0.0 Please write it like this. Five types of non-financial capital for companies★★★

[0336] In the above example, the intangible resource inference request data automatically generated by the intangible resource prompt generation unit 232 includes text, but is not limited to this. The intangible resource inference request data may include sonic / vocal information including voice and music, or may include images and videos.

[0337] An organizational value improvement support program according to one embodiment of the present disclosure sends first inference request data generated based on a first group of input information data to a first generative model, which is a pre-trained generative model, and receives first inference result data from the first generative model.

[0338] The organization value improvement support program includes the following processes. (a) collecting data to be included in a first set of input information data; (b) generating first inference request data based on the first input information data group; (c) sending the first inference request data to the first generative model; (d) receiving first inference result data from the first generative model;

[0339] The first input information data group includes IACC information data including information on the IACC, intangible resource information data including information on intangible resources belonging to the same type of non-financial capital as the non-financial capital to which the IACC belongs among intangible resources possessed by the organization to which the member belongs, and performance-related information data including target values ​​for financial and performance indicators of the organization. The first inference request included in the first inference request data includes generating a first proposal to improve the financial and performance indicators included in the performance-related information data based on the IACC information data and the intangible resource information data.

[0340] An organization value improvement support system according to an embodiment of the present disclosure includes a first device having an input / output function, a second device having a data storage function and including at least one device, and a third device having a control unit. The first device is capable of communicating with the second device and the third device.

[0341] The second device has an IACC information database that stores IACC information data including information about the IACC, an intangible resource information database that stores data including information about intangible resources owned by the organization to which the member belongs, and a performance-related information database that stores performance-related information data including target values ​​for the organization's financial and performance indicators.

[0342] The control unit of the third device has a first inference request generation unit that generates first inference request data to be input to the pre-trained generative model based on data input from the first device.

[0343] The first device includes an input / output unit and a transmission / reception unit. The input / output unit receives from the second device a first input information data group including IACC information data, intangible resource information data that is part of the data stored in the intangible resource information database and includes information on intangible resources that belong to the same type of non-financial capital as the non-financial capital to which the IACC belongs, and performance-related information data, and outputs the first input information data group to the third device. The transmission / reception unit transmits the first inference request data input from the third device to the pre-trained generative model and receives first inference result data from the pre-trained generative model.

[0344] The first inference request included in the first inference request data includes generating a first proposal to improve financial and performance indicators included in the performance-related information data based on the IACC information data and the intangible resource information data.

[0345] In the above-described organization value improvement support system, at least two of the first device, the second device, and the third device may be integrated.

[0346] An organizational value improvement support system according to another embodiment of the present disclosure includes a plurality of computer devices forming a peer-to-peer network. At least one of the plurality of computer devices includes a first inference request generation unit that generates first inference request data to be input to a pre-trained generative model based on input data. At least one of the plurality of computer devices includes a transceiver unit that transmits the first inference request data to the pre-trained generative model and receives first inference result data from the pre-trained generative model. At least one of the plurality of computer devices includes an input information generation unit that collects data from a plurality of databases and generates a first set of input information data.

[0347] The multiple databases include an IACC information database that stores IACC information data including information about the IACC, an intangible resource information database that stores data including information about intangible resources owned by the organization to which the member belongs, and a performance-related information database that stores performance-related information data including target values ​​for the organization's financial and performance indicators.

[0348] The first inference request included in the first inference request data includes generating a first proposal to improve financial and performance indicators included in the performance-related information data based on the IACC information data and intangible resource information data, which is part of the data accumulated in the intangible resource information database and includes information on intangible resources that belong to the same type of non-financial capital as the non-financial capital to which the IACC belongs.

[0349] Another embodiment of the present disclosure will be described below. Fig. 24 is a block diagram illustrating an organization value improvement support system including an organization value improvement support device according to another embodiment of the present disclosure.

[0350] An overview of the organizational value improvement support system according to this embodiment will be described below.

[0351] In Japan, financial information (balance sheet, income statement, cash flow statement) representing the past financial results of companies and other organizations has been criticized for lacking information needed to estimate the future value of the organization. In other words, in the case of a joint-stock company, investors need information to predict the future course of the company and make investment decisions based on whether to invest additional shares or sell them. In particular, institutional investors, including activists, have strongly requested the government to disclose information about future corporate value and the type of non-financial capital (capital required for the business). Based on this recognition, the Tokyo Stock Exchange has called for the disclosure of non-financial information as "soft law." The Corporate Governance Code (CGC) is an example of such soft law, and its second revision was officially published and implemented on June 11, 2021.

[0352] In response to these demands, listed companies now need both "financial information," which is the traditional result of past performance, and "non-financial information," which will be used to determine future stock price trends. This has led to the need for an "integrated" version of both information. This has led to the need for disclosure documents known as "integrated reports."

[0353] The IIRC defined five non-financial capitals (Non-Patent Document 2), finalizing the disclosure items for capital (capital) included in non-financial information. Furthermore, the "legal nature" of these five non-financial capitals can become industrial property rights such as patents or trade secrets through a rights-granting process. However, in the case of self-developed technology, current accounting standards do not allow for the recording of capital as an asset, since the "objective acquisition cost" is unclear, as in the case of sales or licensing. If left to the company's "estimates," there is a risk of falsification. Therefore, capital is not included in financial information. Meanwhile, international accounting standards allow for capitalization based on similar cases.

[0354] In this regard, the organizational value improvement support systems 1000 and 1000A according to the embodiments of the present disclosure described above "visualize" the capital contained in non-financial information, and (I) ensure the three requirements for trade secrets as assets, while (II) clarifying the ledger functions of the five non-financial capitals, thereby resolving the above-mentioned problem (clarifying the objects of capital (capital) contained in non-financial information).

[0355] In addition to the above (I) and (II), the organizational value improvement support system 1000B according to this embodiment contributes to solving management and operational issues in relation to organizational value such as enterprise value. That is, (III) it solves the issue of the so-called "PBR below 1 problem" that the Tokyo Stock Exchange has adopted in response to requests from institutional investors including activists and is pursuing for listed companies, thereby contributing to improving enterprise value.

[0356] PBR stands for price-to-book ratio, which indicates how many times the stock price is compared to the net assets per share, and is one of the criteria for evaluating a company. In other words, PBR is calculated by dividing the stock price by the net assets actually owned by the company per share, and the answer is the ratio of the two. If the two amounts are the same, the PBR will be 1, so the base value is 1. When PBR=1, market valuation (= stock price x total number of issued shares) = net asset value When PBR>1, the market valuation is higher than the net asset value → non-financial capital is high When PBR<1, the market valuation is lower than the net asset value → non-financial capital is low

[0357] The "PBR below 1x problem" occurs when a listed company's market valuation (= stock price x total number of issued shares) is lower than its net asset value. When this happens, activists would be better off for the company to immediately cease operations, sell all assets, and use the proceeds for dividends, rather than continuing its business activities. In other words, a company with a PBR below 1x means that it is in a state where it cannot achieve "sustainable growth," and this is what the "PBR below 1x problem" is concerned about.

[0358] From the perspective of solely resolving this issue, reducing the denominator in the PBR calculation (specifically, by reducing equity capital through share buybacks, dividend increases, etc., the net assets in the denominator can be reduced) would also be a solution to the problem of PBRs below 1. However, the Tokyo Stock Exchange states that such measures do not fundamentally solve the problem of PBRs below 1, and recommends that companies promote measures that contribute to the sustainable growth of companies.

[0359] For listed companies, although stock prices change from moment to moment, stock prices and the total number of shares issued are publicly available. Furthermore, the most recent net asset value of a listed company is disclosed in its balance sheet, which is a type of financial information. In other words, since the information needed to calculate PBR -- stock price, number of shares, and net asset value -- is all publicly available, it is possible to obtain a listed company's PBR in real time by searching "company name PBR" on the Internet, etc.

[0360] In this way, by appropriately accessing publicly available information via the Internet, etc., it is possible to determine the ever-changing "corporate value (= stock price x total number of issued shares)," and from this corporate value, it is possible to calculate the "total amount of the five non-financial capitals (= corporate value - net assets)." In addition, the "balance sheet (B / S)," one of the traditional financial information, is easily available.

[0361] Therefore, in the enterprise value financial statements according to this embodiment, five non-financial capitals other than financial capital, which are non-financial information, are placed under "financial capital," which is the capital section shown on the right side of a conventional balance sheet. Furthermore, intellectual assets (hereinafter referred to as "self-developed intellectual assets"), which are non-financial information and include "visualized" self-developed patents and trade secrets as shown in other embodiments of this disclosure, are placed under "financial assets," which is the asset section shown on the right side of the balance sheet. The total amount of this self-developed intellectual assets is balanced with the amount (enterprise value - net assets) allocated to the five non-financial capitals placed on the capital side.

[0362] As shown in the above embodiment of the present disclosure, it is clarified which of the non-financial capitals each of the "visualized" self-developed intellectual assets belongs to. Furthermore, in this embodiment, the self-developed intellectual assets are positioned as intangible resource information, and as will be described later, the relative valuation amount (monetary conversion index) of each intangible resource information within the organization (inside the company) is clarified. Therefore, in the enterprise value financial statements according to this embodiment, the internal valuation amount (monetary conversion index) of each of the non-financial capitals can be calculated by multiplying the sum of the relative values ​​of the intangible resource information (self-developed intellectual assets) belonging to each of the non-financial capitals by the total internal valuation amount (monetary conversion index) of the non-financial capital allocated as the enterprise value - net assets.

[0363] In this way, the organizational value improvement support system of this embodiment can disclose organizational value financial statements (or "enterprise value balance sheets" if the organization is a corporation) that clearly show the self-developed intellectual assets and non-financial capital included in non-financial information in addition to a traditional balance sheet (B / S). Furthermore, for listed companies, the total amount of non-financial capital (total internal valuation amount) changes from time to time due to fluctuations in stock prices. Therefore, in this specific example, warnings can be issued to companies as needed, and guidance can be provided to increase or reduce negative non-financial capital (i.e., capital that will serve as the seed capital for future business). This can encourage solutions to the "PBR below 1x problem" and contribute to the sustainable growth of companies.

[0364] The organization value improvement support system 1000B according to this embodiment includes at least one processor and a memory. The memory stores non-transitory instructions, and the organization value improvement support system 1000B is configured such that, when the instructions are executed by the processor, the processor controls the intangible resource information utilization management unit 500 by executing the instructions.

[0365] Here, intangible resource information data is a data structure that allows intangible resource information to be stored, transmitted, and processed mechanically. Intangible resource information is an intangible entity that represents the intangible resources possessed by an organization, and includes information describing the content of the intangible resources in question, as well as attribute information that indicates which of the five non-financial capitals included in non-financial information they belong to.

[0366] As with other embodiments, the intangible resource information data is stored in the intangible resource information database 310 .

[0367] The intangible resource information usage management unit 500 acquires the intangible resource information data to be managed from the intangible resource information database 310. Here, the intangible resource information database 310 may be a database different from the IACC information database 300 (see FIG. 1, etc.), or may share some parts with the IACC information database 300.

[0368] In this embodiment, the intangible resource information utilization management unit 500 includes, in one specific example, a history management unit 510, a value calculation unit 520, a classification setting unit 530, a category value calculation unit 540, an organizational value financial statement creation unit 550, a utilization proposal unit 580, a contribution candidate selection unit 590, a PBR information provision creation unit 560, and an improvement proposal inference request unit 570.

[0369] The history management unit 510 executes processing including generating usage history data including usage history of intangible resource information. The history management unit 510 includes a history recording unit 511 that executes processing including recording the used intangible resource information as contributed intangible resource information, and a contribution amount calculation unit 512 that executes processing including calculating the contribution amount of the contributed intangible resource information using the organization's activity record data.

[0370] Specifically, the history recording unit 511 records the fact that the intangible resource information has been used in the tag data portion of the intangible resource information data including the used intangible resource information. At this time, if not only the fact that the intangible resource information has been used but also information such as the department or person in charge that decided to use it, the purpose of use (for example, the project to be used), the date of decision to use it, and the period of use is recorded, it may become easier for the contribution calculation unit 512 to calculate the contribution amount.

[0371] The contribution amount calculation unit 512 may calculate the contribution amount based on the intangible resource information-derived performance that has occurred and / or is expected to occur with the involvement of the contributing intangible resource information, and the contribution rate of the contributing intangible resource information in the intangible resource information-derived performance.

[0372] Identifying achievements derived from intangible resource information can be performed, for example, as follows: First, the purpose of use of the contributed intangible resource information (e.g., a specific task or project) is identified by referencing the tag data portion of the intangible resource information data, including the contributed intangible resource information, recorded by the history recording unit 511. By reading out the achievement information corresponding to that purpose of use from the achievement-related data stored in the achievement-related information database 320, for example, the achievements derived from intangible resource information can be identified.

[0373] The contribution rate of the contributing intangible resource information in the intangible resource information-derived performance can be determined, for example, as follows: First, the intangible resource information database 310 is accessed to identify another contributing intangible resource information (hereinafter referred to as "second contributing intangible resource information") that is used for the same purpose as the contributing intangible resource information (hereinafter referred to as "first contributing intangible resource information") for which the contribution rate is to be calculated. This step can also be performed by referencing the tag data portion of the intangible resource information data stored in the intangible resource information database 310.

[0374] Once the first contributing intangible resource information and the second contributing intangible resource information have been identified in this way, the degree of contribution of these contributing intangible resource information to the purpose of use is evaluated, and the relative contribution rate (normalized allocation coefficient) of the first contributing intangible resource information is set. Note that if there is no second contributing intangible resource information and there is only one contributing intangible resource information that contributes to the target purpose of use, this relative contribution rate will be 1.

[0375] In addition, the degree to which the first-contribution intangible resource information and the second-contribution intangible resource information as a whole contribute to the business performance of the purpose of use is set. This may be based on the so-called three-way or four-way division method. The three-way division method classifies all resources possessed by an organization, including tangible and intangible resources, into three types: (A) physical resources consisting of tangible objects such as manufacturing equipment; (B) human resources including tacit knowledge and labor that are not formalized and are integrated with the organization's members; and (C) intangible resources consisting of information that has been formalized and has proprietary value; specifically, intangible resources related to intangible resource information contained in the intangible resource information data accumulated in the intangible resource information database 310, and assumes that each contributes one-third to business profits. The four-way method is a model that considers the existence of intangible resources that are not managed by the intangible resource information database 310, such as business strategies, business structures, and distribution channels, and treats their contribution equally with the other three types of resources. In this model, the contribution ratio of intangible resource information stored in the intangible resource information database 310 is 1 / 4. The contribution ratio may be set individually depending on the purpose of use. For example, if the purpose of use is licensing, the contribution ratio of intangible resource information may be set higher than 1 / 3 or 1 / 4, such as 2 / 3.

[0376] The product of the relative contribution rate thus set and the degree of contribution is the contribution rate of the first intangible resource information. The contribution amount of the first intangible resource information is calculated from the product of this contribution rate and the performance derived from the intangible resource information.

[0377] As described above, calculation of the contribution amount requires identifying not only the first-contribution intangible resource information but also other contribution intangible resource information (second-contribution intangible resource information) that contributes to the purpose of use, and also requires setting the degree of contribution of the entire intangible resource information to the purpose of use. To perform these processes, it is necessary to grasp the contents of a large amount of information related to the purpose of use, particularly many documents with different formats. To efficiently perform such processes, as described below, the contribution amount calculation unit 512 includes, as a specific example, a contribution inference request unit 513 that executes processes including generating inference request data for a pre-trained generative model.

[0378] The contribution inference request unit 513 receives as input the contribution intangible resource information (first contribution intangible resource information) and the organization's activity record data, and creates calculation inference request data including an inference request to the calculation generative model 940, which is a pre-trained generative model used for this purpose. Then, it executes processing including sending the inference request data to the calculation generative model 940 and receiving contribution inference result data including a contribution amount proposal from the calculation generative model 940. The inference request included in the calculation inference request data includes a request for a contribution amount proposal.

[0379] In the example shown in FIG. 24 , the organization's activity record data, including the performance of the first-contribution intangible resource information, is stored in the performance-related information database 320. In one example, the calculation-use generative model 940, in response to instructions from the contribution inference request unit 513, references the performance-related information database 320 and the intangible resource information database 310, which stores intangible resource information data, during inference. The performance-related information database 320 also stores data including information related to the organization's performance. In one specific example, the inference request contained in the inference request data includes a request for a proposal of the calculation result of the intangible resource information-derived performance, a proposal of the identification result and calculation result of the relative contribution rate of the second-contribution intangible resource information, a proposal of the contribution degree, and a proposal of the calculation result of the contribution amount of the first-contribution intangible resource information. When the relative contribution rate of the first-contribution intangible resource information is premise information in the inference request, the calculation-use generative model 940 may be able to generate an inference result without accessing the intangible resource information database 310.

[0380] The calculation generative model 940 may continuously or intermittently access the performance-related information database 320 and / or the intangible resource information database 310, and re-learning of the calculation generative model 940 may be performed at appropriate timing. In this case, it becomes easier to quickly output appropriate inference results in response to an inference request from the contribution inference request unit 513.

[0381] The value calculation unit 520 executes processing including calculating the intangible resource value, which is the internal valuation amount (monetary index) of the intangible resource information, based on the usage history data generated by the history management unit 510. In one specific example, the value calculation unit 520 calculates the intangible resource value by allocating to each piece of intangible resource information an apportionment amount set based on the difference between the organization's market capitalization and the identifiable net assets (hereinafter, this amount will also be referred to as the "first difference"). More specifically, the value calculation unit 520 obtains the contribution amount (calculated by the contribution amount calculation unit 512 as described above) of the intangible resource information from the usage history data of the intangible resource information, and sets a contribution rate (normalized apportionment coefficient) for all intangible resource information based on this contribution amount. Identifiable net assets may basically be assets associated with tangible assets, and assets associated with intangible assets may be associated with the intangible resource information and evaluated as part of the first difference. If assets associated with intangibles are included as part of identifiable net assets, the link between the intangibles and the intangible resource information can be removed to prevent double counting of intangible resources.

[0382] The apportionment amount may be the entire first difference or a portion of the first difference. In the latter case, a specific example of a non-financial asset that does not constitute the apportionment amount is the unrealized gain on assets held (particularly real estate). The present value of growth opportunities (PVGO), such as the contribution of ongoing projects, in the first difference can be appropriately evaluated by taking into account the business plan in the value calculation unit 520, discounting the results to their present value, and allocating them to the corresponding intangible resource information.

[0383] If the organization is a stock company, the total value of the shares may be used as the market capitalization, and the total value of the financial assets listed on the most recently published balance sheet may be used as the identifiable net assets. In this case, the total value of the shares fluctuates in real time, so the market capitalization also fluctuates in real time, but the valuation value of the identifiable net assets remains constant for a specified period, such as a quarter. An example of the market capitalization of an organization that is not a stock company is the acquisition price if the organization is acquired.

[0384] As explained above, the value calculation unit 520 sets a contribution rate (normalized allocation coefficient) for all intangible resource information. The intangible resource information may be evaluated and managed based on this contribution rate (normalized allocation coefficient). For example, the contribution rate (normalized allocation coefficient) may be used as the basis for calculating the compensation to the creator of the intangible resource information. Alternatively, it may be used as an evaluation basis when inventorying the intangible resource information. As a specific example, intangible resource information that has maintained a low contribution rate (normalized allocation coefficient) for a certain period of time may be subject to licensing out or removed from the management of the intangible resource information database 310.

[0385] The classification setting unit 530 executes processing including classifying the intangible resource information into a plurality of categories. The method of classifying the intangible resource information in the classification setting unit 530 is not particularly limited, and categorization can be performed using various classifications, and categorization can also be performed from a plurality of perspectives. Categorization from a plurality of perspectives makes it possible to evaluate the intangible resource information stored in the intangible resource information database 310 from multiple perspectives.

[0386] As a specific example of categorization, classification may be performed based on the degree of confidentiality management of intangible resource information data and the five types of non-financial capital to which the intangible resource information belongs. Note that while the object to be managed confidentially is essentially intangible resource information, it is intangible resource information data including intangible resource information that is subject to specific management such as password setting and access management. The classification setting unit 530 may classify intangible resource information data into a discloseable information category and a non-discloseable information category based on the degree of confidentiality management.

[0387] The category value calculation unit 540 calculates a category value, which is the total internal evaluation amount (monetary index), for each of the categories set by the classification setting unit 530, based on the intangible resource value (value of intangible resource information) calculated by the value calculation unit 520. As described above, when categorizing based on two perspectives (degree of secrecy management and five types of non-financial capital), the category value of, for example, the discloseable information category is the sum of the intangible resource values ​​belonging to this category, and the category value of, for example, the human capital category is the sum of the intangible resource values ​​belonging to this category. Furthermore, when categorizing based on the type of organizational activity, the category value of each individual activity can be calculated. Specifically, if the organization is a corporation, the category value of a business division can be calculated by summing up the value of the intangible resource information belonging to a specific business division.

[0388] The organizational value financial statement creation unit 550 performs processing including generating OV-B / S data to create an organizational value balance sheet defined by displaying the sustainable assets section following the assets section (hereinafter referred to as "financial assets" in the sense that it is shown in traditional financial statements) and the non-financial capital section following the capital section (hereinafter referred to as "financial capital" in the sense that it is shown in traditional financial statements) in a conventional general balance sheet.

[0389] In a typical balance sheet, although this differs depending on the accounting standard, assets are listed on the left side and the capital (including liabilities) that balances the assets is listed on the right side. In an organizational value balance sheet, a sustainable assets section is set up following the assets (financial assets) section of a typical balance sheet, and the sustainable assets section shows the category value of discloseable information categories and the category value of non-discloseable information categories. As mentioned above, the total value of intangible resource information is the difference (first difference) between the organization's market capitalization and identifiable net assets (total amount in the financial capital section), so the total amount of the financial assets section and sustainable assets in the organizational value balance sheet is the organization's market capitalization.

[0390] Additionally, the organizational value balance sheet has a non-financial capital section following the capital (financial capital) section of a typical balance sheet, and the non-financial capital section shows the category value of each of the five non-financial capital categories. The total value of these categories is equal to the total value of the discloseable information categories that make up sustainable assets and the non-discloseable information categories. In other words, sustainable assets and non-financial capital are balanced in the organizational value balance sheet.

[0391] Figure 25 illustrates the above organizational value balance sheet. In Figure 25, the revalued amount for unrealized gains on real estate and other assets is listed under financial assets, and the revaluation difference is displayed under financial capital. In other words, in Figure 25, only intangible resources are shown within the scope of non-financial information.

[0392] Here, if the organization is a stock company, the market capitalization of the organization is the company's market capitalization, which is the total stock price (= stock price x total number of issued shares). As mentioned above, it has recently become common to evaluate the value of a stock company by its price-to-book ratio (PBR), and in order to maintain a PBR above 1, stock companies are required to disclose to investors and others measures to increase the company's potential (future) value.

[0393] In this regard, the above-mentioned organizational value financial statements (hereinafter, if the organization is a stock company, also referred to as "enterprise value financial statements" as mentioned above) apply intangible resource information contained in the intangible resource information data stored in the intangible resource information database 310 as the object of the first difference, which is positioned as the company's potential value not expressed in financial statements. Furthermore, the value of individual intangible resource information (intangible resource value) and the value of a category consisting of multiple intangible resource information (category value) can be relatively evaluated. In other words, the amount obtained by subtracting the total financial assets from the total stock value is the total sustainable assets, which is the total internally evaluated value of the intangible resource information contained in the intangible resource information data stored in the intangible resource information database 310. Non-financial capital listed under financial capital in the enterprise value financial statements is equal to the amount of sustainable assets. In other words, intangible resource information corresponds to money in a typical balance sheet. If it is evaluated as capital, it becomes non-financial capital, and if it is evaluated as assets, it becomes sustainable assets. This will ensure that non-financial capital and sustainable assets are balanced in the corporate value financial statements.

[0394] To notify the timing of evaluating the value of such intangible resource information, the intangible resource information utilization management unit 500 includes a PBR information provision creation unit 560 that executes processing including creating PBR information provision data for notifying the organization when its price-to-book ratio (PBR) is less than 1, if the organization is a stock company. The specific configuration of the notification indicated by the PBR information provision data is not limited. For example, if the PBR is 1 or greater, the PBR may be displayed in black text, and if the PBR falls below 1, the text color indicating the PBR may be changed to red. Alternatively, if the PBR falls below 1, a notification (e.g., email) may be sent to the department that should take measures to that effect.

[0395] By collecting stock prices for calculating the PBR in real time, an alarm can be issued promptly when the PBR falls below 1. By averaging the stock prices for calculating the PBR over a certain period, the impact of instantaneous fluctuations can be mitigated. The PBR information provision creation unit 560 may also generate PBR information provision data that notifies of the PBR trend in cases other than when the PBR is less than 1. For example, when the PBR is 1 or greater but a downward trend occurs and the PBR approaches 1, or when a downward trend in the PBR becomes apparent (including the detection of an inflection point), PBR information provision data indicating this may be created.

[0396] When considering improvements to PBR, information on non-financial information is necessary, so organizational value financial statements that allow the value of non-financial information to be relatively evaluated by category are extremely useful. From the perspective of supporting considerations based on such organizational value financial statements, the intangible resource information utilization management unit 500 includes an improvement proposal inference request unit 570.

[0397] The improvement proposal inference request unit 570 uses the OV-B / S data created by the organization value financial statement creation unit 550 as input to generate improvement inference request data including an inference request to the improvement generative model 950, which is a pre-trained generative model used for this purpose. The inference request included in the improvement inference request data includes a request for proposals to increase sustainable assets and / or five non-financial capitals. If the organization is a stock company, the inference request may include a request for proposals to increase the organization's PBR (price-to-book ratio), and the proposal may include identifying intangible resources that have a significant impact on the PBR.

[0398] The improvement proposal inference request unit 570 executes processing that includes transmitting the created improvement inference request data to the improvement generation model 950 and receiving improvement inference result data including predetermined proposals from the improvement generation model 950. When identifying intangible resources that have a large impact on PBR as described above, the improvement proposal inference request unit 570 may refer to the intangible resource information database 310, read intangible resource information data corresponding to the identified intangible resources, identify the type of non-financial capital to which the identified intangible resources belong, and output improvement proposal display data for displaying data indicating the identified type of non-financial capital together with the improvement inference result data on an output device such as the improvement proposal input / output device 430.

[0399] If the intangible resource information data also includes information regarding the activity status and planned activities of the intangible asset (for example, information such as which department (more specifically, which member) in the organization is using it or plans to use it for what purpose, for how long; the same applies below), the improvement proposal inference request unit 570 may output improvement proposal display data for displaying that information on the improvement proposal input / output device 430, etc.

[0400] When the improvement proposal inference request unit 570 receives data including information identifying a specific intangible resource from the improvement proposal input / output device 430, it may generate improvement inference request data so that the improvement inference result data from the improvement generation model 950 includes proposals for increasing sustainable assets and / or non-financial capital, focusing on the utilization of the intangible resource.

[0401] The improvement proposal inference request unit 570 may start execution at any timing, but may start execution in association with other elements constituting the organization value improvement support system 1000B according to this embodiment. For example, the improvement proposal inference request unit 570 may start execution when the PBR information provision creation unit 560 creates PBR information provision data.

[0402] 24, the performance-related information database 320 stores performance information and value information (including financial information) of the organization, as well as information about stocks. In one example, the improvement generative model 950, in response to instructions from the improvement proposal inference request unit 570, references the performance-related information database 320 and the intangible resource information database 310, which stores intangible resource information data, during inference. The improvement generative model 950 may reference only one of the performance-related information database 320 and the intangible resource information database 310.

[0403] The improvement generative model 950 may continuously or intermittently access the performance-related information database 320 and / or the intangible resource information database 310, and re-learning of the improvement generative model 950 may be performed at appropriate timing. In this case, it becomes easier to quickly output appropriate inference results in response to an inference request from the improvement proposal inference request unit 570.

[0404] The usage suggestion unit 580 executes processing including proposing the use of intangible resource information to an operator of the usage input / output device 440 (a specific example of which is a member of the organization). If the activities of an organization continue for a predetermined period of time, a large amount of intangible resource information will be accumulated in the intangible resource information database 310. In this case, it is not easy for the members of the organization to determine which intangible resource information they should use to contribute to improving the value of the organization.

[0405] Therefore, in one specific example, the usage suggestion unit 580 causes the proposed generative model 960, which is a pre-trained generative model, to propose intangible resource information that is expected to contribute to improving the value of the organization when used by members of the organization (hereinafter, this intangible resource information will be referred to as "candidate contributor intangible resource information"). The candidate contributor intangible resource information includes the type of non-financial capital to which the intangible resource belongs, and may also include information regarding the activity status and planned activities of the intangible asset (for example, information such as which affiliation (more specifically, which member) in the organization is using or plans to use it for what purpose, for how long).

[0406] That is, in one specific example, the usage suggestion unit 580 includes a contribution candidate inference request unit 581. The contribution candidate inference request unit 581 receives data including information on the organization's activity schedule (activity schedule data) as input and generates proposal inference request data. Here, the proposal inference request data includes an inference request that requests the proposal generation model 960 to propose contribution candidate intangible resource information, which is intangible resource information expected to contribute to improving the value of the organization. The information on the organization's activity schedule is information including future activity plans of the organization's members, such as a business plan, sales plan, and development plan. The contribution candidate inference request unit 581 executes processing including transmitting the generated proposal inference request data to the proposal generation model 960 and receiving, from the proposal generation model 960, proposal inference result data including the contribution candidate intangible resource information generated by the proposal generation model 960.

[0407] As described above, the improvement proposal inference request unit 570 receives a specified intangible resource (resource) via the improvement proposal input / output device 430 and outputs data to the improvement proposal input / output device 430 for displaying an improvement proposal (action plan) for the organization that focuses on the intangible resource, whereas the utilization proposal unit 580 receives activity schedule data including an action plan via the utilization input / output device 440 and outputs data to the utilization input / output device 440 via the contribution candidate selection unit 590 for displaying intangible resources (resources) that can contribute to the activity schedule data.

[0408] 24, activity schedule data is stored in the performance-related information database 320 along with information related to the performance of the organization. In one example, the proposal generation model 960 references the performance-related information database 320 and the intangible resource information database 310, which stores intangible resource information data, during inference in response to an instruction from the contribution candidate inference request unit 581. The proposal generation model 960 may reference only one of the performance-related information database 320 and the intangible resource information database 310.

[0409] The generative model for proposal 960 may continuously or intermittently access the performance-related information database 320 and / or the intangible resource information database 310, and re-learning of the generative model for proposal 960 may be performed at appropriate times. In this case, it becomes easier to quickly output appropriate inference results in response to an inference request from the contribution candidate inference request unit 581.

[0410] In this way, when the proposal generation model 960 is made to propose candidate contributing intangible resource information, it is necessary from the perspective of proper management of confidential information to limit the intangible resource information that forms the population to a range accessible by the operator of the utilization input / output device 440. Therefore, in this embodiment, as a specific example, the intangible resource information utilization management unit 500 generates internal management instruction data that indicates the degree of confidentiality management of the intangible resource information data, and performs confidentiality management of the intangible resource information data stored in the intangible resource information database 310 based on the internal management instruction data. This internal management instruction data may be generated directly by the intangible resource information utilization management unit 500, or the internal management instruction data may be generated by acquiring information generated by the intangible resource information management unit 251 shown in FIG. 16.

[0411] The utilization suggestion unit 580 may include a proposal restriction setting unit 582 that uses the internal management instruction data. The proposal restriction setting unit 582 identifies organizational members involved in the above-mentioned activity schedule (such as a business plan) and determines a range of intangible resource information data that the identified organizational members can access based on the internal management instruction data. Then, the utilization suggestion unit 580 executes a process that includes enabling the utilization suggestion unit 580 to select contribution candidate intangible resource information using intangible resource information included within this range as a population.

[0412] 24, the proposal restriction setting unit 582 generates data indicating a population and outputs this data to the contribution candidate inference request unit 581. Upon receiving this data, the contribution candidate inference request unit 581 includes an instruction to specify the population in the inference request. This enables the proposal generative model 960 to select appropriate intangible resource information from the range of the population specified in the inference request and propose it as contribution candidate intangible resource information, even if it has access to all of the intangible resource information stored in the intangible resource information database 310.

[0413] In this way, the contributing candidate intangible resource information proposed by the proposal generation model 960 may include multiple pieces of intangible resource information, depending on the settings of the inference request included in the proposal inference request data generated by the contribution candidate inference request unit 581. In this case, the intangible resource information usage management unit 500 may be equipped with a contributing candidate selection unit 590 that executes processing including assisting the operator of the usage input / output device 440 in deciding which intangible resource information to actually use, and managing the intangible resource information that has been decided to be used.

[0414] The contribution candidate selection unit 590 generates usage display data for displaying the contribution candidate intangible resource information on the usage input / output device 440 and transmits it to the usage input / output device 440. Next, the intangible resource information selected by the operator of the usage input / output device 440 is input from the usage input / output device 440, history management data indicating that the selected intangible resource information is to be subject to history management is generated, and the generated history management data is output to the history management unit 510. The history management unit 510 receives the history management data as input and executes processing including generating usage history data including the usage history of the intangible resource information.

[0415] The following describes value allocation with respect to the citation notification counting unit 270. As described above, the citation notification counting unit 270 records how many citation notification data items have been generated for each IACC information data item. This recorded information can be used to evaluate the individual values ​​of the IACC items.

[0416] The value allocation calculation unit 271 shown in Fig. 21 is provided for this purpose and sets the value points of the IACC included in the IACC information data from the citation notification data recorded by the citation notification counting unit 270. The value points are set such that the value points for no citations are "1" and the value points for n citations are "n+1." The value points of the IACC set in this way can be used to calculate the value allocation of each IACC by apportioning the total internal valuation amount (monetary conversion index) of the IACC.

[0417] In a specific example, the value allocation calculation unit 271 calculates the sum of the value points of all IACC information (total value points). Here, when calculating the total value points, IACCs that are positioned as candidates for industrial property rights and are in the process of filing an application may be excluded from the calculation because they are subject to a high level of confidentiality management. IACCs that have been excluded from the calculation in this way may also be included in the calculation once the application procedures have been completed.

[0418] The value points of the IACC that are the subject of the value allocation calculation are then divided by this total value point amount to find the allocation ratio, and the value allocation of the IACC that is the subject of the value allocation calculation is calculated by multiplying this allocation ratio by the internal valuation amount (monetary conversion index) of the IACC that is the subject of the value allocation calculation.The total internal valuation amount (monetary conversion index) of non-financial capital that is obtained by subtracting the total amount of financial assets from the market capitalization (total stock value in the case of a joint-stock company) may also be used as the total internal valuation amount (monetary conversion index) of the IACC.

[0419] Here, the IACC information data stored in the intangible resource information database 310 as intangible resource information data contains information indicating the relevance to the original IACC information data. Therefore, the IACC value allocation contained in the corresponding IACC information data can be used as the individual value of the intangible resource contained in the intangible resource information data stored in the intangible resource information database 310.

[0420] Figure 26 shows a specific example in which the display of intangible resource information data includes the value allocation of the corresponding IACC as a display element. In Figure 26, to the right of the display column for the "practitioner" name (Inoue Makio), "3" is displayed as the value point of the IACC corresponding to the intangible resource information being displayed. This means that this IACC has been cited two times to create other IACCs. Also, below the display, "42.957 billion yen," which indicates the value allocation, is displayed as the "management capital allocation value."

[0421] The value allocation calculation unit 271 may calculate an average value allocation by dividing the total internal valuation amount (monetary conversion index) of non-financial capital by the total value points. The average value allocation can be used as a reference value for the value per unit of non-financial capital. Explaining this in more detail based on the example of Figure 26, the intangible resource information data displayed in Figure 26 has a value point of 3, so the average value allocation corresponds to a value point of 1, and is 14.319 billion yen. This average value allocation may be displayed in the organizational value financial statement (enterprise value financial statement). Figure 27 shows a specific example of an enterprise value financial statement in which this average value allocation is displayed as "value allocation."

[0422] The value allocation calculation unit 271 may be configured to execute the following processes (i) to (iii): (i) Identifying an operator requesting access to IACC information data stored in the IACC information database 300, including information on IACC, which is an intangible asset created by members of an organization; (ii) for a third-party IACC not created by the operator and available for viewing by the operator, determining the number of citations of the third-party IACC in response to input data indicating that the operator cites the third-party IACC to create a new IACC; (iii) The value points of the other IACC are defined as the number of citations plus 1, and the value allocation of the other IACC is calculated by allocating the total amount of the IACC's internal evaluation in proportion to the ratio of the value points to the total value points of the IACC accumulated in the IACC information database 300.

[0423] The value allocation calculation unit 271 may be configured to further perform at least one of the following processes (iv) to (vi): (iv) Creating display data for displaying the other IACC and the value allocation of the other IACC, and outputting the data to a display device. A specific example of the display of this display data is shown in FIG. (v) Divide the total internal assessment of non-financial capital by the total value points, which is the sum of the value points of all possible IACC information data points, to obtain an average value allocation. (vi) Creating and outputting display data for displaying the average value allocation and the total internal valuation of non-financial capital to a display device. A specific example of the display of this display data is shown in Figure 27.

[0424] The above-described embodiments are provided to facilitate understanding of the present disclosure and are not intended to limit the present disclosure. Therefore, each element disclosed in the above embodiments is intended to include all design modifications and equivalents that fall within the technical scope of the present disclosure. Figures 28 to 38 are explanatory diagrams (parts 1 to 11) of another example of an organizational value improvement support program according to an embodiment of the present disclosure.

[0425] The present disclosure includes the following aspects. In the following aspects, the term "management resources" is used to mean "intangible resources." [1] A corporate value improvement support device comprising: a first inference request generation unit that generates first inference request data using information data included in a first input information data group as input; and a first transmission / reception unit that sends the first inference request data to a first large-scale language model that has undergone machine learning and receives first inference result data from the first large-scale language model, wherein the first input information data group is data stored in a personal skill information database, and includes personal skill information data including information on employees' personal skills described in a predetermined first format; management resource information data including information on management resources owned by the company to which the employee belongs that belong to the same type of management capital as the management capital to which the personal skills belong; and performance-related information data including target values ​​for financial and performance indicators of the company, wherein the first request included in the first inference request data generates a first proposal to improve the financial and performance indicators included in the performance-related information data based on the personal skill information data and the management resource information data.

[0426] <2> The corporate value improvement support device described in <1> above, wherein the personal skill information data includes information indicating which of the five management capitals included in the non-financial information the personal skill belongs to.

[0427] (3) The corporate value improvement support device described in (1) or (2) above, wherein the first input information data group includes personal emotion information data including information indicating a first emotion, which is an emotion that the employee expects to feel when performing their work, and the first request includes generating a proposal for a behavioral indicator that is in line with the first emotion and contributes to improving the financial and performance indicators.

[0428] <4> A corporate value improvement support device as described in <1> or <2> above, wherein the first input information data group further includes current affairs data including current affairs information about the company, and the first request includes referring to the current affairs information when generating the first proposal.

[0429] <5> The corporate value improvement support device described in <1> or <2> above, further comprising a comparison display generation unit that receives the first inference result data and the personal skill information data as input, displays the information contained in the first inference result data in the first format, and executes a process including generating comparison display data that allows comparison with the information contained in the personal skill information data, and outputs the comparison display data.

[0430] <6> A corporate value improvement support device as described in <1> or <2> above, comprising a first personal skill information registration unit that generates draft personal skill information data in which the information contained in the first inference result data is written in the first format, and, on the condition that an acceptance instruction for the draft personal skill information data is input, replaces the draft personal skill information data with the personal skill information data and stores it in the personal skill information database.

[0431] <7> The corporate value improvement support device described in <6> above, wherein when a correction instruction for the first inference result data is input, the first personal skill information registration unit reflects the correction instruction in the information contained in the first inference result data, generates personal skill information data under correction in which the reflected result is written in the first format, and, provided that an acceptance instruction for the personal skill information data under correction is input, replaces the personal skill information data under correction with the personal skill information data and sets it as the draft personal skill information data to be saved.

[0432] <8> The corporate value improvement support device described in <2> above, further comprising a second inference request generation unit that generates second inference request data using personal skill basic information data as input, which is basic information of the personal skill information data and includes information on the personal skills, and a second transmission / reception unit that sends the second inference request data to a second large-scale language model that has undergone machine learning and receives second inference result data from the second large-scale language model, wherein the second request included in the second inference request data comprises generating a proposal as to which of the five management capitals the personal skill should belong to as a result of analyzing the personal skill basic information data.

[0433] <9> The corporate value improvement support device described in <8> above, comprising a capital display generation unit that receives the second inference result data as input, identifies the proposed type of operating capital from the second inference result data, and performs processing including generating capital display data for displaying the identified type of operating capital.

[0434] <10> The corporate value improvement support device described in <9> above, comprising a second personal skill information registration unit that performs processing including identifying the proposed type of management capital from the second inference result data, generating the personal skill information data based on the identified type of management capital and the personal skill basic information data, and storing the generated personal skill information data in the personal skill information database.

[0435] <11> The ...

Claims

1. A system for supporting the improvement of an organization's value, comprising at least one processor and memory, wherein non-transient instructions are stored in the memory, and when the instructions are executed by the processor, the processor controls an intangible resource information utilization management unit by executing the instructions; where: The intangible resource information is an intangible that represents the intangible resources possessed by the organization, and includes information describing the contents of the intangible resources and attribute information indicating whether the intangible resources belong to non-financial capital; The intangible resource information data is a data structure that allows the intangible resource information to be mechanically stored, transmitted, and processed, and is stored in an intangible resource information database; the intangible resource information utilization management unit acquires the intangible resource information data from the intangible resource information database; The intangible resource information utilization management unit includes a history management unit, a value calculation unit, a classification setting unit, a category value calculation unit, and an organizational value financial statement creation unit, the history management unit executes a process including generating usage history data including a usage history of the intangible resource information; The value calculation unit Using the usage history data, a relative contribution rate is set for the intangible resource information; Based on the set relative contribution rate, an allocation amount set based on the difference between the market capitalization and identifiable net assets of the organization is allocated to each of the intangible resource information, Execute a process including calculating an intangible resource value, which is an internal valuation value of the intangible resource information; Here, if the organization is a stock company, the total value of the shares is used as the market capitalization, and the most recently published balance sheet is referenced as the identifiable net assets. the classification setting unit executes a process including classifying the intangible resource information into a plurality of categories based on the type of non-financial capital to which the intangible resource information belongs; the category value calculation unit executes a process including calculating a category value, which is a total internal valuation amount, for each of the categories based on the intangible resource value calculated by the value calculation unit; The organizational value financial statement creation unit executes a process including generating OV-B / S data for creating an organizational value balance sheet defined by displaying a sustainable asset section after an asset section and a non-financial capital section after an equity section in a balance sheet; The non-financial capital section shall indicate the category value of the category corresponding to each type of non-financial capital. An organizational value improvement support system characterized by the following.

2. The classification setting unit performs classification including a degree of secrecy management of the intangible resource information data as a viewpoint, and classifies the intangible resource information data into a discloseable information category and a non-discloseable information category based on the degree of secrecy management; The organizational value improvement support system of claim 1 , wherein the organizational value financial statement creation unit indicates the category value of the discloseable information category and the category value of the non-discloseable information category in the sustainable assets section.

3. the intangible resource information utilization management unit includes an improvement suggestion inference request unit, The improvement suggestion inference request unit Using the OV-B / S data as input, generating improvement inference request data including an inference request to the improvement generative model; Sending the refinement inference request data to the refinement generative model; receiving remedial inference result data including recommendations to increase said sustainable assets and / or said non-financial capital; The organizational value improvement support system according to claim 1 or 2, which executes a process including the steps of:

4. The improvement generative model is, at the time of inference, in response to an instruction from the improvement proposal inference request unit: a performance-related information database containing performance information of the organization and value information of the organization; and / or The intangible resource information database The organizational value improvement support system according to claim 3, wherein the following is referenced:

5. If the organization is a stock company, The system for supporting improvement of an organization value according to claim 3 , wherein the inference request included in the improvement inference request data includes a request for a proposal to increase the price-to-book ratio of the organization.

6. The history management unit a history recording unit that executes a process including recording the utilized intangible resource information as contributed intangible resource information; a contribution amount calculation unit that executes a process including calculating a contribution amount of the contributed intangible resource information using the activity record data of the organization; Equipped with The contribution calculation unit the Intangible Resource Information-Derived Performance that has arisen and / or will arise in connection with said Contributed Intangible Resource Information; and The contribution rate of the intangible resource information in the performance derived from the intangible resource information; The contribution amount is calculated based on The organization value improvement support system according to claim 1 , wherein the information on the contribution amount is included in the usage history data.

7. The history management unit a history recording unit that executes a process including recording the utilized intangible resource information as contributed intangible resource information; a contribution amount calculation unit that executes a process including calculating a contribution amount of the contributed intangible resource information using the activity record data of the organization; Equipped with the information on the contribution amount is included in the usage history data; the contribution amount calculation unit includes a contribution inference request unit, The contribution inference request unit Using the contributed intangible resource information and the activity record data of the organization as input, creating inference request data for calculation including an inference request to the generative model for calculation; transmitting the calculation inference request data to the calculation generative model; receiving contribution inference result data including contribution amount suggestions; The system for supporting improvement of organizational value according to claim 1 , which executes a process including the steps of:

8. The calculation generative model is, at the time of inference, in accordance with an instruction from the contribution inference request unit: a performance-related information database in which data including information related to the performance of the organization is stored; and / or The intangible resource information database The organizational value improvement support system according to claim 7, wherein the following is referenced:

9. In the case where the organization is a stock company, the intangible resource information utilization management department The organizational value improvement support system described in claim 1, further comprising a PBR information provision creation unit that performs processing including creating PBR information provision data to notify the organization when its price-to-book ratio is less than 1.

10. A system for supporting the improvement of an organization's value, comprising at least one processor and a memory, wherein non-transient instructions are stored in the memory, and when the instructions are executed by the processor, the processor controls an intangible resource information utilization management unit by executing the instructions; where: The intangible resource information is an intangible that represents the intangible resources possessed by the organization, and includes information describing the contents of the intangible resources and attribute information indicating whether the intangible resources belong to non-financial capital; The intangible resource information data is a data structure that allows the intangible resource information to be mechanically stored, transmitted, and processed, and is stored in an intangible resource information database; the intangible resource information utilization management unit acquires the intangible resource information data from the intangible resource information database; the intangible resource information utilization management unit includes a utilization suggestion unit that executes a process including proposing utilization of the intangible resource information to an operator of a utilization input / output device; the utilization suggestion unit includes a contribution candidate inference request unit; The contribution candidate inference request unit generating inference request data for proposal using data including information on the organization's planned activities as input, wherein the inference request data for proposal includes an inference request for requesting the generation model for proposal to propose contribution candidate intangible resource information, which is the intangible resource information expected to contribute to improving the value of the organization; transmitting the proposed inference request data to the proposed generative model; receiving inference result data including the contributing candidate intangible resource information; An organizational value improvement support system that executes a process including:

11. The organizational value improvement support system described in claim 10, wherein the proposed generative model, upon instruction from the contribution candidate inference request unit, refers to a performance-related information database that stores data including information on the organization's planned activities, and / or the intangible resource information database, at the time of inference.

12. The organizational value improvement support system described in claim 10, wherein the intangible resource information utilization management unit is configured to generate internal management instruction data indicating the degree of confidentiality management of the intangible resource information data, and to confidentially manage the intangible resource information data based on the internal management instruction data.

13. the usage suggestion unit includes a suggestion restriction setting unit, The proposal restriction setting unit Identifying organizational members involved in the planned activities; determining a range of the intangible resource information data that the identified organizational member can access based on the internal management instruction data; The intangible resource information included in the range is used as a population, and the utilization suggestion unit is able to select the contribution candidate intangible resource information. The organization value improvement support system according to claim 12, which executes a process including the steps of:

14. The intangible resource information utilization management unit includes a contribution candidate selection unit, The contribution candidate selection unit generating usage display data for displaying the contribution candidate intangible resource information on the usage input / output device and transmitting the generated display data to the usage input / output device; inputting the intangible resource information selected by the operator from the input / output device used; generating history management data indicating that the selected intangible resource information is to be subject to history management; outputting the history management target data to a history management unit that executes a process including generating usage history data including a usage history of the intangible resource information; The system for supporting improvement of organizational value according to claim 10, which executes a process including the steps of:

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