System, method, and program for evaluating a company

Abstract

A system, method and program for evaluating a company using a new index is provided. [Solution] In the system, the processor unit 120 includes a first evaluation means 121 for evaluating the ability of at least one employee of the company, a second evaluation means 122 for evaluating the ability of multiple AI agents used by the company, a third evaluation means 123 for evaluating the compatibility between at least one employee and the multiple AI agents, and a derivation means 124 for deriving an AI index based on the ability of the at least one employee and the ability and compatibility of the multiple AI agents.

Description

[Technical Field]

[0001] The present invention relates to a system, method, and program for valuing a business. [Background technology]

[0002] Efforts have been made to evaluate companies for investment by investors (eg, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2024-32071 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to evaluate companies using a new index. It also aims to provide a system or the like that makes it possible to predict the trend of a company's stock price based on the new index. [Means for solving the problem]

[0005] The present invention evaluates the capabilities of a company's employees, evaluates the capabilities of AI agents within the company, and evaluates the compatibility between employees and AI agents, and based on these evaluations, derives a new indicator, an AI index, for evaluating the company. The present invention provides, for example, the following items. (Item 1) A system for evaluating companies, comprising: a first evaluation means for evaluating the performance of at least one employee of the company; A second evaluation means for evaluating the capabilities of the plurality of AI agents used by the company; a third evaluation means for evaluating compatibility between the at least one employee and the plurality of AI agents; a derivation means for deriving an AI index based on the ability of the at least one employee, the abilities of the plurality of AI agents, and the compatibility; A system comprising: (Item 2) The first evaluation means obtaining data regarding physical characteristics of said at least one employee; estimating the intellectual productivity of said at least one employee based on said data regarding physical characteristics; The system according to the preceding item is configured to perform the following. (Item 3) The first evaluation means obtaining data regarding the psychological characteristics of said at least one employee; and wherein the first evaluation means estimates the intellectual productivity based on the data regarding the physical characteristics and the data regarding the mental characteristics. (Item 4) The second evaluation means assigning a score to each of the plurality of AI agents; deriving an overall score from the scores of each of the plurality of AI agents; 2. The system of claim 1, configured to: (Item 5) The second evaluation means monitoring output from said plurality of AI agents; evaluating the monitored output for at least one metric; assigning the score based on the evaluation result; 2. The system of claim 1, configured to: (Item 6) The system of any one of the preceding items, wherein the at least one indicator includes at least one of an ethical indicator, a technical capability indicator, and a performance indicator. (Item 7) The third evaluation means Obtaining a frequency of use of the plurality of AI agents by the at least one employee and a contribution level of the plurality of AI agents; evaluating the contribution to the frequency of use; 2. The system of claim 1, configured to: (Item 8) The contribution is The degree to which the plurality of AI agents improves the efficiency of the work; and the amount of ideas generated by the plurality of AI agents; and the extent to which said at least one employee has grown through interaction with said plurality of AI agents; and The system of any one of the preceding items, wherein the determination is based on the following: (Item 9) The system described in any one of the preceding items further comprises a prediction means for predicting the trend of the company's stock price or performance based on the AI ​​index. (Item 10) 1. A method for valuing a company, comprising: assessing the performance of at least one employee of said enterprise; Evaluating the capabilities of a plurality of AI agents used by the company; assessing compatibility between the at least one employee and the plurality of AI agents; deriving an AI index based on the capabilities of the at least one employee, the capabilities of the plurality of AI agents, and the compatibility; A method comprising: (Item 10A) 11. The method according to claim 10, comprising the features according to any one of the preceding claims. (Item 11) 1. A program for evaluating a company, the program running on a computer having a processor, the program comprising: assessing the performance of at least one employee of said enterprise; Evaluating the capabilities of a plurality of AI agents used by the company; assessing compatibility between the at least one employee and the plurality of AI agents; deriving an AI index based on the capabilities of the at least one employee, the capabilities of the plurality of AI agents, and the compatibility; A program causing the processor to perform processing including the steps of: (Item 11A) Item 12. A program according to item 11, comprising the features according to any one of the preceding items. (Item 11B) A computer-readable storage medium storing the program according to item 11 or 11A. [Effects of the Invention]

[0006] According to the present invention, a new index for evaluating a company can be provided. This index is an index for evaluating the potential capabilities of a company itself. This index can be correlated with fluctuations in the company's stock price or performance, and can be used for investment. [Brief explanation of the drawings]

[0007] [Figure 1] A diagram showing an example of the flow of services provided by deriving a new AI index [Figure 2] FIG. 1 is a diagram showing an example of the configuration of a system 100 for evaluating a company. [Figure 3] FIG. 1 is a diagram showing an example of a specific configuration of a system 100 for evaluating a company. [Figure 4A] FIG. 1 shows an example of the configuration of a processor unit 120. [Figure 4B] FIG. 10 is a diagram showing another example of the configuration of the processor unit 120. [Figure 5] 1 is a flowchart showing an example of a process (process 50) performed by system 100 for valuing a company. DETAILED DESCRIPTION OF THE INVENTION

[0008] (definition) As used herein, the term "AI agent" refers to an autonomously operating artificial intelligence (AI). An AI agent is designed to satisfy requirements such as autonomy, cooperation, security, privacy, and / or accountability to operate as a human surrogate (i.e., an agent). In particular, the AI ​​agent targeted by the present invention is designed to satisfy at least the accountability requirement so that the AI ​​agent can be evaluated from an ethical perspective. For example, to satisfy the autonomy requirement, an AI agent must have the ability to act autonomously, such as goal setting, planning, execution, situational awareness, and learning and adaptation. To satisfy the cooperation requirement, an AI agent must have the ability to work cooperatively with other agents, such as communicating, coordinating, negotiating, and building trust. To satisfy the security requirement, advanced security measures such as authentication and authorization, confidentiality, integrity, and availability are required. To satisfy the privacy requirement, privacy protection measures such as personal information protection, anonymity, and transparency are required. To satisfy the accountability requirement, an AI agent must have the ability to be held accountable, such as explaining decision-making, auditing actions, and pursuing responsibility. Other requirements may also be considered, such as energy efficiency, scalability, reliability, etc. The AI ​​agent must be able to respond autonomously and appropriately to tasks given by the user.

[0009] As used herein, a "task" refers to a task or work that an AI agent must perform. A task may be given in the form of a question, in which case the term "task" may be used synonymously with "query." The AI ​​agent will output the results of the task.

[0010] In this specification, the term "employee" refers to a person engaged in work at a company, and includes not only those employed by the company, but also company executives and dispatched workers.

[0011] In this specification, "intellectual productivity" refers to the unique human abilities that are difficult to replace with machines or AI. Specifically, it can be qualitative, creative, ethical, and social abilities, or the ability to wisely utilize AI. In contrast, AI specializes in quantitative and speedy abilities. Intellectual productivity can be subdivided into the following abilities, for example: (1) Ability to ask questions and set goals: AI is good at answering given questions, but it is up to humans to set the fundamental questions or goals: "What should we ask?" and "What should we aim for?" The ability to discover and point the way to issues that are truly valuable to society or individuals can be the core of intellectual productivity. (2) Creativity and originality (the ability to create something from nothing): It is the ability to generate completely new ideas, art, concepts, and visions that are not based on existing data or patterns. It is a field that involves intuition, sensitivity, aesthetic sense, and playfulness. (3) Critical thinking and multiple perspectives: It is the ability to not accept information at face value, to question assumptions, and to see the essence of things. It is the ability to evaluate the validity, biases, and limitations of things, including the output of AI, to see things from various perspectives, and to gain deeper insights. (4) Ability to understand complex contexts and utilize tacit knowledge: The ability to understand complex contexts such as nuances that cannot be quantified or put into words, the atmosphere of a situation, culture, and historical background, and to make judgments by integrating one's own experience or tacit knowledge (knowledge or intuition that is difficult to put into words). (5) Ethics and Value Judgments: The ability to judge and act based not only on efficiency or correctness, but also on values ​​such as "good and evil," "beauty and ugliness," and "humanity." In particular, only humans can make decisions when faced with ethical dilemmas. (6) Empathy and communication skills: The ability to understand and empathize with the feelings and intentions of others, build trusting relationships, and work together to get things done. The ability to motivate teams and bring together the wisdom of diverse people is a uniquely human ability. (7) Physicality: Learning and Adaptation through the Five Senses: The ability to learn through physical experience in the real world or through the five senses, and to adapt and respond flexibly to unexpected situations. (8) Ability to collaborate and utilize AI: The ability to use AI not just as a tool, but to understand its characteristics and limitations, collaborate with it in the most optimal way, and extend human capabilities. The skills to give appropriate instructions to AI, interpret and evaluate its output, and ultimately lead to value creation. From the above, intellectual productivity can be said to be the ability to create and realize original, context-sensitive, ethical, and human-centered value by making full use of cognitive abilities, sensibilities, ethics, and sociality, and by effectively utilizing AI.

[0012] (1. "AI Index" - a new indicator for evaluating companies) The inventor of the present invention has developed a new index for evaluating companies. This index visualizes the "intelligence" of a company, its human resources, and AI. The inventor calls this index the "AI Index." The AI ​​Index objectively evaluates a company's own potential, i.e., its "intellectual capital," by integrating an evaluation of human resources capabilities (human brain), AI capabilities (AI brain), and collaboration level (synergy brain). In one example, the AI ​​Index can be a graded evaluation index, and in another example, it can be a continuous index. The AI ​​Index can be a one-dimensional index or a multidimensional index. In a specific example, the AI ​​Index can be a one-dimensional graded index derived from an evaluation of human resources capabilities (human brain), AI capabilities (AI brain), and collaboration level (synergy brain). In another specific example, the AI ​​index may be a multidimensional index having as its components an assessment of human capabilities (human brain), an assessment of AI capabilities (AI brain), and an assessment of collaboration level (synergy brain).

[0013] The inventors of the present invention further believed that the higher a company's AI index, the greater its intellectual capital and the greater its future growth potential. As a result of extensive research, they came to the conclusion that the AI ​​index may be correlated with a company's stock price or business performance. In other words, the inventors of the present invention found that the AI ​​index can be used to predict the trends in a company's stock price or business performance.

[0014] Figure 1 shows an example of the flow of a service that derives and provides a new indicator, the AI ​​index.

[0015] In this example, we will explain the case where Company C has multiple employees E, and at Company C, the multiple employees E use multiple AI agents A to assist them in their work. For example, the multiple employees E interact with the multiple AI agents A to extract information, create ideas, and perform tasks, thereby improving the efficiency of their work. User U uses this service to find out the results of the company's evaluation.

[0016] In step S1, information for evaluating human resource capabilities (human brain) is provided to the system 100.

[0017] The evaluation of human resource capabilities can be based, for example, on the intellectual productivity of a company's employees. As mentioned above, intellectual productivity is a uniquely human ability that is difficult to replace with machines or AI, and can also be expressed by the efficiency with which an employee produces results through intellectual work. In addition to the employee's inherent abilities, intellectual productivity can also be affected by the employee's health status or health risks. If intellectual productivity is high, the employee's human resource capabilities can be evaluated as high, and if intellectual productivity is low, the employee's human resource capabilities can be evaluated as low.

[0018] The information for evaluating the human resource capabilities may include, for example, data on the physical characteristics of the employee E. The data on the physical characteristics may include, for example, data on health, which may be, for example, the results of a medical examination.

[0019] The information for evaluating human resource capabilities may include, for example, data on the psychological characteristics of employee E. The data on the psychological characteristics may include, for example, at least one of data on emotions, data on values ​​or ethics, and data on knowledge or experience. The data on knowledge or experience may include data indicating qualifications held by employee E.

[0020] The information used to evaluate human resource capabilities may be data regularly acquired by the company, or may be data measured by Employee E's device (e.g., a device worn by Employee E or an IoT device around Employee E).

[0021] In step S2, information for evaluating the AI ​​capability (AI brain) is provided to the system 100.

[0022] The evaluation of AI capability may be based on, for example, a score assigned to the AI ​​agent. The score assigned to the AI ​​agent represents characteristics of the AI ​​agent, and the characteristics of the AI ​​agent are represented by at least one indicator. The at least one indicator includes at least one of a technical capability indicator, a performance indicator, or an ethical indicator.

[0023] The information for evaluating AI capabilities may include, for example, data for assigning a score to each of the multiple AI agents. The data for assigning a score to each of the multiple AI agents may be, for example, data obtained by monitoring the output from the multiple AI agents, i.e., may be all or part of the output from the multiple AI agents.

[0024] In step S3, information for evaluating the collaboration level (synergy brain) is provided to the system 100. The collaboration level can be an index showing the compatibility between a plurality of employees E and a plurality of AI agents A.

[0025] The collaboration level may be based on, for example, the frequency of use of AI agent A by employee E and the degree of contribution by AI agent A. In other words, the collaboration level may be based on the degree of contribution relative to the frequency of use.

[0026] The information for evaluating the collaboration level may include, for example, data regarding interactions between the plurality of employees E and the plurality of AI agents A. The data regarding interactions may include, for example, at least one of dialogue data between the plurality of employees E and the plurality of AI agents A, performance data of the plurality of employees E, and performance data of the plurality of AI agents A. The information for evaluating the collaboration level may also include data regarding emotions of the employee E.

[0027] Once this information is provided, the system 100 evaluates human resource capabilities, AI capabilities, and collaboration levels.

[0028] The system 100 estimates intellectual productivity to assess workforce performance.

[0029] The system 100 estimates intellectual productivity based on, for example, data on the physical characteristics of the employee E. For example, the system 100 estimates intellectual productivity from the risk of a disease, typically, the risk of cognitive function. For example, the employee E who is at risk of cognitive function is estimated to have low intellectual productivity.

[0030] The system 100 estimates intellectual productivity based on, for example, data on the mental characteristics of the employee E. For example, intellectual productivity is estimated from data on emotions. For example, an employee E who has a calm mental state is estimated to have higher intellectual productivity than an employee E who is restless. For example, an employee E who has emotions of joy or happiness is estimated to have higher intellectual productivity than an employee E who has emotions of anger or sadness.

[0031] The system 100 assigns scores to AI agents to evaluate their AI capabilities. The scores assigned to each of the AI ​​agents represent characteristics of the AI ​​agent based on at least one indicator, and the at least one indicator includes at least one of a technical capability indicator, a performance indicator, and an ethical indicator.

[0032] The technical capability index is an index that evaluates the technical capability of an AI agent. The technical capability index may be evaluated, for example, from the perspective of at least one of processing ability, learning ability, emotional handling ability, and algorithm complexity. The performance index is an index that evaluates the past performance of an AI agent. The performance index may be evaluated, for example, from the perspective of at least one of past transaction history, success rate, and counterparty satisfaction. The performance index may be past performance with counterparties who have specific emotions. The ethical index is an index that evaluates the ethical correctness of an AI agent. The ethical index may be evaluated, for example, from the perspective of at least one of transparency, fairness, safety, and accountability.

[0033] The score representing the characteristics of an AI agent may preferably represent the characteristics of the AI ​​agent from the perspective of ethical indicators. The inventors of the present invention believed that no matter how capable or proven an AI agent is, output from an ethically questionable AI agent may potentially harm third parties and / or may be inappropriate output based on an unauthorized source, and therefore believed that ethical indicators are particularly important for an AI agent to be suitable for use by a company. Based on this belief, system 100 evaluates AI capability by assigning a score representing the characteristics of the AI ​​agent from at least the perspective of ethical indicators.

[0034] To evaluate the collaboration level, system 100 evaluates the contribution of AI agent A relative to the frequency of use of AI agent A by employee E. For example, if employee E uses AI agent A frequently and also has a high degree of contribution, the collaboration level can be evaluated as high. For example, if employee E uses AI agent A frequently but has a low degree of contribution, the collaboration level can be evaluated as low. For example, if employee E uses AI agent A infrequently, the collaboration level can also be evaluated as low.

[0035] The contribution level may be determined based on at least one of the degree to which work efficiency has been improved by the multiple AI agents A, the amount of ideas generated by the multiple AI agents A, and the degree to which employee E has grown as a result of interacting with the multiple AI agents. For example, if employee E's work efficiency has improved by using AI agent A, the contribution level will be evaluated higher in proportion to the degree of efficiency improvement compared to not using the AI ​​agent. For example, if employee E's use of AI agent A has resulted in many ideas being generated by the AI ​​agent, the contribution level will be evaluated higher in proportion to the amount of ideas generated. For example, if employee E has grown by using AI agent A, the contribution level will be evaluated higher in proportion to the degree to which employee E has grown compared to not using the AI ​​agent.

[0036] The contribution level may be adjusted, for example, according to the emotions of employee E. For example, employee E with relaxed emotions is considered to have no hesitation in using the AI ​​agent, i.e., is considered to have good compatibility with the AI ​​agent. In this case, a positive weight may be assigned to the contribution level. In contrast, employee E with dissatisfied emotions is considered to have no hesitation in using the AI ​​agent, i.e., is considered to have poor compatibility with the AI ​​agent. In this case, a negative weight may be assigned to the contribution level. In this way, the contribution level can be assigned a positive weight or increased when employee E has positive emotions, and can be assigned a negative weight or decreased when employee E has negative emotions. In this way, emotions can be reflected in the compatibility between employee E and the AI ​​agent.

[0037] The system 100 derives an AI index based on the evaluation of human resource capabilities, the evaluation of AI capabilities, and the evaluation of collaboration level. The system 100 can derive the AI ​​index using, for example, machine learning.

[0038] For example, the system 100 can derive an AI index as a multidimensional index having components including a human resource capability evaluation, an AI capability evaluation, and a collaboration level evaluation. In this case, the AI ​​index may be a three-dimensional index or may have additional components other than the above-mentioned components, such as components derived from a human resource capability evaluation, an AI capability evaluation, and a collaboration level evaluation. Each component may be a continuous value or a graded value or level.

[0039] For example, the system 100 can derive an AI index as a one-dimensional indicator having components derived from an assessment of human resource capabilities, an assessment of AI capabilities, and an assessment of collaboration level.

[0040] In step S4, the system 100 outputs the AI ​​index or information based on the AI ​​index to the user U.

[0041] For example, by providing an AI index to user U, user U can learn an objective evaluation of a company's "intellectual capital." User U can use the AI ​​indexes of multiple companies for various purposes. For example, user U may refer to the AI ​​indexes of multiple companies in his / her investment activities, or may refer to the AI ​​indexes of multiple companies in his / her job search. User U may be the manager of the company, and can objectively learn about his / her company's intellectual capital using his / her company's AI index.

[0042] A company with a high AI index can be said to be one where employee E actively uses AI agent A. This can imply that the company's productivity can be expected to improve. Also, a company with a high AI index can be said to have AI agent A used by employee E that is advanced or practical. This can imply that the speed of new business or product development using the company's AI agent can be expected to improve. Also, a company with a high AI index can be said to be one where employee E properly manages their health. This can imply that employee satisfaction is high.

[0043] Information based on the AI ​​indexes of multiple companies (e.g., stock price trends predicted based on the AI ​​indexes) can also be used for various purposes. For example, user U may refer to information based on the AI ​​indexes of multiple companies in investment activities, or may refer to information based on the AI ​​indexes of multiple companies in job hunting.

[0044] In this way, the new index provided by this invention enables the objective evaluation of intellectual capital. The AI ​​Index can provide companies with objective guidelines for their AI strategies and investors with clear criteria for selecting attractive investment targets. Furthermore, it can motivate employees to improve their health while maximizing their abilities. This can create a positive cycle of intellectual capital in which companies, AI agents, and employees mutually enhance each other, promoting sustainable growth. The AI ​​Index is expected to contribute to the development of companies and society.

[0045] The above-described system 100 can be realized, for example, by a system for evaluating companies, which will be described later.

[0046] (2. System configuration for evaluating companies) FIG. 2 shows an example of the configuration of a system 100 for evaluating a company.

[0047] The system 100 is connected to a database unit 200. The system 100 is also connected to at least one employee terminal device 300 via a network N. The system 100 is also connected to at least one server device 400 via the network N. The system 100 is also connected to at least one user terminal device 500 via the network N.

[0048] Here, the employee terminal device 300 is a terminal device used by an employee of a company. The employee uses the terminal device to perform work for the company. The employee also uses the terminal device to access the server device 400 and use the AI ​​agent.

[0049] The server device 400 may be a device capable of implementing an AI agent. The server device 400 has a respective database, holds a respective knowledge or information, and / or holds a respective large-scale language model (LLM). The server device 400 may be provided within a company or may be external to the company.

[0050] An example of the system 100 may be a computer (e.g., a server device) installed at a provider that provides an AI index providing service, and another example of the system 100 may be a computer (e.g., a server device) installed at a provider that provides a service that predicts company stock prices. An example of the user terminal device 500 is a computer (e.g., a terminal device) used by a user who uses the service, but is not limited to this. Here, the computer (server device or terminal device) may be any type of computer. For example, the terminal device may be any type of terminal device, such as a smartphone, tablet, personal computer, smart glasses, or smart watch.

[0051] Although two employee terminal devices 300 are shown in FIG. 2, the number of employee terminal devices 300 is not limited to this. Any number of employee terminal devices 300 may be connected to the system 100 via the network N. Although two server devices 400 are shown in FIG. 2, the number of server devices 400 is not limited to this. Any number of server devices 400 may be connected to the system 100 via the network N. Although two user terminal devices 500 are shown in FIG. 2, the number of user terminal devices 500 is not limited to this. Any number of user terminal devices 500 may be connected to the system 100 via the network N.

[0052] Data on employees and data on AI agents for each of a plurality of companies may be stored in the database unit 200. The database unit 200 may also store various information used to assign scores representing the characteristics of the AI ​​agents.

[0053] FIG. 3 shows an example of a specific configuration of a system 100 for evaluating a company.

[0054] The system 100 comprises an interface section 110, a processor section 120, and a memory section .

[0055] The interface unit 110 exchanges information with the outside of the system 100. The processor unit 120 of the system 100 can receive information from the outside of the system 100 and can send information to the outside of the system 100 via the interface unit 110. The interface unit 110 can exchange information in any format.

[0056] The interface unit 110 includes, for example, an input unit that allows information to be input to the system 100. It does not matter how the input unit allows information to be input to the system 100. For example, if the input unit is a receiver, the receiver may input information by receiving information from outside the system 100 via a network. Alternatively, if the input unit is a data reading device, the input unit may input information by reading information from a storage medium connected to the system 100.

[0057] The interface unit 110 includes, for example, an output unit that enables information to be output from the system 100. It does not matter in what manner the output unit enables information to be output from the system 100. For example, if the output unit is a transmitter, the transmitter may output information by transmitting it to an external device outside the system 100 via a network. Alternatively, if the output unit is a data writing device, the output unit may output information by writing it to a storage medium connected to the system 100.

[0058] The system 100 can, for example, transmit information to the database unit 200 and / or receive information from the database unit 200 via the interface unit 110. The system 100 can, for example, transmit information to the employee terminal device 300 and / or receive information from the employee terminal device 300 via the interface unit 110. The system 100 can, for example, transmit information to the server device 400 and / or receive information from the server device 400 via the interface unit 110. The system 100 can, for example, transmit information to the user terminal device 500 and / or receive information from the user terminal device 500 via the interface unit 110.

[0059] The system 100 can receive, for example, via the interface unit 110, information for deriving an AI index (e.g., information about the company's employees, information about the company's AI agents, and information about interactions between the company's employees and the AI ​​agents).

[0060] The system 100 can transmit, for example, information including the derived AI index via the interface unit 110. The system 100 can transmit, for example, information including the predicted stock price via the interface unit 110.

[0061] The processor unit 120 executes the processing of the system 100 and controls the overall operation of the system 100. The processor unit 120 reads and executes a program stored in the memory unit 130. This allows the system 100 to function as a system that executes desired steps. The processor unit 120 may be implemented by a single processor or by multiple processors.

[0062] The memory unit 130 stores programs required to execute the processing of the system 100, data required to execute the programs, and the like. The memory unit 130 may also store a program (e.g., a program that implements the processing shown in FIG. 5, described below) that causes the processor unit 120 to perform processing for evaluating companies (i.e., processing for deriving an AI index). Here, how the program is stored in the memory unit 130 is not important. For example, the program may be pre-installed in the memory unit 130. Alternatively, the program may be stored in a non-transitory computer-readable storage medium and installed by reading the storage medium. Alternatively, the program may be installed in the memory unit 130 by being downloaded via a network. In this case, the type of network is not important. The memory unit 130 may be implemented by any storage means.

[0063] The database unit 200 stores various information used to assign scores representing the characteristics of AI agents.

[0064] Here, the score representing the characteristics of the AI ​​agent is expressed by at least one index. The at least one index includes at least one of a technical capability index, a performance index, and an ethical index. The database unit 200 may store past outputs from the AI ​​agent in association with each of the technical capability index, the performance index, and the ethical index.

[0065] In the examples shown in FIGS. 2 and 3 , the database unit 200 is provided outside the system 100, but the present invention is not limited to this. At least a portion of the database unit 200 can also be provided inside the system 100. In this case, at least a portion of the database unit 200 may be implemented by the same storage means as the storage means that implements the memory unit 130, or by a storage means different from the storage means that implements the memory unit 130. In either case, at least a portion of the database unit 200 is configured as a storage unit for the system 100. The configuration of the database unit 200 is not limited to a specific hardware configuration. For example, the database unit 200 may be configured as a single hardware component or multiple hardware components. For example, the database unit 200 may be configured as an external hard disk drive for the system 100, as cloud storage connected via a network, or as a distributed network using blockchain technology or the like.

[0066] For example, information about AI agents is stored in the database unit 200 configured as a distributed network using blockchain technology, etc., and at this time, the information about the AI ​​agents is virtually impossible to tamper with. This ensures the reliability of the information about the AI ​​agents. Information about agents can also be stored in the database unit 200 configured as a distributed network using blockchain technology, etc.

[0067] FIG. 4A shows an example of the configuration of the processor unit 120.

[0068] The processor unit 120 includes a first evaluation means 121, a second evaluation means 122, a third evaluation means 123, and a derivation means .

[0069] The first evaluation means 121 is configured to evaluate the ability of at least one employee of the company. The first evaluation means 121 can evaluate the ability of the employee based on the employee's intellectual productivity. If the employee's intellectual productivity is high, the first evaluation means 121 can evaluate the employee's ability as high, and if the employee's intellectual productivity is low, the first evaluation means 121 can evaluate the employee's ability as low.

[0070] In order to estimate the intellectual productivity of the employee, the first evaluation means 121 acquires data on the physical characteristics of the employee. The first evaluation means 121 can acquire the data on the physical characteristics of the employee via the interface unit 110, for example, from the database unit 200 or from the employee terminal device 300.

[0071] The data on physical characteristics includes, for example, health-related data, which may be, for example, health check results. The health check results may preferably include an assessment of cognitive function risk. The data on physical characteristics includes, for example, physiological data. The physiological data may be, for example, data measured by a device that can be worn by employee E or an IoT device around employee E, and may typically be data such as body temperature, blood pressure, heart rate, sweat rate, and brain waves.

[0072] The first evaluation means 121 may further acquire data regarding the mental characteristics of the employee E. The first evaluation means 121 may acquire the data regarding the mental characteristics of the employee E, for example, from the database unit 200 or from the employee terminal device 300 via the interface unit 110.

[0073] The data on mental characteristics includes, for example, data on emotions. The data on emotions can be any data that can be used to read the employee's emotions, such as linguistic data (e.g., text data, voice data) or non-linguistic data (e.g., image data expressing facial expressions or gestures, or physiological data such as heart rate or sweat rate). The system 100 has prior knowledge for reading emotions, which can include, for example, cultural background data (e.g., differences in emotional expression in a particular culture, taboo emotions, etc.), research data (e.g., past counseling cases, psychology research cases, etc.), data recording the circumstances or causes of emotions (e.g., data showing what events or thoughts cause emotions such as joy, sadness, anger, and fear), and data showing the relationship between emotions and behavior (e.g., data recording what behaviors specific emotions are linked to). Based on this prior knowledge, the first evaluation means 121 can read the employee's emotions from the linguistic data or non-linguistic data.

[0074] The data on psychological characteristics includes, for example, data on values ​​or ethics. The data on values ​​or ethics may be data indicating the values ​​or ethics held by a particular employee E, or may be data indicating the values ​​or ethics commonly held by humanity. The data indicating the values ​​or ethics held by a particular employee may be, for example, data describing the employee's outlook on life, beliefs, goals, etc., or data analyzing the values ​​behind the employee's actions or decision-making.

[0075] The data on mental characteristics includes, for example, data on knowledge or experience. The data on knowledge or experience may be data indicating the knowledge or experience of a specific employee E, data indicating the knowledge or experience of a person in a specific field of expertise, or data indicating the knowledge or experience of a person from a specific cultural background. The data on knowledge or experience may include data indicating the qualifications held by the employee, or data indicating the skills to use AI tools and the status of AI training.

[0076] The first evaluation means 121 can estimate intellectual productivity based on data related to the physical characteristics of the employee. The first evaluation means 121 can estimate intellectual productivity using, for example, a trained model. The trained model is a model that has learned the relationship between data related to the physical characteristics of each person and intellectual productivity for multiple people, and when data related to the physical characteristics of the employee is input, the trained model can estimate and output the intellectual productivity of the employee.

[0077] Alternatively, the first evaluation means 121 can estimate intellectual productivity based on data on the employee's physical characteristics and data on the employee's mental characteristics. The first evaluation means 121 can estimate intellectual productivity using, for example, a trained model. The trained model is a model that has learned the relationship between the data on the physical characteristics and data on the mental characteristics of each person and the intellectual productivity of the person for multiple people, and when the trained model receives the data on the employee's physical characteristics and data on the mental characteristics, it can estimate and output the employee's intellectual productivity.

[0078] The evaluation of the employee's ability by the first evaluation means 121 is passed to the derivation means 124 .

[0079] The second evaluation means 122 is configured to evaluate the capabilities of multiple AI agents used by the company. The second evaluation means 122 can evaluate the capabilities of the AI ​​agent based on the score of the AI ​​agent. If the score of the AI ​​agent is high, the second evaluation means 122 can evaluate the capability of the AI ​​agent as high, and if the score of the AI ​​agent is low, the second evaluation means 122 can evaluate the capability of the AI ​​agent as low.

[0080] To assign a score to an AI agent, the second evaluation means 122 monitors the output from the multiple AI agents and acquires all or a portion of the output from each AI agent of the multiple AI agents. The second evaluation means 122 can, for example, continuously monitor the output from the multiple AI agents. For example, the continuous monitoring of the output from the multiple AI agents may be performed by a dedicated monitoring device, in which case the second evaluation means 122 can receive the output from the monitoring device. The second evaluation means 122 can automatically assign a score based on the results of the continuous monitoring by the monitoring device.

[0081] The second evaluation means 122 can evaluate the output from each AI agent with respect to at least one index and assign a score to each AI agent based on the evaluation result. The at least one index can include at least one of a technical capability index, a performance index, or an ethical index. Preferably, the at least one index can include an ethical index.

[0082] More preferably, the at least one index includes a technical capability index or a performance index, and an ethical index. Even more preferably, the at least one index includes a technical capability index, a performance index, and an ethical index. By representing the characteristics of multiple AI agents with multiple indexes, the characteristics of each of the multiple AI agents can be more accurately expressed.

[0083] An ethical index is an index that evaluates the ethical correctness of an AI agent. The ethical index may be evaluated from the perspective of at least one of transparency, fairness, safety, and accountability, for example.

[0084] Transparency indicates whether the rationale for an AI agent's judgment or decision-making process is clear. If an agent can provide a clear rationale when asked, it is said to have high transparency, and the ethical index value is high. Even if an agent cannot provide a clear rationale, it can still clearly show its judgment process.

[0085] Fairness indicates whether the output from an AI agent contains any prejudice or discrimination. If there is no prejudice or discrimination, it can be said to be fair to all parties, and the ethical index value will be high.

[0086] Safety indicates the level of cybersecurity provided by the AI ​​agent. The higher the cybersecurity, the lower the risk of harm to trading partners or third parties, and the higher the ethical index value.

[0087] Accountability, also known as accountability, indicates whether or not the responsibility for the output of an AI agent can be explained. If the responsibility can be clearly explained, the accountability is high, and the ethical index value will be high.

[0088] The second evaluation means 122 monitors the output from each AI agent of the multiple AI agents, detects words, phrases, sentences, etc. that may be related to at least one of transparency, fairness, safety, and accountability, and can assign a score in terms of ethical indicators based on the detection results. For example, if the second evaluation means 122 detects many phrases related to affirming transparency, it can assign a high score in terms of ethical indicators. For example, if the second evaluation means 122 detects many phrases related to denying safety, it can assign a low score in terms of ethical indicators.

[0089] The second evaluation means 122 may assign a score based on rules or machine learning. When assigning a score based on machine learning, a trained model that has learned the relationship between words, phrases, sentences, etc. that may be associated with at least one of transparency, fairness, safety, and accountability and scores in terms of ethical indicators may be used.

[0090] The technical capability index is an index for evaluating the technical capability of an AI agent, and may be evaluated in terms of, for example, at least one of processing ability, learning ability, emotional coping ability, and algorithm complexity.

[0091] Processing power indicates the information processing speed or parallel computing power of the system that builds the AI ​​agent. The faster the information processing speed or parallel computing power, the higher the value of the technical capability index.

[0092] Learning ability indicates the ability of the system that builds the AI ​​agent to acquire new knowledge or skills. The higher the learning ability, the more flexible it can be in responding to changes in employees, and therefore the higher the value of the technical ability index.

[0093] Emotion coping ability indicates the ability to respond appropriately to others who have a specific emotion. The specific emotion can be any one of joy, anger, sadness, pleasure, love, and hate. For example, emotion coping ability can be ability with respect to the emotion of anger (i.e., the ability to respond appropriately to others who have the emotion of anger), ability with respect to the emotion of sadness (i.e., the ability to respond appropriately to others who have the emotion of sadness), ability with respect to the emotion of excitement (i.e., the ability to respond appropriately to others who have the emotion of excitement), etc. An AI agent with high emotion coping ability can generate outputs that are appropriate for specific emotions. For example, an AI agent with high emotion coping ability with respect to the emotion of anger can generate outputs that include information or expressions that soothe the anger. For example, an AI agent with high emotion coping ability with respect to the emotion of sadness can generate outputs that include information or expressions that comfort the sad. For example, an AI agent with high emotion coping ability with respect to the emotion of excitement can generate outputs that include information or expressions that calm or maintain the excitement. For example, an AI agent with high emotional coping skills for the emotion of joy can generate output that adds information or expressions that sustain the joy.

[0094] Algorithm complexity indicates the sophistication of the algorithms used by the system that builds the AI ​​agent. The more complex the algorithm, the more sophisticated the decision-making it allows, and therefore the higher the value of the technical capability index.

[0095] The second evaluation means 122 may assign a score in terms of a technical capability index based on the performance or aspect of a system implementing each AI agent of the plurality of AI agents.

[0096] The second evaluation means 122 may assign a score based on rules or machine learning. When assigning a score based on machine learning, a trained model that has learned the relationship between the performance or aspect of a system that implements an AI agent and a score in terms of a technical capability index can be used.

[0097] The performance index is an index that evaluates the past performance of the AI ​​agent. The performance index may be evaluated, for example, from the perspective of at least one of past transaction history, success rate, and counterparty satisfaction. The performance index may be past performance with counterparties having a specific emotion.

[0098] The past trading history shows the performance of the AI ​​agent's past trading. The more performance there is, the more reliable the AI ​​agent is deemed to be, and the higher the performance index value.

[0099] The success rate indicates the percentage of successful transactions that an AI agent has made in past transactions. The higher the success rate, the higher the AI ​​agent's trading ability is judged to be, and the higher the performance index value.

[0100] Counterparty satisfaction indicates the satisfaction of the other party in a transaction with the AI ​​agent. The higher the satisfaction level, the higher the AI ​​agent's trading ability is judged to be, and the higher the performance index value. For example, the performance index value of an AI agent that is highly satisfied regardless of the emotions of its trading partners may be higher than that of an AI agent that is only highly satisfied by partners with specific emotions.

[0101] The second evaluation means 122 can assign a score in terms of a performance index based on the past trading results of each of the plurality of AI agents.

[0102] The second evaluation means 122 may assign a score based on rules or machine learning. When assigning a score based on machine learning, a trained model that has learned the relationship between past trading results and scores from the perspective of performance indicators can be used.

[0103] In the above example, it has been described that each of the multiple indicators is independent. In other words, the score can be a multidimensional score having each of the multiple indicators as an axis. However, the present invention is not limited to this, and a one-dimensional score may be formed by correlating multiple indicators. The second evaluation means 122 may assign a score based on rules, for example, based on the values ​​of each of the multiple indicators, or may assign a score based on machine learning. When assigning a score based on machine learning, a trained model that has learned the relationship between each value of the multiple indicators and the score can be used.

[0104] The second evaluation means 122 can derive an overall score from the scores of each of the multiple AI agents. For example, the second evaluation means 122 can derive the overall score by summing the scores of each of the multiple AI agents, or by averaging the scores of each of the multiple AI agents. The overall score can be considered an indicator of the advanced nature or practicality of the AI ​​technology possessed by a company, and can be an evaluation of the capabilities of the multiple AI agents.

[0105] The evaluation of the capabilities of the plurality of AI agents by the second evaluation means 122 is passed to a derivation means 124 .

[0106] The third evaluation means 123 is configured to evaluate the compatibility between at least one employee of the company and multiple AI agents. The third evaluation means 123 can evaluate the compatibility based on the contribution of multiple AI agents to the frequency of use of the multiple AI agents by at least one employee of the company. The third evaluation means 123 can evaluate the compatibility as good when the frequency of use is high and the contribution is also high. The third evaluation means 123 can evaluate the compatibility as bad when the frequency of use is high but the contribution is low. The third evaluation means 123 can also evaluate the compatibility as bad when the frequency of use is low.

[0107] The contribution level may be determined based on at least one of the degree to which work efficiency has been improved by the multiple AI agents, the amount of ideas generated by the multiple AI agents, and the degree to which the employee has grown as a result of interaction with the multiple AI agents. For example, if the employee's work efficiency has improved by using an AI agent, the third evaluation means 123 may determine a contribution level that is proportional to the degree of efficiency improvement compared to when the employee had not used the AI ​​agent. For example, if the employee's use of an AI agent has resulted in many ideas being generated by the AI ​​agent, the third evaluation means 123 may determine a contribution level that is proportional to the amount of ideas generated. For example, if the employee has grown by using an AI agent, the third evaluation means 123 may determine a contribution level that is proportional to the degree to which the employee had grown compared to when the employee had not used the AI ​​agent.

[0108] The contribution level may be adjusted, for example, depending on the employee's emotions. The third evaluation means 123 can adjust the contribution level by utilizing the data on emotions acquired by the first evaluation means 121. The third evaluation means 123 can adjust the contribution level so as to give a positive weight or increase the contribution level when the employee has a positive emotion, and can adjust the contribution level so as to give a negative weight or decrease the contribution level when the employee has a negative emotion.

[0109] The compatibility evaluation by the third evaluation means 123 is passed to the derivation means 124 .

[0110] The deriving means 124 is configured to derive an AI index based on the capabilities of the at least one employee and the capabilities and compatibility of the plurality of AI agents.

[0111] The derivation means 124 may derive the AI ​​index on a rule basis, but preferably, the AI ​​index can be derived using machine learning, particularly unsupervised learning.

[0112] In a preferred embodiment, the derivation means 124 can derive an AI index using a trained model that has previously trained the relationship between the abilities of employees of each company, the abilities of AI agents used by each company, the compatibility between employees and AI agents, and the AI ​​index for multiple companies. The trained model learns the relationships between multiple clusters classified by clustering analysis of the abilities of employees of each company, the abilities of AI agents used by each company, and the compatibility between employees and AI agents for each company. These multiple clusters correspond to the stages represented by the AI ​​index. For example, the derivation means 124 can derive an AI index by analyzing to which of multiple clusters the abilities of at least one employee, the abilities of multiple AI agents, and the compatibility belong. This analysis is performed by calculating the similarity between each cluster and a data point represented by the abilities of at least one employee, the abilities of multiple AI agents, and the compatibility.

[0113] In another preferred embodiment, the derivation means 124 can derive the AI ​​index as a multidimensional index having, as components, the ability of at least one employee, the ability of multiple AI agents, and compatibility. The AI ​​index may be a three-dimensional index or an index having more than three dimensions. In the case of an index having more than three dimensions, the derivation means 124 can derive an additional component of the AI ​​index from at least two of the ability of at least one employee, the ability of multiple AI agents, and compatibility. The additional component can be, for example, a calculated value such as the sum, average, minimum, or maximum of at least two of the ability of at least one employee, the ability of multiple AI agents, and compatibility.

[0114] The AI ​​index derived in this way can be used for any purpose, such as predicting the trends in stock prices or business performance of companies, as described below.

[0115] Alternatively, the AI ​​Index can be used for business strategy and internal management. For example, the AI ​​Index can be used for benchmarking purposes, allowing companies to objectively understand their own AI utilization level by comparing it with peers and leading companies. This allows companies to analyze their strengths and weaknesses and identify areas for improvement. For example, the AI ​​Index can be used to measure the progress of digital transformation (DX) and serve as a KPI to measure the progress of AI utilization in a company-wide DX strategy. This allows companies to evaluate the progress of their goals and review their strategies. For example, the AI ​​Index can be used in investment decisions and resource allocation, helping to evaluate the return on investment (ROI) of AI-related projects. This allows companies to determine effective resource allocation. For example, the AI ​​Index can be used for risk management, evaluating and managing risks specific to companies with a high level of AI utilization (e.g., algorithm bias, operational issues, cybersecurity, black box issues, etc.). This allows companies to determine the focus of their governance structure or audits based on their index scores. For example, the AI ​​Index can be used in human resource strategies to predict the demand for necessary human resources (AI developers, data scientists, AI-savvy business personnel, etc.) according to the level of AI utilization, and this can be reflected in recruitment and training plans. The AI ​​Index can also be used as an indicator to measure the need to improve employees' AI literacy. For example, the AI ​​Index can also be used in evaluating organizational culture, as an indirect indicator to measure an organization's receptiveness to the introduction or utilization of AI, or employees' attitude toward working with AI.

[0116] Alternatively, AI indices can be used for external stakeholder purposes. For example, AI indices can be used by investors to evaluate factors other than stock prices and business performance. AI indices can be used to evaluate a company's ethical use of AI, its governance structure, and the impact on employees; to evaluate a company's future innovation capacity, market adaptability, or long-term growth potential; or to evaluate a company's technological advancement, operational efficiency, and post-merger synergy potential. For example, AI indices can be used by financial institutions for credit decisions, where financial institutions can consider the AI ​​index as one of the factors evaluating a company's operational efficiency, competitiveness, and future prospects and reflect it in their lending decisions or ratings. For example, AI indices can be used for supply chain management, where they can evaluate a supplier's technological capabilities, efficiency, risk resilience, etc. For example, AI indices can be used by insurance companies for risk assessment, where insurance companies can use the index to indicate their reliance on AI when assessing operational or cyber risks, and reflect this in calculating insurance premiums, etc. For example, the AI ​​Index can be used for industry analysis or market research, and consulting firms or research companies can use it as data to analyze AI utilization trends, competitive environments, future forecasts, etc. by industry.

[0117] Alternatively, the AI ​​Index can be used for policymaking or social analysis. For example, the AI ​​Index can be used for policymaking, allowing national or local governments to grasp the state of AI adoption across industries and use it as basic data to consider the need for AI promotion support measures, human resource development policies, and regulations, as well as for comparing international competitiveness. For example, the AI ​​Index can be used for labor market analysis, allowing national or local governments to analyze correlations between AI adoption and employment trends, wage levels, changes in required skill sets, and other factors, and to investigate or predict the impact on the labor market. For example, the AI ​​Index can be used for productivity analysis, allowing national or local governments or research institutes to analyze the relationship between AI adoption at the company or industry level and productivity improvements, thereby enabling them to study the impact of AI on economic growth.

[0118] 4B shows another example of the configuration of the processor unit 120. The processor unit 120 shown in FIG. 4B has a configuration that uses an AI index to predict stock prices or business performance. In the example shown in FIG. 4B, the processor unit 120 has a configuration similar to that shown in FIG. 4A, except that the processor unit 120 includes a prediction means 125. Therefore, components similar to those described above with reference to FIG. 4A are given the same reference numerals and will not be described in detail here.

[0119] The first evaluation means 121 is configured to evaluate the performance of at least one employee of the company. The evaluation of the employee's performance by the first evaluation means 121 is passed to the derivation means 124.

[0120] The second evaluation means 122 is configured to evaluate the capabilities of the multiple AI agents used by the company. The evaluation of the capabilities of the multiple AI agents by the second evaluation means 122 is passed to the derivation means 124.

[0121] The third evaluation means 123 is configured to evaluate the compatibility between at least one employee of the company and the plurality of AI agents. The evaluation of the compatibility by the third evaluation means 123 is passed to the derivation means 124.

[0122] The derivation means 124 is configured to derive an AI index based on the capabilities of at least one employee and the capabilities and compatibility of the plurality of AI agents. The AI ​​index derived by the derivation means 124 is passed to the prediction means 125.

[0123] The prediction means 125 is configured to predict the transition of a company's stock price or business performance based on the AI ​​index. As described above, the AI ​​index is an index that objectively evaluates the potential of a company itself, i.e., its "intellectual capital," and it can be understood that the AI ​​index can be correlated with the company's future business performance and, ultimately, its stock price.

[0124] The prediction means 125 can predict trends in stock prices or business performance using a trained model. The trained model is a model that has learned the relationship between the AI ​​index of each company and trends in stock prices or business performance for multiple companies, and when a company's AI index is input, the trained model can predict and output trends in the company's stock prices or business performance.

[0125] In one example, a case will be described in which the AI ​​index is a multidimensional index having as its components the ability of at least one employee, the ability of multiple AI agents, and compatibility.

[0126] The capability of at least one employee, as described above, is based on the employee's intellectual productivity and may be an indicator evaluating the employee's AI literacy, AI tool utilization skills, ability to interpret and judge AI output, adaptability to AI implementation, and related training status. The capability of multiple AI agents, as described above, is based on a score assigned to the AI ​​agent from at least one indicator and may be an indicator evaluating the AI ​​agent's processing power, accuracy, scope of automation, reliability, cost-effectiveness, and the like. Compatibility, as described above, is based on the AI ​​agent's contribution to the frequency of use of the AI ​​agent by employees and may be an indicator evaluating the productivity improvement rate, error reduction rate, speed of new service development, contribution to improving customer satisfaction, degree of interdepartmental collaboration, workflow efficiency between humans and AI agents, and the like. These three indicators may be calculated and updated periodically.

[0127] These three indicators may be correlated with corporate performance or stock prices, and their correlations can be analyzed based on historical data for multiple companies. In particular, historical data can be used to analyze or model how the combination or interaction of the three indicators affects future financial performance (e.g., growth, profitability) and stock returns. For example, there may be interdependencies between multiple AI agents, such as high performance but limited contribution to performance if the employees' abilities or compatibility are low. For example, a high level of balance among all three indicators may tend to indicate sustained high performance. These relationships or trends can be revealed using, for example, machine learning models (e.g., multivariate analysis, structural equation modeling, ensemble learning, etc.). Furthermore, machine learning models can be constructed using the three indicators as input features and future performance or stock prices as output features. In this case, it is particularly important to incorporate interaction terms between the indicators into the model. For example, if the latest AI index is input into a constructed machine learning model, the machine learning model will output a prediction of future performance or stock prices.

[0128] Corporate profiles may be classified according to the trends of the three indicators, and a machine learning model may be constructed for each profile classification. For example, a machine learning model specific to companies with highly capable employees (human resources-driven), a machine learning model specific to companies with highly capable AI agents (AI brain-driven), and a machine learning model specific to companies with high compatibility (synergy-focused) may be constructed, and an appropriate machine learning model may be used for each profile classification.

[0129] Machine learning models can be improved by verifying the effectiveness of predictions or the validity of the three indicators. In particular, AI technology is advancing rapidly, so it is desirable to improve the model to keep up with the latest technology.

[0130] For example, a user can refer to the output stock price or performance trends to decide whether or not to invest in the company. Alternatively, investment decisions (e.g., stock selection, weighting, and investment timing decisions) may be made taking into account the company profile classification and the AI ​​index score itself (or a ranking based on the score). For example, a user can adopt a strategy of investing in companies that are predicted to have high stock prices or performance and that have a good balance of the three AI index indicators.

[0131] Alternatively, the user can refer to the output stock price or performance trends, for example, and decide whether or not to work for the company.

[0132] In this example, we view a company's AI utilization capabilities as a trinity of "technology," "people," and "collaboration," and predict stock prices from the perspective that the overall strength and balance of these determines corporate value over the medium to long term. This can make it possible to evaluate the soundness and sustainability of a company's AI strategy in more depth than by using a single indicator.

[0133] 4A and 4B, the components of the processor unit 120 are provided in the same processor unit 120, but the present invention is not limited to this. A configuration in which the components of the processor unit 120 are distributed across multiple processor units is also within the scope of the present invention. In this case, the multiple processor units may be located in the same hardware component, or in separate hardware components located nearby or remotely.

[0134] Each component of the system 100 described above may be composed of a single hardware component or multiple hardware components. When composed of multiple hardware components, the manner in which the hardware components are connected does not matter. The hardware components may be connected wirelessly or by wire. The system 100 of the present invention is not limited to a specific hardware configuration. It is also within the scope of the present invention for the processor unit 120 to be configured using analog circuits rather than digital circuits. The configuration of the system 100 of the present invention is not limited to the one described above as long as it can realize its functions.

[0135] (3. Processing in the system for evaluating companies) 5 is a flow chart illustrating an example process (process 50) for valuing a company by system 100. Process 50 is performed in processor unit 120 of system 100.

[0136] In step S501, the first evaluation means 121 of the processor unit 120 evaluates the ability of at least one employee of the company. The first evaluation means 121 can evaluate the ability of the employee based on the employee's intellectual productivity.

[0137] For this purpose, in step S501, the first evaluation means 121 obtaining data regarding physical characteristics of at least one employee; Estimating intellectual productivity based on data regarding the physical characteristics of at least one employee; Do the following.

[0138] Alternatively, in step S501, the first evaluation means 121 obtaining data regarding a physical characteristic of at least one employee and data regarding a mental characteristic of at least one employee; estimating intellectual productivity based on data regarding physical characteristics of at least one employee and data regarding mental characteristics of at least one employee; Do the following.

[0139] In step S502, the second evaluation means 122 of the processor unit 120 evaluates the capabilities of multiple AI agents used by the company. The second evaluation means 122 can evaluate the capabilities of the AI ​​agents based on the scores of the AI ​​agents.

[0140] For this purpose, in step S502, the second evaluation means 122 Monitoring output from multiple AI agents; evaluating the monitored output for at least one metric; Based on the evaluation results, a score is assigned to each of the multiple AI agents. The at least one indicator includes at least one of a technical capability indicator, a performance indicator, or an ethical indicator.

[0141] Next, the second evaluation means 122 derives an overall score from the scores of each of the multiple AI agents. For example, the second evaluation means 122 may derive the overall score by adding up the scores of each of the multiple AI agents, or may derive the overall score by averaging the scores of each of the multiple AI agents.

[0142] In step S503, the third evaluation means 123 of the processor unit 120 evaluates the compatibility between at least one employee of the company and multiple AI agents used by the company. The third evaluation means 123 evaluates the compatibility based on the contribution of the multiple AI agents to the frequency of use of the multiple AI agents by the at least one employee of the company.

[0143] For this purpose, the third evaluation means 123 Obtaining the usage frequency of a plurality of AI agents and the contributions of the plurality of AI agents; Evaluating the contribution to frequency of use The third evaluation means 123 can evaluate that the compatibility is good when the frequency of use is high and the degree of contribution is also high. The third evaluation means 123 can evaluate that the compatibility is bad when the frequency of use is high but the degree of contribution is low. The third evaluation means 123 can also evaluate that the compatibility is bad when the frequency of use is low.

[0144] In step S504, the derivation means 124 of the processor unit 120 derives an AI index based on the evaluations in steps S501, S502, and S503. The derivation means 124 can preferably derive the AI ​​index using machine learning, in particular, unsupervised learning.

[0145] After step S504, the prediction means 125 of the processor unit 120 may predict the transition of the company's stock price or business performance based on the AI ​​index derived in step S504.

[0146] In the example described above with reference to FIG. 5, the processes are described as being performed in a specific order, but the order of each process is not limited to that described and may be performed in any order that is logically possible.

[0147] In the example described above with reference to Fig. 5, the processing of each step shown in Fig. 5 can be realized by the processor unit 120 and a program stored in the memory unit 130, but the present invention is not limited to this. At least one of the processing of each step shown in Fig. 5 may be realized by a hardware configuration such as a control circuit.

[0148] The present invention is not limited to the above-described embodiments. It is understood that the scope of the present invention should be interpreted only by the claims. It is understood that a person skilled in the art can implement an equivalent scope based on the description of the present invention and common technical knowledge from the description of specific preferred embodiments of the present invention. [Industrial Applicability]

[0149] The present invention is useful in providing a new index for evaluating a company. [Explanation of symbols]

[0150] C company E. Employee A. AI Agent U User 100 systems 200 Database Department 300 Employee terminal device 400 Server device 500 User terminal device

Claims

1. A system for evaluating companies, comprising: a first evaluation means for evaluating the performance of at least one employee of the company; A second evaluation means for evaluating the capabilities of a plurality of AI agents used by the company; a third evaluation means for evaluating compatibility between the at least one employee and the plurality of AI agents; a derivation means for deriving an AI index based on the ability of the at least one employee, the abilities of the plurality of AI agents, and the compatibility; A system comprising:

2. The first evaluation means obtaining data regarding physical characteristics of said at least one employee; estimating the productivity of the at least one employee based on the data regarding the physical characteristics; The system of claim 1 configured to:

3. The first evaluation means obtaining data regarding a psychological characteristic of said at least one employee; 3. The system according to claim 2, further configured to perform the following: wherein the first evaluation means estimates the intellectual productivity based on the data regarding the physical characteristics and the data regarding the mental characteristics.

4. The second evaluation means assigning a score to each of the plurality of AI agents; deriving an overall score from the scores of each of the plurality of AI agents; and The system of claim 1 configured to:

5. The second evaluation means monitoring output from said plurality of AI agents; evaluating the monitored output for at least one metric; assigning the score based on the evaluation result; The system of claim 4 configured to:

6. The system of claim 5 , wherein the at least one metric includes at least one of an ethics metric, a technical competency metric, and a performance metric.

7. The third evaluation means Obtaining a usage frequency of the plurality of AI agents by the at least one employee and a contribution degree of the plurality of AI agents; evaluating the contribution to the frequency of use; The system of claim 1 configured to:

8. The contribution is The degree of business efficiency improvement by the plurality of AI agents; and The amount of ideas generated by the plurality of AI agents; and the extent to which the at least one employee has grown through interaction with the plurality of AI agents; and The system of claim 7 , wherein the determination is based on:

9. The system according to claim 1 , further comprising a forecasting means for forecasting the trends in stock prices or business performance of the company based on the AI ​​index.

10. 1. A method for valuing a company, the method being executed on a computer having a processor, the method comprising: said processor evaluating the performance of at least one employee of said enterprise; The processor evaluates the capabilities of a plurality of AI agents utilized by the enterprise; the processor evaluating a compatibility between the at least one employee and the plurality of AI agents; the processor deriving an AI index based on the capabilities of the at least one employee, the capabilities of the plurality of AI agents, and the compatibility; A method comprising:

11. 1. A program for evaluating a company, the program running on a computer having a processor, the program comprising: assessing the performance of at least one employee of the enterprise; Evaluating the capabilities of a plurality of AI agents utilized by the company; assessing compatibility between the at least one employee and the plurality of AI agents; deriving an AI index based on the capabilities of the at least one employee, the capabilities of the plurality of AI agents, and the compatibility; A program that causes the processor to perform processing including the steps of:

Images