Information processing system, information processing method, and program

Abstract

This invention provides an information processing device that can generate effective action plans for solving organizational problems by improving the accuracy of evaluating the state of the organization. [Solution] The information processing device comprises at least an analysis unit that evaluates the state of the organization by analyzing bio-related data of multiple members belonging to the organization, and a generation unit that generates a first action plan for solving the organization's problems based on the evaluation results of the state of the organization. The bio-related data includes biological data that shows biological information extracted from biological samples of the members.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] The information processing apparatus described in Patent Document 1 includes a first acquisition unit that acquires information indicating a user's motivation from a response to a survey input by the user in a terminal device, a second acquisition unit that acquires information indicating a condition related to the user's health acquired by the terminal device in the survey, a calculation unit that calculates an index associated with an organization based on the information indicating the user's motivation and the information indicating the condition related to the user's health, and an output unit that outputs the index. As a result, an index considering the user's motivation can be visualized.

[0003] The information indicating the condition includes vital data such as an autonomic nerve index calculated by pulse wave fluctuation analysis or heart rate variability analysis, respiratory rate, oxygen saturation, blood pressure, body weight, body composition, etc.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the information processing apparatus described in Patent Document 1, for information such as vital data, the resolution as an index for evaluating an individual's state may be low. Therefore, the accuracy of evaluating an individual's state may decrease.

[0006] For example, using vital data has limitations in identifying the root causes of problems an individual is experiencing. In other words, vital data cannot identify detailed biochemical factors such as nutritional deficiencies or the accumulation of toxins in the body. These biochemical factors can be the underlying causes of various ailments, such as decreased concentration or lack of energy.

[0007] Since an organization is a collection of individuals, if the resolution of the indicators used to evaluate the state of individuals is low, the accuracy of evaluating the state of the organization to which those individuals belong will decrease. For example, if the detailed biochemical factors mentioned above cannot be identified, it becomes impossible to identify the root causes of individual problems that may be causing problems in the organization, and consequently, the root causes of organizational problems. This can make it difficult to generate effective action plans to solve organizational problems.

[0008] Therefore, this disclosure has been made in view of the above-mentioned problems, and its purpose is to provide an information processing device, an information processing method, and a program that can generate effective action plans for solving organizational problems by improving the accuracy of evaluating the state of the organization. [Means for solving the problem]

[0009] According to this disclosure, an information processing device can be provided comprising at least an analysis unit that evaluates the state of an organization by analyzing bio-related data of multiple members belonging to the organization, and a generation unit that generates a first action plan for solving the organization's problems based on the evaluation results of the state of the organization, wherein the bio-related data includes biological data representing biological information extracted from biological samples of the members.

[0010] According to this disclosure, an information processing method can be provided in which a computer evaluates the state of an organization by analyzing bio-related data of at least several members belonging to the organization, and the computer generates a first action plan for solving the organization's problems based on the evaluation results of the state of the organization, wherein the bio-related data includes biological data representing bio-information extracted from biological samples of the members.

[0011] According to this disclosure, a program can be provided that causes a computer to perform the steps of: evaluating the state of an organization by analyzing bio-related data of at least several members belonging to the organization; and generating a first action plan for solving the organization's problems based on the evaluation results of the state of the organization, wherein the bio-related data includes biological data representing bio-information extracted from biological samples of the members. [Effects of the Invention]

[0012] According to this disclosure, improving the accuracy of assessing the state of an organization makes it possible to generate effective action plans for solving organizational challenges. [Brief explanation of the drawing]

[0013] [Figure 1] This figure shows an example configuration of a support system according to one embodiment of the present invention. [Figure 2] This is a block diagram showing an example configuration of an information processing device according to the same embodiment. [Figure 3] (a) is a schematic diagram showing biological data according to the same embodiment. (b) is a schematic diagram showing mind-related data according to the same embodiment. [Figure 4] This is a diagram illustrating an example of the operation of the information processing device according to the same embodiment. [Figure 5] This figure shows an example of an organizational action plan according to the same embodiment. [Figure 6] This figure shows an example of an organizational root cause map according to the same embodiment. [Figure 7] It is a diagram showing an example of an organizational evaluation report according to the same embodiment. [Figure 8] It is a diagram showing an example of a group of action plan candidates for generating an organizational action plan according to the same embodiment. [Figure 9] It is a diagram showing an example of an individual action plan according to the same embodiment. [Figure 10] It is a diagram showing an example of an individual root cause map according to the same embodiment. [Figure 11] It is a diagram showing an example of an individual evaluation report according to the same embodiment. [Figure 12] It is a diagram showing an example of a group of action plan candidates for generating an individual action plan according to the same embodiment. [Figure 13] It is a schematic diagram showing an example of self-evaluation data of biological-related data according to the same embodiment. [Figure 14] It is a schematic diagram showing an example of biological data of biological-related data according to the same embodiment. [Figure 15] It is a schematic diagram showing an example of analysis logic information for biological data according to the same embodiment. [Figure 16] It is a schematic diagram showing an example of analysis logic information for self-evaluation data and biological data according to the same embodiment. [Figure 17] It is a schematic diagram showing an example of thinking tendency data of mind-related data according to the same embodiment. [Figure 18] It is a diagram showing an example of an analysis logic table for thinking tendency data according to the same embodiment. [Figure 19] It is a schematic diagram showing an example of action tendency data of mind data according to the same embodiment. [Figure 20] (a) is a schematic diagram showing an example of analysis logic information according to the same embodiment. (b) is a schematic diagram showing an example of an analysis logic table according to the same embodiment. [Figure 21] It is a schematic diagram showing an example of interview data according to the same embodiment. [Figure 22]This flowchart shows an example of an information processing method for an organization according to the same embodiment. [Figure 23] This flowchart shows an example of a method for processing information about members according to the same embodiment. [Modes for carrying out the invention]

[0014] Preferred embodiments of this disclosure will be described in detail below with reference to the attached drawings. In this specification and the drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant descriptions will be omitted.

[0015] Figure 1 shows an example configuration of the support system SYS according to one embodiment of the present invention. The support system SYS (information processing system 1) evaluates the members and state of the organization and generates an action plan to solve the problems of the members and the organization.

[0016] Organizations are not particularly limited, but include, for example, companies, associations, institutions (e.g., medical institutions, research institutions), or business entities. Organizations can be, for example, for-profit or non-profit organizations. Furthermore, an organization may be a group as long as it has multiple members. A group may be, for example, a department formed within a company, or a team such as a sports team. Organizational challenges include, for example, one or more of the following: worsening employee turnover, increasing number of employees on leave, shortage of leadership talent, a passive organizational culture, and stagnant sales growth (Figure 6).

[0017] As shown in Figure 1, the support system SYS comprises an information processing system 1, one or more organizational terminals T1, one or more member terminals T2, a physician terminal T3, a health coach terminal T4, and a mind coach terminal T5. The information processing system 1 includes an information processing device 100, a bio-related database device 200, and a mind-related database device 300. The organizational terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, mind coach terminal T5, information processing device 100, bio-related database device 200, and mind-related database device 300 are connected to a network NW. The network NW includes, for example, one or more of the following: the Internet, a LAN (Local Area Network), a public telephone network, a closed network, and a short-range wireless network.

[0018] Organizational terminal T1 is a terminal assigned to members responsible for the operation or management of the organization. These members include, for example, members of the organization's management, senior management, or human resources department. Member terminal T2 is a terminal individually assigned to and used by each member of the organization.

[0019] The physician terminal T3 is a terminal used by physicians. The physician terminal T3 is installed, for example, in medical institutions such as hospitals and clinics. The health coach terminal T4 is a terminal used by health coaches. Health coaches support the physical health of members through the health coach terminal T4, in accordance with the individual action plan generated by the information processing device 100. Health coaches are, for example, experts in nutrition and exercise guidance. Health coaches support improvements in diet and lifestyle habits, for example. The mind coach terminal T5 is a terminal used by mind coaches. Mind coaches support members responsible for the operation or management of an organization from a psychological perspective, in accordance with the organizational action plan generated by the information processing device 100. Mind coaches are, for example, experts in cognitive psychology and / or behavioral psychology. Mind coaches may also support individual members in accordance with their individual action plans.

[0020] Cognitive psychology is the study of mental processes (such as thinking tendencies) based on human cognitive functions, including perception, memory, judgment, and decision-making.

[0021] Behavioral psychology is the study of analyzing observable human behavior based on psychological or mental factors, and scientifically elucidating the mechanisms by which behavior arises and changes (e.g., behavioral tendencies).

[0022] Organization terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, and mind coach terminal T5 are personal computers such as desktop PCs and laptop PCs, or mobile devices such as tablets and smartphones.

[0023] The transmission of information or data to the organization terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, or mind coach terminal T5 includes, for example, placing the information or data on the cloud (on a server) so that it can be accessed by the organization terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, or mind coach terminal T5. Furthermore, the transmission of information or data to the information processing device 100 also includes, for example, placing the information or data on the cloud (on a server) so that it can be accessed by the information processing device 100.

[0024] The biological database device 200 stores biological data. This biological data includes the biological data of each member. The mind-related database device 300 stores mind-related data. This mind-related data includes the mind data of each member. The biological database device 200 and the mind-related database device 300 are, for example, database servers.

[0025] The information processing device 100 analyzes the biological data of at least several members belonging to an organization and generates an action plan (hereinafter, "organizational action plan") to solve the organization's problems based on the analysis results. In this way, the accuracy of evaluating the state of members using biological data is improved. As a result, the accuracy of evaluating the state of the organization, which is a collection of multiple members, is also improved. Therefore, an effective organizational action plan can be generated. The information processing device 100 is, for example, a server. The organizational action plan corresponds to an example of the "first action plan" in this disclosure.

[0026] Preferably, the information processing device 100 comprehensively analyzes the biological and mental data of multiple members belonging to the organization and generates an organizational action plan based on the analysis results. Therefore, the accuracy of the state assessment of each member is improved through a comprehensive analysis from both biochemical and psychological / mental aspects, and consequently, the accuracy of the state assessment of the organization to which each member belongs is improved. Thus, a more effective organizational action plan can be generated.

[0027] Next, the information processing device 100 will be described with reference to Figure 2. Figure 2 is a block diagram showing an example configuration of the information processing device 100. As shown in Figure 2, the information processing device 100 includes a processing unit 110, a communication unit 150, and a storage unit 160. The information processing device 100 may also include an input unit 130 and an output unit 140.

[0028] The processing unit 110 performs various processes (various calculations). The processing unit 110 controls the input unit 130, output unit 140, communication unit 150, and storage unit 160. The processing unit 110 includes one or more processors. The processors are CPUs (Central Processing Units), GPUs (Graphics Processing Units), FPGAs (Field Programmable Gate Arrays), DSPs (Digital Signal Processors), or ASICs (Application Specific Integrated Circuits). The processors may be operated by programs or by hardwired logic.

[0029] The input unit 130 is an input device for inputting various types of information to the processing unit 110. For example, the input unit 130 may be a keyboard and pointing device, or a touch panel.

[0030] The output unit 140 outputs various types of information. The output unit 140 includes, for example, a display unit that displays various types of information and an audio output unit that outputs sound. The display unit is, for example, a liquid crystal display, an organic electroluminescent display, or a head-mounted display. The audio output unit is, for example, a speaker.

[0031] The communication unit 150 is connected to a network NW. The communication unit 150 communicates with the organization terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, mind coach terminal T5, bio-related database device 200, and mind-related database device 300 via the network NW. The communication unit 150 is a communication device that performs communication according to a predetermined communication protocol, and includes, for example, a network interface controller. The predetermined communication protocol is, for example, a protocol compliant with Ethernet®, the Internet Protocol Suite, and a protocol compliant with a short-range wireless communication standard.

[0032] The storage unit 160 includes one or more storage devices and stores data and programs. The storage unit 160 includes a main memory such as semiconductor memory and an auxiliary storage device such as semiconductor memory and a storage drive. The storage drive is, for example, a hard disk drive or a solid-state drive. The storage unit 160 is, for example, a non-temporary computer-readable storage medium.

[0033] The processing unit 110 includes an acquisition unit 111, an analysis unit 112, a generation unit 113, and an output control unit 114. Specifically, the processor of the processing unit 110 functions as the acquisition unit 111, analysis unit 112, generation unit 113, and output control unit 114 by executing a program stored in the storage device of the storage unit 160.

[0034] The organizational terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, and mind coach terminal T5 each comprise a processing unit, a communication unit, a storage unit, an input unit, and an output unit. The hardware configurations of the processing unit, communication unit, storage unit, input unit, and output unit of the organizational terminal T1, member terminal T2, physician terminal T3, health coach terminal T4, and mind coach terminal T5 are the same as the hardware configurations of the processing unit 110, communication unit 150, storage unit 160, input unit 130, and output unit 140, respectively.

[0035] Next, we will explain the biological data D1 and mind-related data D2 with reference to Figure 3. Figure 3(a) is a schematic diagram showing the biological data D1. Figure 3(b) is a schematic diagram showing the mind-related data D2.

[0036] As shown in Figure 3(a), the biological database device 200 stores biological data D1 for each member. The biological data D1 includes the member's biological function data D12.

[0037] Biological function data D12 includes biological data D121. Biological data D121 includes biological information extracted from the member's biological sample. The biological sample is, for example, one or more of body fluids, microbiome, DNA, and hair. Body fluids are, for example, one or more of blood, interstitial fluid, saliva, and urine.

[0038] Biological data D121 includes, for example, one or more of the following: blood test information related to nutrients, blood glucose levels, salivary cortisol test information, microbiome test information, urinary organic acid test information, DNA test information, hair test information, and blood test information related to physiological function. Preferably, biological data D121 includes blood test information related to nutrients, blood glucose levels, and salivary cortisol test information. This is because these three pieces of information, in particular, represent the biological state for identifying the root cause of the body's ailments.

[0039] Nutrient-related blood test information includes, for example, information on blood components that directly or indirectly indicate nutrients such as magnesium, zinc, and iron. Blood glucose levels may be expressed as blood glucose levels themselves or as glucose values. Blood glucose levels can provide information such as blood glucose spikes and hypoglycemia. Blood glucose levels may be measured by a sensing device and transmitted to the bio-related database device 200, or transmitted to the bio-related database device 200 via a member's mobile terminal, etc. The sensing device is, for example, a CGM (Continuous Glucose Monitoring) sensor attached to the human body.

[0040] Salivary cortisol test results can estimate, for example, the presence and degree of HPA axis (hypothalamic-pituitary-adrenal axis) dysfunction. This, in turn, can estimate the degree of adrenal fatigue. Microbiome test results can estimate, for example, the state of the gut environment. The state of the gut environment includes, for example, a decrease in beneficial bacteria, indigestion, intestinal inflammation, excessive proliferation of harmful bacteria, and excessive proliferation of Candida. A microbiome test is, for example, a GI-MAP test. Urine organic acid test results can estimate, for example, damage to the body, the state of neurotransmitters, and the TCA cycle. Damage to the body includes, for example, inflammation, oxidative stress, insulin resistance, hypoglycemia, and increased protein catabolism. Neurotransmitters include, for example, serotonin and dopamine. The TCA cycle allows for the evaluation of mitochondrial function. DNA test results can estimate, for example, the state of brain function. Hair analysis results can detect, for example, the accumulation of toxins. Blood test information related to physiological function includes, but is not limited to, CRP, ferritin, and triglycerides.

[0041] Biological data D121 includes scores indicating the primary evaluation of each of several biological information items. The primary evaluation indicates that the individual biological information item is evaluated according to predetermined criteria (thresholds or ranges). In this case, the biological information can be considered as an evaluation item.

[0042] The biological function data preferably includes vital data D122. Vital data D122 is information indicating the vital signs of the members (vital sign information). Vital data D122 may include, for example, the members' sleep information. Sleep information indicates information about the members' sleep. Sleep information may include, for example, a sleep score from a sensing device, melatonin cycle, or caffeine level. Note that vital data D122 is not limited to sleep information and may also include, for example, blood pressure, heart rate information (e.g., heart rate and heart rate variability index).

[0043] The vital data D122 may be measured by a sensing device such as a wearable device and transmitted to the bio-related database device 200, or it may be transmitted to the bio-related database device 200 via a member's mobile terminal or the like.

[0044] Vital data D122 includes a score indicating the primary evaluation of each of multiple vital sign pieces of information. The primary evaluation indicates that the vital sign piece of information is evaluated according to predetermined criteria (thresholds or ranges). In this case, the vital sign piece of information can be considered an evaluation item.

[0045] Furthermore, the biological data D121 and vital data D122 may be transmitted to the biological-related database device 200, for example, directly from the physician terminal T3 or the member terminal T2, or via the information processing device 100.

[0046] The biological data D1 preferably includes member self-assessment data D11. The self-assessment data D11 includes scores for each of several evaluation items used to identify the biochemical causes of symptoms appearing in humans. The scores are based on member input. Biochemistry is the study of life and physiological phenomena from a chemical perspective.

[0047] Furthermore, the biometric data D12 may include information on the number of steps and / or physical activity level (e.g., METs). The information on the number of steps and physical activity level is measured by a sensing device such as a wearable device.

[0048] As shown in Figure 3(b), the mind-related database device 300 stores mind-related data D2 for each member. Mind-related data D2 includes mind data D21.

[0049] Mind data D21 is information based on the members' responses to a questionnaire. In other words, mind data is data based on the members' self-assessments. Specifically, mind data D21 includes information from at least one of the members' thinking tendency data D211 and behavioral tendency data D212. Preferably, mind data D21 includes the members' thinking tendency data D211. More preferably, mind data D21 includes both the members' thinking tendency data D211 and behavioral tendency data D212. Thinking tendency data D211 includes information indicating thinking tendencies based on the members' cognitive functions. Thinking tendencies can also be called thinking characteristics. Behavioral tendency data D212 includes information indicating behavioral tendencies based on the members' psychological or mental factors. Behavioral tendencies can also be called behavioral characteristics. It is preferable that mind data D21 includes thinking tendency data D211, and more preferably that it includes both thinking tendency data D211 and behavioral tendency data D212.

[0050] Mind-related data D2 preferably includes interview data D22. Interview data D22 includes information indicating the results of interviews between the member and the mind coach. For example, interview data D22 is dialogue log data or interpersonal observation data from the interview. Interview data D22 includes scores for each of several evaluation items for evaluating the member's thinking tendencies and / or behavioral tendencies. The scores are based on input from the mind coach. The mind coach is an expert in evaluating human thinking tendencies and / or behavioral tendencies.

[0051] Next, the operation of the information processing device 100 will be explained with reference to Figure 4. Figure 4 is a diagram illustrating an example of the operation of the information processing device 100. As shown in Figure 4, the acquisition unit 111 acquires at least biological-related data D1 from the biological-related database device 200 (Figure 1). The storage unit 160 (Figure 2) stores the biological-related data D1. The biological-related data D1 includes at least biological function data D12. The biological function data D12 includes at least biological data D121.

[0052] The analysis unit 112 evaluates the state of the organization by analyzing the biological data D121 of at least multiple members belonging to the organization. Specifically, the analysis unit 112 evaluates the state of each member by analyzing the biological data D121 of at least the members. Then, the analysis unit 112 evaluates the state of the organization based on the evaluation results of the states of multiple members. For example, the analysis unit 112 evaluates the state of the organization by aggregating and analyzing the evaluation results of the states of multiple members. Aggregation and analysis may include, for example, statistical processing.

[0053] The generation unit 113 generates an organizational action plan to solve organizational problems based on the evaluation results of the organization's state. The output control unit 114 generates image information representing the organizational action plan and displays it on the organizational terminal T1 (Figure 1).

[0054] Thus, in this embodiment, high-resolution biological data D121 is used as an indicator for evaluating the condition of members. Therefore, the accuracy of evaluating the condition of members can be improved. Furthermore, since an organization is a collection of members, high accuracy in evaluating the condition of members improves the accuracy of evaluating the condition of the organization to which the members belong. As a result, by using highly accurate evaluation results, an effective organizational action plan for solving organizational problems can be generated.

[0055] In detail, an organization is a social and physiological system composed of multiple members who interact under common purposes or goals. The function of the organization fluctuates depending on the state and interrelationships of individual members. Therefore, in order to accurately assess the state of the organization, it is preferable to understand the physiological state of its members. However, conventional indicators for evaluating an individual's state, such as those in Patent Document 1, have low information resolution. When the information resolution of indicators for evaluating an individual's state is low, the accuracy of estimating the state of the organization to which the individual belongs also decreases. For example, if biochemical factors (nutritional deficiencies, oxidative stress, hormonal imbalances, etc.) cannot be identified, it becomes impossible to grasp the root causes at the individual level that cause poor performance or inefficient decision-making within the organization. As a result, it may be difficult to accurately identify the true causes of organizational problems and generate effective organizational action plans and intervention strategies. In contrast, in this embodiment, by utilizing biological data D121, which has high resolution as an indicator for evaluating the state of members, the accuracy of evaluating the state of members, and consequently the accuracy of evaluating the state of the organization, can be improved. As a result, it becomes possible to generate effective organizational action plans and intervention strategies.

[0056] For example, the use of biological data D121 makes it possible to identify the root causes of problems experienced by members. Specifically, biological data D121 can identify detailed biochemical factors such as nutrient deficiencies and the accumulation of harmful metabolites and toxic substances in the body. These biochemical factors affect the neuroendocrine and energy metabolic systems of members and can be underlying factors that induce functional impairments such as decreased concentration and energy deficiency. By identifying the biochemical factors that cause member symptoms of distress (e.g., functional impairments) that can lead to problems in the organization, the root causes of the organization's problems can also be identified. As a result, an effective organizational action plan can be generated that includes solutions that address the root causes of the organization's problems.

[0057] In particular, it includes one or more of the following: biological data D121, blood test information related to nutrients, blood glucose levels, salivary cortisol test information, microbiome test information, urinary organic acid test information, DNA test information, hair test information, and blood test information related to physiological function. These factors affect the neuroendocrine system, metabolic system, and cognitive function of members and can be underlying factors that disrupt energy homeostasis and attention control. Therefore, by including this information in vital data D122 and analyzing it, it is possible to generate an effective organizational action plan that includes solutions to the root causes of members' symptoms of malaise (e.g., disruption of energy homeostasis and attention control), and consequently, the root causes of organizational problems.

[0058] Furthermore, the biochemical state of members can be evaluated from multiple perspectives using biological data D121 obtained from different types of bodily fluids (blood, interstitial fluid, saliva, and urine). Moreover, the biochemical state of members can be evaluated even more comprehensively using biological data D121 obtained not only from bodily fluids but also from other biological samples (microbiome, DNA, and hair). In this way, the biochemical state of members can be analyzed and evaluated in detail. Therefore, the root causes of members' health problems can be identified with high accuracy, and consequently, the root causes of organizational issues can be identified with high accuracy. This allows for the generation of more effective organizational action plans that include solutions addressing organizational challenges.

[0059] Furthermore, in the above, the analysis unit 112 may evaluate the state of the tissue by analyzing vital data D122 and / or self-assessment data D11 in addition to the biological data D121 of multiple members.

[0060] Preferably, in this embodiment, the acquisition unit 111 acquires mind-related data D2 from the mind-related database device 300 (Figure 1). The storage unit 160 (Figure 2) stores the mind-related data D2. The mind-related data D2 includes at least mind data D21.

[0061] The analysis unit 112 evaluates the state of the organization by analyzing the biological data D121 and mind data D21 of multiple members. Specifically, the analysis unit 112 evaluates the state of each member by analyzing the biological data D121 and mind data D21 of each member. Then, the analysis unit 112 evaluates the state of the organization based on the evaluation results of the states of multiple members. For example, the analysis unit 112 evaluates the state of the organization by aggregating and analyzing the evaluation results of the states of multiple members. Aggregation and analysis may include, for example, statistical processing.

[0062] Thus, according to this embodiment, both biological data D121, which is biochemical data, and mind data D21, which is based on psychological factors, are analyzed. Therefore, mind data D21 can complement psychological factors that cannot be captured by biological data D121 alone, and biological data D121 can complement the medical validity that cannot be obtained by mind data D21 alone. Through this synergistic effect, the accuracy of the assessment of the state of the organization is further improved, and consequently, a more effective organizational action plan can be generated.

[0063] For example, as mentioned above, the function of an organization fluctuates depending on the state and interrelationships of its individual members. Therefore, in order to evaluate the state of the organization with greater accuracy, it is preferable to comprehensively understand the physiological, psychological, and cognitive states of its members. However, conventional indicators for evaluating an individual's state, such as those described in Patent Document 1, fail to reflect cognitive and psychological aspects, and have limited information resolution for grasping the overall picture of an individual's state. In contrast, in this embodiment, by analyzing not only biological data D121 but also mind data D21, the accuracy of evaluating the state of members can be further improved with even higher information resolution. As a result, the accuracy of evaluating the state of the organization is further improved, making it possible to generate more effective organizational action plans and intervention strategies.

[0064] Specifically, in this embodiment, the analysis unit 112 evaluates the state of the tissue by comprehensively analyzing the biological data D121 and mind data D21 for multiple members.

[0065] As a first example, the analysis unit 112 evaluates the condition of each member by comprehensively analyzing the member's biological data D121 and mind data D21. Then, the analysis unit 112 evaluates the condition of the tissue based on the evaluation results of the conditions of multiple members. Alternatively, as a second example, the analysis unit 112 evaluates the condition of each member by analyzing the member's biological data D121, and also evaluates the condition of each member by analyzing the mind data D21. Then, the analysis unit 112 evaluates the condition of the tissue by comprehensively analyzing the overall evaluation results of multiple members based on the biological data D121 and the overall evaluation results of multiple members based on the mind data D21. In the second example as well, the biological data D121 and mind data D21 are indirectly analyzed comprehensively.

[0066] Thus, in this embodiment, by performing integrated analysis, it becomes possible to make a sophisticated assessment of the organizational state, including the interaction and causal relationships between biological data D121 and mind data D21. Therefore, a more effective organizational action plan can be generated.

[0067] Preferably, the mind data D21 includes scores (points) for each of several evaluation items used to identify the thinking tendencies of the members based on their cognitive functions. In this case, the scores are based on the members' input. The scores make it possible to quantitatively grasp the members' thinking tendencies, enabling a more objective and systematic analysis and evaluation of their thinking tendencies.

[0068] Preferably, the mind data D21 includes scores (points) for each of several evaluation items used to identify the behavioral tendencies of members based on psychological or mental factors. In this case, the scores are based on input from the members. The scores make it possible to quantitatively grasp the behavioral tendencies of members, enabling a more objective and systematic analysis and evaluation of behavioral tendencies.

[0069] Preferably, in this embodiment, the bio-related data D1 includes vital data D122 in addition to biological data D121. The analysis unit 112 then evaluates the state of the tissue by comprehensively analyzing the bio-related data D1 and mind data D21 for multiple members. In this case, detailed biochemical factors can be identified by the biological data D121, while the vital data D122 can capture daily fluctuations and rhythms of biological functions and immediate physical responses. Therefore, the accuracy of the evaluation of the state of the tissue is improved, and consequently, a more effective organizational action plan can be generated.

[0070] In particular, it is preferable that vital data D122 include sleep information. Sleep abnormalities such as sleep deprivation, poor sleep quality, and short sleep duration can be the root cause of discomfort symptoms among members. Therefore, by including sleep information in the analysis of vital data D122, it is possible to generate an effective organizational action plan that includes solutions to the root causes of discomfort symptoms among members, and consequently, to the root causes of organizational issues.

[0071] Preferably, in this embodiment, the biological data D1 includes self-assessment data D11 in addition to biological data D121, or includes self-assessment data D11 in addition to biological data D121 and vital data D122. The analysis unit 112 then evaluates the state of the tissue by comprehensively analyzing the biological data D1 and mind data D21 for multiple members.

[0072] Self-assessment data D11 includes scores for each of several assessment items used to identify the biochemical causes of symptoms appearing in humans. Biological data D121 then includes biological information related to the assessment items in self-assessment data D11. Therefore, comparative analysis of the subjective data, self-assessment data D11, and the objective data, biological data D121, becomes possible. As a result, the accuracy of the assessment of each member's condition is further improved, and consequently, the accuracy of the assessment of the tissue's condition is further improved.

[0073] Preferably, in this embodiment, mind-related data D2 includes interview data D22 in addition to mind data D21. The analysis unit 112 then evaluates the state of the tissue by comprehensively analyzing the biological-related data D1 and mind-related data D2 for multiple members. In this case, biological-related data D1 includes biological data, or includes biological data D121 in addition to self-assessment data D11, or includes biological data D121 in addition to vital data D122 and self-assessment data D11.

[0074] Interview data D22 shows the results of interviews between the mind coach and the member. The mind coach is an expert in evaluating human thinking and / or behavioral tendencies. Specifically, interview data D22 is information about the member's thinking and / or behavioral tendencies obtained from interviews between the mind coach and the member. More specifically, interview data D22 includes the scores for each of several evaluation items used to assess the member's thinking and / or behavioral tendencies. In this way, by including interview data D22 in the analysis in addition to mind data D21, which is based on the member's self-assessment, it becomes possible to identify the member's thinking and / or behavioral tendencies more accurately and in depth.

[0075] For example, a mind coach can extract the true feelings and thought processes of members through face-to-face dialogue, thereby identifying members' thinking tendencies and / or behavioral tendencies that cannot be fully captured by analyzing self-assessed mind data D21 alone, and recording them as interview data D22. Furthermore, a mind coach can ask members questions based on the content of a pre-interview questionnaire regarding their thinking tendencies and / or behavioral tendencies, score their responses, and record them as interview data D22. Therefore, based on the pre-interview questionnaire scores and interview data D22, it is possible to identify, for example, the "difference between the pre-interview questionnaire and the interview content," and extract "biases related to wanting to present oneself favorably."

[0076] As explained above with reference to Figure 4, the analysis unit 112 evaluates the state of the organization by comprehensively analyzing the biological data D1 and mind-related data D2 of multiple members. Then, the generation unit 113 generates an organizational action plan to solve the organization's problems based on the evaluation results of the organization's state. The output control unit 114 generates image information representing the organizational action plan and displays it on the organizational terminal T1 (Figure 1). The organizational action plan may be provided, for example, as a roadmap.

[0077] Figure 5 shows an example of an organizational action plan PL1. In the figure, "Q" represents an integer of 2 or more. As shown in Figure 5, organizational action plan PL1 includes action plan information 700 for each implementation day. The action plan information 700 includes the organization's challenges and content. The content indicates the coaching content (coaching information) for solving the challenges. The coaching content is determined, for example, from a cognitive psychology perspective and / or a behavioral psychology perspective. As an example, a mind coach provides coaching to members responsible for the management or operation of the organization in accordance with each action plan information 700 of organizational action plan PL1. In the example in Figure 5, the action plan information 700 is for implementation in one day, but it may also be for implementation over two or more days.

[0078] As an example, the organizational action plan PL1 includes one or more pieces of information from among coaching information (content) to solve issues related to employee turnover, issues related to employees on leave, issues related to human resources, issues related to organizational culture, and issues related to sales, depending on the results of the assessment of the organization's state. Therefore, it is possible to provide the organization with an action plan for typical and important issues that companies face, such as worsening employee turnover, an increase in employees on leave, a shortage of leadership talent, a passive organizational culture, and / or stagnant sales growth. In particular, it is preferable that the coaching information to solve issues related to employee turnover, employees on leave, human resources, organizational culture, and sales each include coaching information to solve the root causes of worsening employee turnover, an increase in employees on leave, a shortage of leadership talent, a passive organizational culture, and stagnant sales growth, respectively.

[0079] Preferably, the generation unit 113 generates information indicating the basis for the organizational action plan PL1 based on the integrated analysis results of bio-related data D1 and mind-related data D2. The basis for the organizational action plan PL1 includes, for example, the reasons for implementing the organizational action plan PL1 and / or the state of organizational change after implementation. The output control unit 114 then displays the information indicating the basis for the organizational action plan PL1 on the organizational terminal T1. In this way, the persuasiveness and effectiveness of the organizational action plan PL1 can be improved through an explainable approach. Clearly stating the reasons why the organizational action plan PL1 is effective can increase the sense of acceptance among the organization and its members, and promote motivation for behavioral change.

[0080] For example, the content of Action Plan Information 700 may include the basis for coaching shown in Action Plan Information 700 as the basis for Organizational Action Plan PL1.

[0081] Specifically, as an example, the memory unit 160 stores rationale generation information (e.g., a rationale generation table) for generating information that shows the rationale for the organizational action plan. The rationale generation information is information that associates multiple candidate rationale information with multiple organizational state information. For example, one candidate rationale information is associated with one organizational state information. The organizational state information indicates evaluation information of the state of the organization. The candidate rationale information indicates candidate rationales for the organizational action plan. The generation unit 113 obtains candidate rationale information from the rationale generation information that corresponds to the evaluation of the state of the organization based on the integrated analysis results of biological data D1 and mind-related data D2, and associates it with organizational state information. The generation unit 113 then adopts the obtained candidate rationale information as the rationale for the organizational action plan. In this way, the generation unit 113 generates information that shows the rationale for the organizational action plan.

[0082] Preferably, in this embodiment, the analysis unit 112 identifies (estimates) the cause of the organizational problem by analyzing at least mind-related data D2. Mind-related data D2 includes mind data D21, or includes mind data D21 and interview data D22. For example, the analysis unit 112 identifies (estimates) the cause of the organizational problem by analyzing the mind-related data D2 of multiple members. Then, based on the identification result (estimate result) of the cause of the organizational problem (e.g., root cause), the analysis unit 112 estimates error events that may occur in the organization. Directly, it can be estimated that the organizational problem is caused by error events. Error events represent specific problematic behaviors or phenomena that stem from the cause of the organizational problem. Then, the output control unit 114 generates the result of the cause identification and the estimation result of the error events, as well as image information showing the organizational problem (hereinafter, "organizational root cause map MP1"), and displays the image information on the organizational terminal T1. More specifically, for example, the analysis unit 112 performs root cause identification (estimation) and error event estimation for each of the thinking tendency data D211 and behavioral tendency data D212 of the mind data D21.

[0083] Figure 6 shows an example of an organizational root cause map MP1. As shown in Figure 6, the organizational root cause map MP1 is generated based on at least mind-related data D2 and visualizes the structured chain from the root cause 401 of organizational issue 403 to the occurrence of issue 403. Specifically, the organizational root cause map MP1 includes information on root cause 401, error event 402, and organizational issue 403. In Figure 6, the black squares indicate items that correspond to the organization being evaluated. The information included in the organizational root cause map MP1 is an example of the "evaluation results of the state of the organization".

[0084] In this way, by visualizing and providing the organization with the estimated causes of organizational problems (e.g., root causes) and error events, the organization's managers or operators can clearly grasp the structured chain from the root cause to the occurrence of the problem. More preferably, the analysis unit 112 estimates the causes of organizational problems (e.g., root causes) by comprehensively analyzing mind-related data D2 and biological-related data D1. In this case, the causes of organizational problems can be estimated with greater accuracy.

[0085] Specifically, as an example, the memory unit 160 stores cause-error response information (e.g., a cause-error response table) that shows the correspondence between the causes of organizational problems and error events. The cause-error response information is information that associates multiple error event candidates with multiple cause information. For example, one or more error event candidates are associated with one cause information. The cause information indicates the fundamental factors that cause organizational problems. The error event candidates are candidates for error events that can be derived from the cause information. The analysis unit 112 refers to the cause-error response information and retrieves error event candidates associated with the cause information corresponding to the identified cause (estimated cause) from the cause-error response information. Then, the analysis unit 112 adopts the retrieved error event candidates as error events. In this way, the analysis unit 112 estimates error events. An example of identifying (estimating) the cause (root cause) of organizational problems will be described later.

[0086] The output control unit 114 may generate a tissue root cause map MP10 and display the image information on the tissue terminal T1. The tissue root cause map MP10 is generated based on at least the biological data D1 and visualizes the structured chain from the root cause of the tissue problem to the occurrence of the problem. The information included in the tissue root cause map MP10 is an example of the "evaluation results of the tissue state".

[0087] Preferably, the generation unit 113 generates a tissue evaluation report based on the integrated analysis results of the biological-related data D1 and mind-related data D2 of multiple members. The tissue evaluation report is a report showing the evaluation results of the state of the tissue. The tissue evaluation report may include the evaluation results of the state of the tissue based on the analysis results of the biological-related data D1, and / or the evaluation results of the state of the tissue based on the analysis results of the mind-related data D2.

[0088] Figure 7 shows an example of an organizational evaluation report 500. As shown in Figure 7, the organizational evaluation report 500 includes, for example, an organizational health score 501, personnel information 502, improvement rate information 503, and return on investment information 504.

[0089] The Organizational Health Score 501 is a composite score obtained by aggregating the scores of biometric data D1 and mind-related data D2 for each member, and then aggregating these results across multiple members of the organization. Therefore, the Organizational Health Score 501 is an indicator that represents the state of the organization, reflecting the physical health status and psychological performance of multiple members. Psychological performance indicates the degree of a member's state from a psychological or mental perspective.

[0090] The personnel information 502 indicates the number of members participating in the organizational support provided by the information processing device 100 (hereinafter referred to as "this support"). The improvement rate information 503 is a value expressed as a percentage of the increase in the organizational health score at the time of evaluation compared to the organizational health score at the start of this support. The investment effectiveness information 504 includes a value indicating the economic return on investment in this support in monetary terms, and the return on investment (ROI) which indicates the economic return on investment in this support. For example, if the turnover rate is reduced by this support, the economic return is the amount equivalent to the recruitment costs that would have otherwise been incurred due to employee turnover. Also, for example, if productivity is improved by this support, the economic return is the increase in sales due to the improved productivity.

[0091] Furthermore, for example, the organizational assessment report 500 includes a health distribution chart 505 and departmental health scores 506.

[0092] Health Distribution Chart 505 shows the distribution of individual health scores among members of an organization. The individual health score is a composite score obtained by aggregating the scores of biometric data D1 and mind-related data D2. Therefore, the individual health score is an indicator representing the individual member's state, reflecting their physical health status and psychological performance.

[0093] The Departmental Health Score 506 is a score obtained by aggregating individual health scores for each department. Therefore, the Departmental Health Score 506 is an indicator that represents the state of a department, reflecting the physical health status and psychological performance of its members.

[0094] The above is merely an example, and the contents of the organizational evaluation report 500 are not particularly limited. Furthermore, the organizational health score 501, personnel information 502, improvement rate information 503, return on investment information 504, health distribution chart 505, and departmental health score 506 included in the organizational evaluation report 500 are examples of "evaluation results of the state of the organization."

[0095] Returning to Figure 4, a detailed example of integrated analysis and organizational action plan generation will be explained. The analysis unit 112 calculates an integrated score for each member based on the scores of the evaluation items for each member's biological data D1 and the evaluation items for each member's mind-related data D2. The analysis unit 112 then aggregates the integrated scores for multiple members and evaluates the state of the organization based on the aggregated results. In this case, the analysis unit 112 may output the aggregated result of the integrated scores for multiple members as the evaluation result of the state of the organization.

[0096] In this way, by calculating and aggregating the combined scores of multiple members, it is possible to perform an objective and highly reproducible "assessment of the organizational state" based on quantitative analysis.

[0097] Specifically, the analysis unit 112 calculates an integrated score by integrating the scores of the evaluation items of the biological data D1 and the evaluation items of the mind-related data D2 using a predetermined function (hereinafter referred to as "predetermined function PF"). The predetermined function PF consists of, for example, addition, multiplication, subtraction, and division, or a combination of two or more of these. For example, the predetermined function PF may be a statistical model (mathematical model) or a learning model. The learning model is a learning model that has been trained to output an evaluation result of the state of tissue when the scores of the evaluation items of the biological data D1 and the evaluation items of the mind-related data D2 are input. The learning model may be, for example, an artificial intelligence model as described later. Furthermore, when integrating, the scores may be weighted between the evaluation items of the biological data D1 and the evaluation items of the mind-related data D2.

[0098] In this case, the combination of evaluation items to be integrated from among the multiple evaluation items of biological data D1 and the multiple evaluation items of mind-related data D2 (hereinafter sometimes referred to as "heterogeneous item combinations") is predetermined by one or more of the following, for example, a physician, a mind coach, and a health coach, according to the purpose of the integrated evaluation, and is stored in the memory unit 160. An example of a heterogeneous item combination is a combination of evaluation items such as "deficient nutrients" and "magnesium" from biological data D1 (biological data D121) and evaluation items such as "thinking patterns" from mind-related data D2 (mind data D21).

[0099] A heterogeneous item combination is a combination of one or more evaluation items from biological data D1 and one or more evaluation items from mind-related data D2. A heterogeneous item combination is not limited to a combination of one evaluation item from biological data D1 and one evaluation item from mind-related data D2. Furthermore, the purpose of integrated evaluation is to identify (estimate) various root causes that give rise to organizational problems such as worsening employee turnover or an increase in employees on leave, and there are multiple such root causes. For this reason, heterogeneous item combinations are predetermined for each purpose of integrated evaluation. Moreover, even for the same problem, the root causes may differ depending on the characteristics and circumstances of the organization. For this reason, multiple heterogeneous item combinations are defined even for the same problem (e.g., worsening employee turnover). In other words, since multiple different root causes may exist for a single organizational problem, multiple heterogeneous item combinations are defined to correspond to each of the multiple root causes.

[0100] For example, if the aggregated score of a combination of heterogeneous items for multiple members exceeds a certain value, the root cause corresponding to that combination of heterogeneous items is identified (estimated) as the root cause of the problem in the organization being evaluated.

[0101] Specifically, the analysis unit 112 aggregates the integrated scores of multiple members for each purpose of integrated evaluation (for each combination of heterogeneous items). For example, if the organization's problem is a worsening employee turnover rate, the analysis unit 112 aggregates the integrated scores of multiple members for each of the multiple combinations of heterogeneous items corresponding to each of the multiple root causes of that problem. Then, based on the aggregated results of the integrated scores for each of the multiple combinations of heterogeneous items corresponding to each of the multiple root causes, the analysis unit 112 identifies (estimates) one or more root causes that are causing the problem of the organization being evaluated from among the multiple root causes. For example, if there are multiple possible root causes for a certain problem, the root cause corresponding to the combination of heterogeneous items whose aggregated integrated score (aggregate score) is above a certain value is identified as the root cause of the problem of the organization being evaluated.

[0102] The aggregated score and identified root causes are examples of the results of an assessment of the organization's condition.

[0103] The memory unit 160 stores multiple action plan candidates for each of the multiple issues that may arise in the organization. In this case, the action plan candidates are prepared for each root cause. The generation unit 113 then, for example, selects an action plan candidate from among the multiple action plan candidates that corresponds to the organization's issue and root cause. Alternatively, the generation unit 113 also selects an action plan candidate from among the multiple action plan candidates that corresponds to the aggregated result of the integrated score (aggregate score). In this way, the generation unit 113 selects an action plan candidate that corresponds to the evaluation result of the organization's state. The generation unit 113 then sets the selected action plan candidates as the organizational action plan.

[0104] Figure 8 shows an example of a group of candidate action plans PL10 for generating an organizational action plan. In the figure, "Q" represents an integer of 2 or more. The group of candidate action plans PL10 is stored in the memory unit 160. As shown in Figure 8, the group of candidate action plans PL10 includes candidate action plan information 700a for each implementation day. The candidate action plan information 700a includes the organization's issues and content. The content indicates the coaching content (coaching information) for solving the issues. The coaching content is, for example, determined from a cognitive psychology perspective and / or a behavioral psychology perspective.

[0105] For example, the generation unit 113 acquires multiple action plan candidate information 700a from the action plan candidate group PL10 according to the evaluation result of the organization's state. Then, the generation unit 113 sets each of the acquired multiple action plan candidate information 700a into multiple action plan information 700 (Figure 5), thereby generating an organizational action plan PL1 (Figure 5) that includes multiple action plan information 700.

[0106] Referring to Figure 4, another example of the process for generating an organizational action plan will be described. The analysis unit 112 evaluates the state of the organization by comprehensively analyzing the biological data D1 and mind-related data D2 for multiple members.

[0107] In this case, for example, multiple organizational evaluation candidates are pre-stored in the memory unit 160 as candidates for evaluating the state of the organization. Therefore, the analysis unit 112 selects an organizational evaluation candidate from among the multiple organizational evaluation candidates according to the results of the integrated analysis, and sets the selected organizational evaluation candidate as the evaluation result of the state of the organization. In this case, the "results of the integrated analysis" are, for example, the "aggregate results of the integrated score (aggregate score)" mentioned above. The analysis unit 112 may also select two or more organizational evaluation candidates, for example, by assigning them a priority (recommended order). In this case, the analysis unit 112 transmits the integrated analysis results and information on two or more organizational evaluation candidates assigned a priority (recommended order) to one or more of the mind coach terminal T5, physician terminal T3, and health coach terminal T4 via the communication unit 150. Then, via one or more of the mind coach terminal T5, physician terminal T3, and health coach terminal T4, the selected organizational evaluation candidate from among multiple organizational evaluation candidates, based on the judgment of one or more of the mind coach, physician, and health coach, is set as the organizational status evaluation result and transmitted to the analysis unit 112 of the information processing device 100. In this case, the fact that the analysis unit 112 has acquired information on the organizational status evaluation result corresponds to the analysis unit 112's evaluation of the organizational status.

[0108] One or more of the mind coach, physician, and health coach may modify the organizational evaluation candidate selected by the analysis unit 112 or the mind coach via the mind coach terminal T5 or the like. In this case, the modified organizational evaluation candidate will be set as the evaluation result of the organizational state.

[0109] Furthermore, the generation unit 113 generates an organizational action plan based on the evaluation results of the organization's state, according to the first action plan selection information (for example, the first action plan selection table). The first action plan selection information is information for selecting an organizational action plan according to the evaluation results of the organization's state. The first action plan selection information is stored in the storage unit 160. Specifically, the first action plan selection information includes information that associates multiple organizational state information with multiple action plan candidates. Organizational state information is information that can be used as the evaluation content of the organization's state. The generation unit 113 refers to the first action plan selection information to obtain action plan candidates associated with organizational state information corresponding to the evaluation results of the organization's state, and adopts the obtained action plan candidates as the organizational action plan. Note that the "aggregate results of the integrated score (aggregate score)" described above may be used as the "evaluation results of the organization's state".

[0110] In this way, by utilizing the first action plan selection information, which systematically associates organizational status information with candidate action plans, it becomes possible to generate an objective and consistent organizational action plan through a simple process.

[0111] The generation unit 113 may select two or more action plan candidates, assigning them a priority order (recommended order). In this case, the generation unit 113 may transmit information of the two or more action plan candidates, assigned a priority order (recommended order), to the organization terminal T1 via the communication unit 150. Then, a member responsible for the operation or management of the organization may select an action plan candidate appropriate to the organization from among the two or more action plan candidates via the organization terminal T1 and set the selected action plan candidate as the organization action plan. Information on the organization action plan is transmitted from the organization terminal T1 to the generation unit 113 of the information processing device 100. In this case, the generation unit 113 acquiring the organization action plan from the organization terminal T1 corresponds to the generation of the organization action plan by the generation unit 113.

[0112] As another example, an organizational action plan may be generated as follows: The generation unit 113 may transmit the evaluation results of the organization's state and information on multiple action plan candidates to the mind coach terminal T5 via the communication unit 150. The mind coach then, via the mind coach terminal T5, selects an action plan candidate that is effective for solving the organization's problems from among the multiple action plan candidates based on the evaluation results of the organization's state, and sets the selected action plan candidate as the organizational action plan. The mind coach terminal T5 then transmits the information of the organizational action plan to the generation unit 113 of the information processing device 100. In this case, the generation unit 113 obtaining the organizational action plan from the mind coach terminal T5 corresponds to the generation of the organizational action plan by the generation unit 113. The generation unit 113 may also transmit the evaluation results of the organization's state and information on multiple action plan candidates to one or more of the mind coach terminal T5, physician terminal T3, and health coach terminal T4 via the communication unit 150. In this case, via one or more of the mind coach terminal T5, physician terminal T3, and health coach terminal T4, an action plan candidate selected from among multiple action plan candidates according to the judgment of one or more of the mind coach, physician, and health coach is set as the organizational action plan and transmitted to the generation unit 113 of the information processing device 100.

[0113] In these examples, the "aggregate result of the integrated score (aggregate score)" described above may be used as the "evaluation result of the state of the organization." Alternatively, two or more action plan candidates may be selected from among multiple action plan candidates by assigning a priority (recommendation ranking) based on the judgment of one or more of the mind coach, physician, and health coach via one or more of the mind coach terminal T5, physician terminal T3, and health coach terminal T4. In this case, information on the two or more action plan candidates with assigned priority (recommendation ranking) is transmitted to the generation unit 113 of the information processing device 100. The generation unit 113 then transmits information on the two or more action plan candidates with assigned priority (recommendation ranking) to the organization terminal T1 via the communication unit 150. A member responsible for the operation or management of the organization may then select an action plan candidate appropriate to the organization from among the two or more action plan candidates via the organization terminal T1 and set the selected action plan candidate as the organizational action plan. Information on the organizational action plan is transmitted from the organization terminal T1 to the generation unit 113 of the information processing device 100. In this case, the generation unit 113 acquiring the organizational action plan from the organizational terminal T1 corresponds to the generation of the organizational action plan by the generation unit 113.

[0114] Continuing with reference to Figure 4, preferably in this embodiment, the analysis unit 112 evaluates the state of each member by comprehensively analyzing the bio-related data D1 and the mind-related data D2. Then, the generation unit 113 generates an action plan (hereinafter referred to as the "individual action plan") for each member to solve the member's problems based on the evaluation results of the member's state. The individual action plan may be provided, for example, as a roadmap. The contents of the bio-related data D1 and the mind-related data D2 are as described with reference to Figure 3. The individual action plan corresponds to an example of the "second action plan" in this disclosure.

[0115] Thus, both biological data D1, which includes biochemical data D121, and mind-related data D2, which includes mind data D21 based on psychological factors, are analyzed. Therefore, mind data D21 complements psychological factors that cannot be captured by biological data D121 alone, and biological data D121 complements the medical validity that cannot be obtained by mind data D21 alone. This synergistic effect improves the accuracy of the assessment of the members' conditions, making it possible to generate personalized and effective individual action plans for each member.

[0116] In particular, providing not only organizational action plans at the organizational level but also individual action plans at the individual level for each member allows for support to be provided to both the organization and its members. As a result, it is possible to contribute to solving organizational challenges by improving the health and performance of individual members.

[0117] Figure 9 shows an example of an individual action plan PL2. In the figure, "q" represents an integer of 2 or more. As shown in Figure 9, the individual action plan PL2 includes action plan information 710 for each day of implementation. The action plan information 710 includes the member's challenges and content. The content 712 shows the coaching content (coaching information) to solve the challenges 711. The coaching content may be, for example, health-related content determined from a biochemical perspective. As an example, coaching is provided to the member by a doctor and / or a health coach in accordance with each action plan information 710 of the individual action plan PL2. In the example in Figure 9, the action plan information 710 is content to be implemented in one day, but it may also be content to be implemented over two or more days.

[0118] For example, the Individual Action Plan PL2 includes one or more pieces of coaching information (content) to address nutritional issues and coaching information (content) to address lifestyle issues, depending on the assessment results of the individual's condition. In this way, by providing optimal coaching information from the perspectives of nutrition and lifestyle based on the individual's assessment results, effective interventions that address the unique challenges of each member become possible.

[0119] Preferably, in this embodiment, the analysis unit 112 identifies (estimates) the cause (root cause) of the member's problem (discomfort symptoms) by analyzing at least the bio-related data D1. The contents of the bio-related data D1 are as shown in Figure 3. Then, based on the identification result (estimated result) of the cause (root cause) of the member's problem, the analysis unit 112 estimates the physiological damage that may occur in the member. Furthermore, based on the estimated result of the physiological damage, the analysis unit 112 estimates the internal systems related to the physiological damage. Directly, it can be estimated that the internal systems related to the physiological damage are causing the member's problem (discomfort symptoms). Internal systems represent the biological elements that form the basis for maintaining biological functions. Then, the output control unit 114 generates the identification result of the cause (root cause), the estimation result of the physiological damage, the estimation result of the internal systems, and image information (hereinafter, "personal root cause map MP2") showing the member's problem (discomfort symptoms), and displays the image information on the member terminal T2.

[0120] Figure 10 shows an example of an Individual Root Cause Map MP2. As shown in Figure 10, the Individual Root Cause Map MP2 visualizes the structured chain from the root cause 411 of a member's issue 414 (discomfort symptoms) to the member's issue 414 (discomfort symptoms). Specifically, the Individual Root Cause Map MP2 includes information on the root cause 411, physiological damage 412, internal systems 413, and issue 414 (discomfort symptoms). In Figure 10, the black squares indicate items that apply to the member being evaluated. The information included in the Individual Root Cause Map MP2 is an example of the "evaluation results of an individual's condition."

[0121] In this way, by visualizing and providing members with the root causes of their problems (symptoms), physiological damage, and estimated results of their internal systems, members can clearly understand the structured chain from the root cause to their problems (symptoms). More preferably, the analysis unit 112 estimates the root causes of the members' problems by comprehensively analyzing mind-related data D2 and biological-related data D1. In this case, the root causes of the members' problems can be estimated with greater accuracy.

[0122] Specifically, as an example, the memory unit 160 stores cause-and-damage correspondence information (e.g., a cause-and-damage correspondence table) that shows the correspondence between the cause of a member's problem and physiological damage. The cause-and-damage correspondence information is information that associates multiple physiological damage candidates with multiple cause information. For example, one or more physiological damage candidates are associated with one cause information. The cause information indicates the fundamental factors that cause the member's problem. Physiological damage candidates are candidates for physiological damage that may arise from the cause information. The analysis unit 112 refers to the cause-and-damage correspondence information and retrieves physiological damage candidates associated with the cause information corresponding to the identified cause (estimated cause) from the cause-and-damage correspondence information. Then, the analysis unit 112 adopts the retrieved physiological damage candidates as physiological damage. In this way, the analysis unit 112 estimates the physiological damage. An example of identifying (estimating) the cause (root cause) of a member's problem will be described later.

[0123] As an example, the memory unit 160 stores injury-internal system correspondence information (for example, an injury-internal system correspondence table) that shows the correspondence between a member's physiological injury and internal systems. The injury-internal system correspondence information is information that associates multiple internal system candidates with multiple physiological injury information. The analysis unit 112 then refers to the injury-internal system correspondence information and obtains internal system candidates associated with the physiological injury information corresponding to the estimated physiological injury from the injury-internal system correspondence information, and adopts them as internal systems. In this way, the analysis unit 112 estimates the internal systems.

[0124] Here, the analysis unit 112 can evaluate the state of the organization by aggregating the information from the individual root cause maps MP2 of multiple members. The results of this evaluation of the state of the organization can be included, for example, in the organization evaluation report 500.

[0125] Preferably, the output control unit 114 generates an individual root cause map MP20 and displays the image information on the organizational terminal T1. The individual root cause map MP20 is generated based on at least mind-related data D2 and is information that visualizes the structured chain from the root cause of a member's problem to the occurrence of the problem. The configuration of the individual root cause map MP20 is similar to, for example, the configuration of the organizational root cause map MP1 in Figure 6. Therefore, the individual root cause map MP20 includes, for example, information on the root cause of a member's problem, error events, and the member's problem (symptoms of discomfort). This information is estimated (generated) in the same way as in the case of the organizational root cause map MP1. The information included in the individual root cause map MP20 is an example of an "evaluation result of an individual's state".

[0126] For example, the analysis unit 112 identifies (estimates) the cause of a member's problem (discomfort symptoms) by analyzing at least the mind-related data D2. The mind-related data D2 includes either mind data D21 or mind data D21 and interview data D22. The analysis unit 112 then estimates possible error events that may occur to the member based on the identification result (estimated result) of the cause of the member's problem (e.g., root cause). Directly, it can be estimated that the member's problem (discomfort symptoms) is caused by an error event. An error event represents a specific problematic behavior or phenomenon that stems from the cause of the member's problem. The output control unit 114 then generates an individual root cause map MP20 (the result of identifying the cause, the estimated result of the error event, and image information showing the member's problem) and displays it on the member terminal T2. For example, a root cause might be low self-efficacy or a low comfort zone. For example, error events include phrases like, "I want to do it, but I don't think I'm good enough," or "But, but..." Examples of problems (symptoms of dysfunction) include "I lack motivation" or "I can't take action." More specifically, for example, the analysis unit 112 performs root cause identification (estimation) and error event estimation for each of the thinking tendency data D211 and behavioral tendency data D212 of the mind data D21.

[0127] Preferably, the generation unit 113 generates a personal evaluation report based on the integrated analysis results of the member's biological data D1 and mind-related data D2. The personal evaluation report is a report showing the evaluation results of the member's condition. The personal evaluation report may include the evaluation results of the individual's condition based on the analysis results of the biological data D1, and / or the evaluation results of the individual's condition based on the analysis results of the mind-related data D2.

[0128] Figure 11 shows an example of an individual assessment report 800. As shown in Figure 11, the individual assessment report 800 includes, for example, blood test information 801, biomedical function analysis information 802, mind analysis information 803, wellness analysis information 804, and performance analysis information 805.

[0129] Blood test information 801 is information showing the results of blood tests of the members. Biological function analysis information 802 is information showing biological functions such as nutritional status and intestinal inflammation status. Biological function analysis information 802 is information obtained by aggregating the scores of biological data D121 and based on the aggregated results. Mind analysis information 803 is information showing the thinking tendencies of the members, such as thinking patterns and the balance between the right and left hemispheres of the brain. Mind analysis information 803 may also include information showing the behavioral tendencies of the members, such as the type of behavioral tendency. Mind analysis information 803 is information obtained by aggregating the scores of mind data D21 and based on the aggregated results.

[0130] Wellness analysis information 804 includes indicators showing the physical health status of members and indicators showing the psychological health status of members. The indicators showing physical health status are based on the aggregated results of the scores of members' biological data D121. The indicators showing psychological health status are based on the aggregated results of the scores of members' mind data D21.

[0131] Performance analysis information 805 includes productivity indicators that show the productivity of members, and creativity indicators that show the creativity of members. Each of the productivity indicators and creativity indicators is a composite score obtained by aggregating the scores of members' biological data D121 and mind data D21.

[0132] Please note that the above is merely an example, and the content of the individual evaluation report 800 is not particularly limited.

[0133] Returning to Figure 4, a detailed example of integrated analysis and individual action plan generation will be explained. The analysis unit 112 calculates an integrated score based on the scores of the evaluation items for the member's bio-related data D1 and the evaluation items for the member's mind-related data D2. The analysis unit 112 then evaluates the member's condition based on the integrated score. In this case, the analysis unit 112 may output the member's integrated score as the evaluation result of the member's condition.

[0134] In this way, by calculating an integrated score for each member, it is possible to perform an objective and highly reproducible "assessment of the member's condition" based on quantitative analysis.

[0135] Specifically, the analysis unit 112 calculates an integrated score by integrating the scores of the evaluation items of the biological data D1 and the evaluation items of the mind-related data D2 using a predetermined function PF. The predetermined function PF consists of, for example, addition, multiplication, subtraction, and division, or a combination of two or more of these. For example, the predetermined function PF may be a statistical model (mathematical model) or a learning model. The learning model is a learning model that has been trained to output an evaluation result of an individual's state when the scores of the evaluation items of the biological data D1 and the evaluation items of the mind-related data D2 are input. The learning model may be, for example, an artificial intelligence model as described later. Furthermore, when integrating, the scores may be weighted between the evaluation items of the biological data D1 and the evaluation items of the mind-related data D2.

[0136] In this case, the combination of evaluation items to be integrated from among the multiple evaluation items of biological data D1 and the multiple evaluation items of mind-related data D2 (hereinafter sometimes referred to as "heterogeneous item combinations") is predetermined by, for example, at least by a physician, according to the purpose of the integrated evaluation, and is stored in the memory unit 160. A mind coach and / or health coach may also participate in determining heterogeneous item combinations. A heterogeneous item combination is, for example, a combination of evaluation items of biological data D1 (biological data D121) such as "deficient nutrients" or "magnesium" and evaluation items of mind-related data D2 (mind data D21) such as "thinking patterns".

[0137] A heterogeneous item combination is a combination of one or more evaluation items from biological data D1 and one or more evaluation items from mind-related data D2. A heterogeneous item combination is not limited to a combination of one evaluation item from biological data D1 and one evaluation item from mind-related data D2. Furthermore, the purpose of integrated evaluation is to identify (estimate) various root causes that give rise to member issues such as decreased concentration or chronic fatigue, and there are multiple such causes. For this reason, heterogeneous item combinations are predetermined for each purpose of integrated evaluation. Moreover, even for the same issue, the root cause may differ depending on the characteristics and circumstances of the member. For this reason, multiple heterogeneous item combinations are defined even for the same issue (e.g., decreased concentration). In other words, since multiple different root causes may exist for a single issue of a member, multiple heterogeneous item combinations are defined to correspond to each of the multiple root causes.

[0138] For example, if the combined score of a certain combination of heterogeneous items for a member exceeds a certain value, the root cause corresponding to that combination of heterogeneous items is identified (estimated) as the root cause of the member's problem being evaluated.

[0139] Specifically, the analysis unit 112 calculates an integrated score for each member for each purpose of integrated evaluation (for each combination of heterogeneous items). For example, if a member's problem is a decrease in concentration, the analysis unit 112 calculates an integrated score for each of the multiple combinations of heterogeneous items corresponding to the multiple root causes of that problem. Then, based on the integrated score of each of the multiple combinations of heterogeneous items corresponding to the multiple root causes, the analysis unit 112 identifies (estimates) one or more root causes that are causing the problem of the member being evaluated. For example, if there are multiple possible root causes for a certain problem, the root cause corresponding to the combination of heterogeneous items with an integrated score of a certain value or higher is identified as the root cause of the problem of the member being evaluated.

[0140] The integrated score and identified root causes are examples of the results of the assessment of the members' condition.

[0141] The memory unit 160 stores multiple action plan candidates for each issue that a member may encounter. In this case, the action plan candidates are prepared for each root cause. The generation unit 113 then, for example, selects an action plan candidate from among the multiple action plan candidates that corresponds to the member's issue and root cause. Alternatively, for example, the generation unit 113 selects an action plan candidate from among the multiple action plan candidates that corresponds to the integrated score. In this way, the generation unit 113 selects an action plan candidate that corresponds to the evaluation result of the member's condition. The generation unit 113 then sets the selected action plan candidates as individual action plans.

[0142] Figure 12 shows an example of a group of candidate action plans PL20 for generating individual action plans. In the figure, "q" represents an integer of 2 or more. The group of candidate action plans PL20 is stored in the memory unit 160. As shown in Figure 12, the group of candidate action plans PL20 includes candidate action plan information 710a for each implementation day. The candidate action plan information 710a includes the member's challenges and content. The content indicates the coaching content (coaching information) for solving the challenges. The coaching content is, for example, content determined from a biochemical perspective.

[0143] For example, the generation unit 113 acquires multiple action plan candidate information 710a from the action plan candidate group PL20 according to the evaluation results of the members' status. The generation unit 113 then sets each of the acquired multiple action plan candidate information 710a into multiple action plan information 710 (Figure 9), thereby generating an individual action plan PL2 (Figure 9) that includes multiple action plan information 710.

[0144] Referring to Figure 4, another example of the process for generating an individual action plan will be explained. The analysis unit 112 evaluates the individual's state by comprehensively analyzing the member's bio-related data D1 and mind-related data D2.

[0145] In this case, for example, multiple individual evaluation candidates are pre-stored in the memory unit 160 as candidates for evaluating the status of the members. Therefore, the analysis unit 112 selects an individual evaluation candidate from among the multiple individual evaluation candidates according to the result of the integrated analysis, and sets the selected individual evaluation candidate as the evaluation result of the individual's status. In this case, the "result of the integrated analysis" is, for example, the "integrated score" mentioned above. The analysis unit 112 may also select two or more individual evaluation candidates, for example, by assigning them a priority (recommended order). In this case, the analysis unit 112 transmits the integrated analysis result and information on two or more individual evaluation candidates assigned a priority (recommended order) to at least the physician terminal T3 via the communication unit 150. Then, via the physician terminal T3, the individual evaluation candidate selected by the physician from among the multiple individual evaluation candidates according to the physician's diagnosis is set as the evaluation result of the individual's status and transmitted to the analysis unit 112 of the information processing device 100. In this case, the fact that the analysis unit 112 has obtained information on the evaluation result of the individual's status corresponds to the analysis unit 112's evaluation of the individual's status.

[0146] The physician may modify the analysis unit 112 or the individual evaluation candidate selected by the physician via the physician terminal T3. In this case, the modified individual evaluation candidate will be set as the evaluation result for the individual's condition.

[0147] As described above, the analysis unit 112 may evaluate the individual's condition based on the results of the integrated analysis and the input information entered via the physician's terminal T3. By utilizing the physician's input information, reliable evaluation results can be obtained while ensuring legal and medical legitimacy.

[0148] Furthermore, when assessing an individual's condition, a health coach may participate via the health coach terminal T4.

[0149] Furthermore, the generation unit 113 generates individual action plans based on the evaluation results of the members' states, according to the second action plan selection information (for example, the second action plan selection table). The second action plan selection information is information for selecting an individual action plan that corresponds to the evaluation results of an individual's state. The second action plan selection information is stored in the storage unit 160. Specifically, the second action plan selection information includes information that associates multiple individual state information with multiple action plan candidates. The individual state information is information that can be used as the evaluation content of an individual's state. The generation unit 113 refers to the second action plan selection information to obtain action plan candidates associated with individual state information corresponding to the evaluation results of an individual's state, and adopts the obtained action plan candidates as individual action plans. Note that the "integrated score" mentioned above may be used as the "evaluation results of an individual's state."

[0150] In this way, by using the second action plan selection information, which systematically associates personal status information with candidate action plans, it becomes possible to generate an objective and consistent personal action plan through a simple process. Furthermore, the physician may modify the personal action plan generated by the analysis unit 112 via the physician terminal T3. In this case, the modified personal action plan will be set as the final personal action plan.

[0151] The generation unit 113 may select two or more action plan candidates, assigning them a priority order (recommended order). In this case, the generation unit 113 may transmit information of the two or more action plan candidates, assigned a priority order (recommended order), to the member terminal T2 via the communication unit 150. The member may then select an action plan candidate that suits them from among the two or more candidates via the member terminal T2 and set the selected action plan candidate as their personal action plan. Information on the personal action plan is transmitted from the member terminal T2 to the generation unit 113 of the information processing device 100. In this case, the generation unit 113 acquiring the personal action plan from the member terminal T2 corresponds to the generation of the personal action plan by the generation unit 113.

[0152] As another example, an individual action plan may be generated as follows: The generation unit 113 may transmit the evaluation results of the individual's condition and information on multiple action plan candidates to at least the physician terminal T3 via the communication unit 150. The physician then, via the physician terminal T3, selects an action plan candidate that is effective for solving the individual's problem from among the multiple action plan candidates based on the evaluation results of the individual's condition, and sets the selected action plan candidate as the individual action plan. The physician terminal T3 then transmits the information of the individual action plan to the generation unit 113 of the information processing device 100. In this case, the generation unit 113 acquiring the individual action plan from the physician terminal T3 corresponds to the generation of the individual action plan by the generation unit 113. The physician may also modify the selected action plan candidate via the physician terminal T3 and set the modified action plan candidate as the individual action plan.

[0153] In these examples, the "integrated score" described above may be used as the "evaluation result of the individual's condition." Alternatively, at least two or more action plan candidates may be selected from among multiple action plan candidates, with priority (recommendation ranking) assigned according to the physician's judgment, via the physician terminal T3. In this case, information on the two or more action plan candidates with priority (recommendation ranking) is transmitted to the generation unit 113 of the information processing device 100. The generation unit 113 then transmits information on the two or more action plan candidates with priority (recommendation ranking) to the member terminal T2 via the communication unit 150. The member may then select an action plan candidate that suits them from among the two or more action plan candidates via the member terminal T2 and set the selected action plan candidate as their personal action plan. Information on the personal action plan is transmitted from the member terminal T2 to the generation unit 113 of the information processing device 100. In this case, the generation unit 113 acquiring the personal action plan from the member terminal T2 corresponds to the generation of the personal action plan by the generation unit 113.

[0154] Furthermore, when generating an individual action plan, a health coach may be involved via the health coach terminal T4.

[0155] As described above, the generation unit 113 may generate an individual action plan based on the evaluation results of the member's condition and the input information entered via the physician terminal T3. In this case, the input information includes information related to the individual action plan (physician input information).

[0156] By utilizing information entered by physicians, it becomes possible to provide more reliable and safer individual action plans by combining the analysis results of biometric data D12 with the physician's clinical knowledge and diagnosis. As a result, effective support can be achieved while ensuring legal and medical legitimacy.

[0157] Next, we will explain a detailed example of bio-related data D1 with reference to Figures 13 to 16.

[0158] Figure 13 is a schematic diagram showing the self-assessment data D11 of the biological data D1. As shown in Figure 13, the self-assessment data D11 includes biochemical categories 71, assessment items 72, question items 73, response information 74, and assessment score 75. Biochemical categories 71 include, for example, nutrient deficiencies, damage to the body, adrenal glands, thyroid gland, mitochondrial function, neurotransmitters, improvement of the gut environment, toxins, and brain function. Note that biochemical categories 71 can also be considered as "assessment items." The assessment score 75 is an example of a "score."

[0159] Evaluation item 72 indicates the evaluation targets included in biochemical category 71. For example, if biochemical category 71 is "nutrient deficiencies," then evaluation item 72 would be magnesium, zinc, and iron.

[0160] For each evaluation item 72, multiple question items 73 are assigned to the members. Each question item 73 contains questions for evaluating the target of evaluation indicated by the evaluation item 72. The response information 74 contains the answers to the questions in question item 73. The response information 74 is information indicating "yes" or "no" to the questions in question item 73. The evaluation score 75 indicates a score corresponding to the member's response information 74 to the questions in question item 73. For example, if the response information 74 is "yes", the evaluation score 75 is set to "1", and if the response information 74 is "no", the evaluation score 75 is set to "0". The evaluation score 75 is, for example, binary information.

[0161] The information processing device 100 then transmits the self-assessment data D11, for which the response information 74 and evaluation score 75 have not yet been set, as a questionnaire to the member terminal T2. The member then sets the response information 74 in the questionnaire via the member terminal T2. The information processing device 100 (analysis unit 112) then sets the score corresponding to the response information 74 as the evaluation score 75. The information processing device 100 then stores the self-assessment data D11 in the bio-related database device 200, associating it with the member's identification information.

[0162] Figure 14 is a schematic diagram showing the biological data D121 of the biological-related data D1. As shown in Figure 14, the biological data D121 includes a biochemical category 91, evaluation items 92, and multiple test information 93. The biochemical category 91 corresponds to the biochemical category 71 of the self-assessment data D11 (Figure 13). The evaluation items 92 correspond to the evaluation items 72 of the self-assessment data D11 (Figure 13) and indicate the evaluation targets included in the biochemical category 91. Note that the biochemical category 91 can also be considered as "evaluation items".

[0163] The multiple test information 93 includes, for example, two or more pieces of information from among blood tests (nutrients), hair tests, blood glucose tests, salivary cortisol tests, microbiome tests, urine organic acid tests, and DNA tests (methylation). The multiple test information 93 may also include, for example, information about sleep tests. Furthermore, one piece of test information 93 may be assigned to one evaluation item 92, or two or more pieces of test information 93 may be assigned to one evaluation item 92.

[0164] Each piece of test information 93 includes biometric test information 931 extracted from a member's biological sample and an evaluation score 932. The evaluation score 932 is an example of a "score". Biometric test information 931 may be, for example, blood test information, hair test information, blood glucose test information, salivary cortisol test information, microbiome test information, urine organic acid test information, or DNA test information. Also, one piece of test information 93 may be assigned to one evaluation item 92, or two or more pieces of test information 93 may be assigned. The evaluation score 932 indicates a score corresponding to the biometric test information 931.

[0165] Furthermore, vital data D122 is constructed in the same way as biological data D121.

[0166] Figure 15 is a schematic diagram showing the analysis logic information LG1 for biological data D121. As shown in Figure 15, the analysis logic information LG1 includes a biochemical category 201, evaluation items 202, and multiple judgment criterion information 203. The biochemical category 201 corresponds to the biochemical category 91 (Figure 14) of biological data D121. The evaluation items 202 correspond to the evaluation items 92 of biological data D121 and indicate the evaluation targets included in the biochemical category 201. The judgment criterion information 203 indicates the judgment criteria for determining the evaluation score 932 (Figure 14) for the biological test information 931 (Figure 14). For example, the judgment criterion information 203 defines judgment conditions for the test values ​​shown by the biological test information 931, and assigns points depending on whether the judgment conditions are met or not. For example, judgment criterion information 203 specifies that if the test value is above the threshold, the evaluation score 932 should be set to "1", and if the test value is below the threshold, the evaluation score 932 should be set to "0". The evaluation score 932 is, for example, binary information.

[0167] The analysis unit 112 refers to the judgment criteria information 203 and sets an evaluation score 932 (Figure 14) for the biological examination information 931 (Figure 14).

[0168] Referring to Figures 13 to 15, the analysis unit 112 calculates a total score (hereinafter referred to as "total score TS") by adding the sum of evaluation scores 75 for evaluation item 72 in self-assessment data D11 (Figure 13) and the sum of evaluation scores 932 for evaluation item 92 in biological data D121 (Figure 14). As a result, a total score TS is calculated for each evaluation item 72 (evaluation item 92) from self-assessment data D11 to biological data D121. For example, the total score TS for evaluation item 72 (evaluation item 92) "Magnesium" is calculated by adding the sum of evaluation scores 75 for evaluation item 72 "Magnesium" and the sum of evaluation scores 932 for evaluation item 92 "Magnesium," which has the same content as evaluation item 72. Note that the sum of the total scores TS is not limited to a simple sum, but is not particularly limited to weighted sums, etc.

[0169] Then, the analysis unit 112 sets evaluation information for evaluation item 72 (evaluation item 92) according to the total score TS, based on the analysis logic information LG2. The total score TS is an example of a "score".

[0170] Figure 16 is a schematic diagram showing the analysis logic information LG2 for self-assessment data D11 and biological data D121. As shown in Figure 16, the analysis logic information LG2 includes biochemical categories 211, evaluation items 212, judgment criteria information 213, and evaluation information 214. Biochemical categories 211 correspond to biochemical categories 71 and 91 (Figures 13 and 14). Evaluation items 212 correspond to evaluation items 72 and 92 and indicate the evaluation targets included in biochemical categories 211. Judgment criteria information 213 indicates the judgment criteria for determining the evaluation information for the total score TS. In the example in Figure 16, the judgment criteria information 213 includes a range of scores for the total score TS. Then, in the analysis logic information LG2, evaluation information 214 for the total score TS is associated stepwise with each range.

[0171] The analysis unit 112 sets evaluation information 214 for evaluation item 72 (evaluation item 92) based on the analysis logic information LG2, corresponding to the total score TS. The total score TS and evaluation information 214 are examples of evaluation results for an individual's condition.

[0172] Furthermore, the analysis unit 112 can evaluate the condition of the members from a biochemical perspective by analyzing the members' biological data D121 and self-assessment data D11. Therefore, the validity of the subjective self-assessment data D11 can be verified by the objective biological data D121.

[0173] Next, we will explain a detailed example of mind-related data D2 by referring to Figures 17 to 21.

[0174] Figure 17 is a schematic diagram showing an example of the thinking tendency data D211 of the mind-related data D2. As shown in Figure 17, the thinking tendency data D211 includes multiple section data A1 to AN. N is an integer of 2 or greater. Section data A1 to AN are sometimes collectively referred to as section data An. n is an integer of 1 or greater.

[0175] Section data An includes multiple segment data 10. Segment data 10 includes question items 11, response information 12, and evaluation scores 13. The evaluation score 13 is an example of a "score". Question item 11 includes question content to identify the thinking tendencies based on the cognitive function of the members. In other words, question item 11 is an evaluation item to identify the thinking tendencies based on the cognitive function of the members. Response information 12 includes multiple answer candidates for the question in question item 11. The answer candidate selected by the member from the multiple answer candidates is set as the answer information 12. In the example in Figure 17, the black rectangles indicate the answer candidates selected by the members. Response information 12 shows the member's answer to the question in question item 11. The evaluation score 13 shows the score set for the evaluation item (question item) according to the answer information 12.

[0176] For example, section data A1-A7 each include 11 questions relating to thinking patterns (how one tends to perceive and think about things), right-brain and left-brain balance (whether information processing style is logical or intuitive), risk and reward (in what situations one is likely to take action), future / present / past (where one's decision-making timeline lies), reward system (what motivates one), modal channels (which sense of information one is best at inputting—auditory, visual, emotional / kinesthetic), and prefrontal cortex function (levels of planning, self-control, and creativity). The titles of these section data A1-A7 can also be considered "evaluation items." Motivation, for example, refers to the driving force that prompts action.

[0177] The information processing device 100 then transmits the thinking tendency data D211, for which the response information 12 and evaluation score 13 have not yet been set, as a questionnaire to the member terminal T2. The member then sets the response information 12 on the questionnaire via the member terminal T2. The information processing device 100 (analysis unit 112) then sets the score corresponding to the response information 12 as the evaluation score 13. The information processing device 100 stores the thinking tendency data D211 in the mind-related database device 300, associating it with the member's identification information.

[0178] Figure 18 shows the analysis logic table TB1 for the thinking tendency data D211. As shown in Figure 18, the analysis logic table TB1 is stored in the storage unit 160. The analysis logic table TB1 includes analysis logic information B1 to BN, which are assigned to each of the section data A1 to AN. N is an integer of 2 or more. Sometimes the analysis logic information B1 to BN are collectively referred to as analysis logic information Bn. n is an integer of 1 or more.

[0179] The analysis logic information Bn includes cognitive psychology indicators 21, section information 22, judgment criterion information 23, and type information 24.

[0180] Cognitive psychology index 21 indicates categories of thinking tendencies identified by section data An, which corresponds to the analytical logic information Bn. For example, cognitive psychology index 21 corresponds to thinking patterns, information processing styles, behavioral types, thinking time axes, sources of motivation, dominant sensory channels, and self-growth potential, respectively, for each of the section data A1 to AN.

[0181] Section information 22 is information for identifying section data An corresponding to the analysis logic information Bn. Judgment criterion information 23 includes range information defined for the total score 14 (Figure 17), which is the sum of the evaluation scores 13 for each section data An. Type information 24 includes information on the type of thinking tendency corresponding to each range defined in judgment criterion information 23. Type information 24 may be, for example, analytical / risk management type, balanced type, or creative / goal-oriented type.

[0182] The analysis unit 112 calculates a total score 14 for each section data An, summing the evaluation scores 13. Then, the analysis unit 112 refers to the judgment criterion information 23 of the analysis logic information Bn corresponding to the section data An to identify the range to which the total score 14 belongs. Furthermore, the analysis unit 112 refers to the type information 24 to identify the type (the type of thinking tendency of the member) corresponding to the range to which the total score 14 belongs. Note that the total score 14 and the type information 24 are examples of evaluation results of an individual's state.

[0183] Figure 19 is a schematic diagram showing an example of behavioral tendency data D212 for mind data D21. As shown in Figure 19, behavioral tendency data D212 includes multiple section data C1 to CK. K is an integer greater than or equal to 2. Section data C1 to CK are sometimes collectively referred to as section data Ck. k is an integer greater than or equal to 1.

[0184] Section data Ck includes multiple segment data 30. Segment data 30 includes a question item 31, response information 32, and evaluation score 33. Question item 31 includes question content to identify behavioral tendencies based on psychological or mental factors of the members. In other words, question item 31 is an evaluation item to identify behavioral tendencies based on psychological or mental factors of the members. Response information 32 includes multiple answer candidates for the question in question item 31. The answer candidate selected by the member from the multiple answer candidates is set as the response information 32. Response information 32 shows the member's answer to the question in question item 31. Evaluation score 33 shows the score set for the evaluation item (question item) according to the response information 32. Evaluation score 33 is an example of a "score".

[0185] Let's take the case where K=6 as an example. For instance, section data C1 to C6 each contain 31 questions related to the "Analyzer" (analysis, organization, planning), "Believer" (trust, mission, ethics), "Empathizer" (warmth, relationships, security), "Innovator" (creativity, play, freedom), "Driver" (challenge, action, results), and "Reflector" (insight, serenity, vision) in the CRM (Cognitive Response Model) model.

[0186] The CRM model is a diagnostic model that focuses on "which cognitive pathways a person prioritizes when under stress or making decisions." In the CRM model, "Analyzer," "Believer," "Empathizer," "Innovator," "Driver," and "Reflector" represent personality types. Each type is identified by information processing style, motivation, interpersonal tendencies, and stress response.

[0187] The information processing device 100 has previously sent the behavioral tendency data D212, for which the response information 32 and evaluation score 33 have not yet been set, as a questionnaire to the member terminal T2. The member then sets the response information 32 on the questionnaire via the member terminal T2. The information processing device 100 (analysis unit 112) then sets the score corresponding to the response information 32 as the evaluation score 33. In the example in Figure 19, the black rectangles represent the responses of the members. The information processing device 100 stores the behavioral tendency data D212 in the mind-related database device 300, associating it with the member's identification information.

[0188] Next, with reference to Figure 20, the analysis logic information EX and analysis logic table TB2 for the behavioral trend data D212 will be explained. The analysis logic information EX and analysis logic table TB2 are stored in the storage unit 160.

[0189] Figure 20(a) is a schematic diagram showing the analysis logic information EX. As shown in Figure 20(a), the analysis logic information EX includes rule information for determining the type of behavioral tendency of the members. The rule information ranks the total score 34 (Figure 19), which is the sum of the evaluation scores 33 for each section data Ck, and identifies the type of behavioral tendency according to the rank. For example, the personality type corresponding to the section data Ck with the largest total score 34 (e.g., "Analyzer") is identified as the basic type of behavioral tendency of the member (e.g., Base). Alternatively, for example, the personality type corresponding to the section data Ck with the second largest total score 34 (e.g., "Driver") may be identified as a secondary type of behavioral tendency of the member (e.g., Phase).

[0190] The analysis unit 112 calculates a total score 34 for each section data Ck, summing the evaluation scores 33, and ranks them. Then, the analysis unit 112 refers to the analysis logic information EX and identifies the type of behavioral tendency of the members according to their ranking in the total score 34. Note that the total score 34 and the type of behavioral tendency are examples of evaluation results for an individual's state.

[0191] Figure 20(b) is a schematic diagram showing the analysis logic table TB2. As shown in Figure 20(b), the analysis logic table TB2 contains multiple analysis logic information F1 to FK. K is an integer greater than or equal to 2. Sometimes, the analysis logic information F1 to FK are collectively referred to as analysis logic information Fk. k is an integer greater than or equal to 1.

[0192] In the analysis logic information Fk, type information 41, which indicates the personality type corresponding to the section data Ck, and attribute information 42 of the personality type indicated by type information 41 are associated. The attribute information includes, for example, the characteristics of the personality type (e.g., psychological needs) and recommended responses (e.g., communication tips).

[0193] The analysis unit 112 refers to the analysis logic information Fk corresponding to the type of behavioral tendency (personality type) of the member identified based on the analysis logic information EX, from among the analysis logic information F1 to FK, and identifies and acquires attribute information 42 associated with the type of behavioral tendency of the member. Note that attribute information 42 is an example of the evaluation result of the individual's state.

[0194] Figure 21 is a schematic diagram showing an example of interview data D22. As shown in Figure 21, interview data D22 includes first interview information D221 and second interview information D222.

[0195] The first interview information D221 includes question items 51, record information 52, reference score 53, evaluation criteria information 54, and evaluation score 55. The evaluation score 55 is an example of a "score".

[0196] Question Item 51 includes questions designed to identify the thinking and / or behavioral tendencies of the members. In other words, Question Item 51 is an evaluation item designed to identify the thinking and / or behavioral tendencies of the members. Question Item 51 may include questions such as "experience challenging high goals."

[0197] Record information 52 includes a record of the member's responses to question item 51 during the interview with the mind coach and the results of observations. The baseline score 53 indicates the score awarded when the member's response meets the criteria shown in the evaluation criteria information 54. The evaluation criteria information 54 indicates the criteria for evaluating the member's thinking tendencies and / or behavioral tendencies based on the record information 52 for question item 51. The baseline score 53 and evaluation criteria information 54 are multiple-choice. The evaluation score 55 indicates the score actually awarded to the evaluation item based on the member's response results, with reference to the baseline score 53 and evaluation criteria information 54.

[0198] During interviews conducted via the Mind Coach terminal T5 and the member terminal T2, the Mind Coach asks the member questions from question item 51 and records the recorded information 52. The Mind Coach then sets an evaluation score 55 based on the member's responses and observations, referring to the reference score 53 and evaluation criteria information 54.

[0199] The information processing device 100 may also transmit the first interview information D221 as a questionnaire to the member terminal T2 in advance. The member then sets an evaluation score 55 as the result of answering question item 51 in the questionnaire via the member terminal T2. In this case, the mind coach can grasp the difference between the member's prior evaluation score 55 (response result) and the mind coach's evaluation score 55 for the same question item 51 during the interview.

[0200] The second interview information D222 includes observation items 56, record information 57, reference score 58, evaluation criteria information 59, and evaluation score 60. Evaluation score 60 is an example of a "score".

[0201] Observation item 56 indicates the matters to be observed in an interview to identify the thinking tendencies and / or behavioral tendencies of the members. In other words, observation item 56 is an evaluation item for identifying the thinking tendencies and / or behavioral tendencies of the members. Observation item 56 is, for example, an observation of "goal setting and motivation to achieve." Record information 57 includes a record of the members' observations regarding observation item 56 during the interview conducted by the mind coach.

[0202] The baseline score 58 indicates the score awarded when the member's observations meet the criteria shown in the evaluation criteria information 59. The evaluation criteria information 59 provides criteria for evaluating the member's thinking tendencies and / or behavioral tendencies based on the observation items 56. The baseline score 58 and the evaluation criteria information 59 are multiple-choice options. The evaluation score 60 indicates the score actually awarded to the evaluation items based on the member's observations, with reference to the baseline score 58 and the evaluation criteria information 59.

[0203] The mind coach observes the member based on observation items 56 during the interview via the mind coach terminal T5 and the member terminal T2. The mind coach then sets an evaluation score 60 based on the member's observation results, referring to the reference score 58 and evaluation criteria information 59.

[0204] The interview data D22 is transmitted from the mind coach terminal T5 to the mind-related database device 300.

[0205] Furthermore, the analysis unit 112 can evaluate the member's state from a psychological or mental perspective by analyzing the member's mind data D21 and interview data D22. Specifically, the analysis unit 112 identifies at least one of the member's thinking tendencies and behavioral tendencies by analyzing the member's mind data D21 and interview data D22. Interview data D22 is information about at least one of the member's thinking tendencies and behavioral tendencies obtained from interviews between the mind coach and the member. Therefore, by including interview data D22 in the analysis in addition to mind data D21 based on the member's self-assessment, it becomes possible to identify the member's thinking tendencies and / or behavioral tendencies more accurately and in depth.

[0206] Next, the processing flow of the information processing device 100 will be explained with reference to Figures 2, 22, and 23.

[0207] Figure 22 is a flowchart showing an example of an information processing method for an organization according to this embodiment. The information processing method is performed by the information processing device 100 shown in Figure 2.

[0208] As shown in Figure 22, the information processing method includes steps S1 to S13. The program stored in the storage unit 160 of the information processing device 100 causes the processing unit 110 to execute steps S1 to S13. In other words, the program product realizes steps S1 to S13 when the program is executed by the processing unit 110. The processing unit 110 corresponds to an example of a "computer" in this disclosure.

[0209] First, in step S1, the acquisition unit 111 acquires the member's biological data D1 from the biological database device 200.

[0210] Next, in step S2, the analysis unit 112 analyzes the biological data D1 and evaluates the member's condition from a biochemical perspective based on the analysis results. In this case, for example, various information of the member included in the individual root cause map MP2 may be output as an evaluation of the member's condition.

[0211] Next, in step S3, the acquisition unit 111 acquires the member's mind-related data D2 from the mind-related database device 300.

[0212] Next, in step S4, the analysis unit 112 analyzes the mind-related data D2 and evaluates the state of the members from a psychological perspective based on the analysis results.

[0213] Next, in step S5, the analysis unit 112 comprehensively analyzes the evaluation results of the member's state based on the analysis results of the biological data D1 and the evaluation results of the member's state based on the analysis results of the mind-related data D2 to obtain an integrated analysis result. Even in this case, the biological data D1 and the mind-related data D2 are indirectly analyzed comprehensively.

[0214] Next, in step S6, the analysis unit 112 determines whether the processing in steps S1 to S5 has been completed for all members to be supported.

[0215] If a negative result is obtained in step S6 (NO), the process proceeds to step S1, and processing for another member is executed.

[0216] On the other hand, if a positive result is obtained in step S6 (YES), the process proceeds to step S7.

[0217] Next, in step S7, the analysis unit 112 aggregates and analyzes the integrated analysis results (step S5) for all members, and evaluates the state of the organization based on the aggregated and analyzed results.

[0218] Next, in step S8, the generation unit 113 obtains an action plan candidate associated with the evaluation result of the organization's state from the group of action plan candidates for the organization, and sets the action plan candidate as the organizational action plan. The storage unit 160 stores the organizational action plan.

[0219] Next, in step S9, the output control unit 114 displays the organizational action plan on the organizational terminal T1.

[0220] Next, in step S10, the generation unit 113 generates organizational root cause maps MP1 and MP10 based on the aggregation of integrated analysis results for all members and the results of that analysis (step S7). The generation unit 113 may also generate organizational root cause map MP1 based on the analysis results of mind-related data D2 for all members. The generation unit 113 may also generate organizational root cause map MP10 based on the analysis results of biological-related data D1 for all members. The storage unit 160 stores organizational root cause maps MP1 and MP10.

[0221] Next, in step S11, the output control unit 114 displays the organizational root cause map on the organizational terminal T1.

[0222] Next, in step S12, the generation unit 113 generates an organizational evaluation report based on the analysis results from step S2 for all members, the analysis results from step S4 for all members, and the aggregated results and analysis of the integrated analysis results from step S7. The storage unit 160 stores the organizational evaluation report.

[0223] Next, in step S13, the output control unit 114 displays the organizational evaluation report on the organizational terminal T1. Then, the information processing method is completed.

[0224] Note that the order of steps S1 and S2 and steps S3 and S4 can be reversed. Steps S10, S11, and steps S12 and S13 can be executed at any time after step S7.

[0225] Furthermore, for example, step S5 may be omitted. In this case, in step S7, the analysis unit 112 aggregates and analyzes the evaluation results of the state of the tissue (a collection of multiple members) obtained from the analysis results in step S2 for all members and the evaluation results of the state of the tissue (a collection of multiple members) obtained from the analysis results in step S4 for all members, and finally evaluates the state of the tissue based on the aggregated and analyzed results. Even in this case, the biological data D1 and mind-related data D2 are indirectly analyzed in an integrated manner.

[0226] Furthermore, steps S3 to S5 are omitted when generating an organizational action plan based on biological data D1, which includes at least biological data D121. Then, in step S7, the analysis unit 112 aggregates the analysis results of the biological data D1 for all members and evaluates the state of the organization based on the results of that analysis.

[0227] For example, one or more of the information from the organizational root cause maps MP1 and MP10, the information from the organizational evaluation report 500, the bio-related data D1, and the mind-related data D2 are scored according to a predetermined scoring rule, and a score (e.g., points) is assigned to each evaluation item. Therefore, for example, the analysis unit 112 may evaluate the state of the organization based on two or more combinations of these (for example, information related to bio-related data D1 and information related to mind-related data D2). For example, the combination is a combination of the information from the organizational root cause map MP1 and the evaluation result of the state of the organization obtained from the analysis results of the individual root cause maps MP2 of multiple members that make up the organization. Furthermore, the various information input to the artificial intelligence model described later is, for example, the score of the evaluation item for each piece of information.

[0228] Figure 23 is a flowchart showing an example of an information processing method for members according to this embodiment. The information processing method is executed by the information processing device 100 shown in Figure 2.

[0229] As shown in Figure 23, the information processing method includes steps S21 to S31. The program stored in the storage unit 160 of the information processing device 100 causes the processing unit 110 to execute steps S21 to S31. In other words, the program product realizes steps S21 to S31 when the program is executed by the processing unit 110. The processing unit 110 corresponds to an example of a "computer" in this disclosure.

[0230] First, steps S21 to S24 are executed. The processes in steps S21 to S24 are the same as the processes in steps S1 to S4 in Figure 22.

[0231] Next, in step S25, the analysis unit 112 comprehensively analyzes the evaluation results of the member's state based on the analysis results of the biological data D1 and the evaluation results of the member's state based on the analysis results of the mind-related data D2, and evaluates the member's state based on the analysis results. Even in this case, the biological data D1 and the mind-related data D2 are indirectly analyzed comprehensively.

[0232] Next, in step S26, the generation unit 113 obtains an action plan candidate associated with the evaluation result of the member's status (step S25) from the group of action plan candidates for the member, and sets the action plan candidate as the individual action plan. The storage unit 160 stores the individual action plan.

[0233] Next, in step S27, the output control unit 114 displays the individual action plan on the member terminal T2.

[0234] Next, in step S28, the generation unit 113 generates individual root cause maps MP2 and MP20 based on the integrated analysis results (step S25). The generation unit 113 may also generate individual root cause map MP2 based on the analysis results of step S22. The generation unit 113 may also generate individual root cause map MP20 based on the analysis results of step S24. The storage unit 160 stores the individual root cause maps MP2 and MP20. Furthermore, for example, in step S25, the analysis unit 112 may evaluate the status of the members based on the information contained in the individual root cause maps MP2 and MP20.

[0235] Next, in step S29, the output control unit 114 displays the individual root cause maps MP2 and MP20 on the member terminal T2.

[0236] Next, in step S30, the generation unit 113 generates an individual evaluation report 800 based on the analysis results of step S22, step S24, and step S25. The storage unit 160 stores the individual evaluation report 800.

[0237] Next, in step S31, the output control unit 114 displays the individual evaluation report 800 on the member terminal T2. Then, the information processing method is completed.

[0238] Note that the order of steps S21 and S22 and steps S23 and S24 may be reversed. Steps S28, S29, and steps S30 and S31 can be executed at any time after step S25.

[0239] For example, one or more of the information from the individual root cause maps MP2 and MP20, the information from the individual evaluation report 800, the bio-related data D1, and the mind-related data D2 are scored according to a predetermined scoring rule, and a score (e.g., points) is assigned to each evaluation item. Therefore, for example, the analysis unit 112 may evaluate the state of the organization based on two or more combinations of these (for example, information related to bio-related data D1 and information related to mind-related data D2). For example, the combination is the combination of information from the individual root cause map MP2 and information from the individual root cause map MP20. In addition, the various information input to the artificial intelligence model described later is, for example, the score of the evaluation item for each piece of information.

[0240] (Examples of using artificial intelligence models such as generative AI (Artificial Intelligence)) An artificial intelligence model is, for example, a generative artificial intelligence model or other machine learning model. An artificial intelligence model is a program. A generative artificial intelligence model is, for example, a language model. A language model is, for example, a generative AI such as a Large Language Model (LLM) or a Vision Language Model (VLM). For example, an artificial intelligence model may generate information in response to a prompt using only a language model such as an LLM. A prompt is text information containing instructions for the artificial intelligence model. For example, an artificial intelligence model may generate information in response to a prompt by referencing information from an external database using Retrieval Augmented Generation (RAG) technology. For example, an artificial intelligence model may generate information in response to a prompt by referencing information from an external system in cooperation with an external system. Thus, the implementation form of an artificial intelligence model is not particularly limited. Also, for example, a machine learning model is a learning model that learns the correspondence between first information and second information, and outputs second information when first information is input. In this case, machine learning algorithms include, for example, linear regression, naive Bayes, support vector machines, neural networks, deep neural networks, decision trees, random forests, gradient boosting, or regularized regression. The artificial intelligence model may also be, for example, a pre-trained model that has undergone fine-tuning.

[0241] The artificial intelligence model may be stored in the memory unit 160, or it may be stored in an external system (e.g., a server). The artificial intelligence model may also be stored in a memory device within the information processing system 1 or the support system SYS.

[0242] The processing unit 110 may input biometric data D1 and mind-related data D2 of multiple members belonging to an organization into an artificial intelligence model to generate (output) an organizational action plan to solve the organization's problems. The artificial intelligence model has learned the relationship between the biometric data D1 and mind-related data D2 of multiple members belonging to an organization and the organizational action plan, and generates (outputs) an organizational action plan according to the input biometric data D1 and mind-related data D2. In other words, the artificial intelligence model is trained to output an organizational action plan when biometric data D1 and mind-related data D2 of multiple members belonging to an organization are input. In effect, the artificial intelligence model is trained to comprehensively analyze the biometric data D1 and mind-related data D2 of multiple members belonging to an organization, evaluate the state of the organization based on the analysis results, and generate an organizational action plan based on the evaluation results. The learning includes fine tuning. The learning targets are past biometric data D1, past mind-related data D2, and past organizational action plans. Furthermore, the learning target may be historical data of the organization from which the AI ​​model is to generate an organizational action plan, or it may be historical data of a different organization. In addition, the learning process may involve, for example, learning biometric data D1, mind-related data D2, and the organizational action plan of multiple members, with one organization as the unit.

[0243] For example, the processing unit 110 inputs prompts and reference data (context data) to a generative artificial intelligence model to generate an organizational action plan. The reference data includes, for example, biometric data D1 and mind-related data D2 of multiple members belonging to the organization, and information on their analysis rules (analysis logic). The analysis rules are information that indicates the rules or logic for integratively analyzing the biometric data D1 and mind-related data D2. The prompts include, for example, instructions to "integratively analyze the reference data according to the analysis rules, evaluate the state of the organization based on the analysis results, and generate an organizational action plan to solve the organization's problems based on the evaluation results."

[0244] For example, it is preferable that the artificial intelligence model learns (e.g., through self-learning, continuous learning) various information included in the organizational evaluation report 500 (e.g., organizational health score 501, number of people information 502, improvement rate information 503, return on investment information 504, health distribution map 505, and departmental health score 506, etc.) along with biometric data D1, mind-related data D2, and organizational action plans of multiple members of the organization. For example, the artificial intelligence model may learn the amount of change, improvement rate, and / or achievement level of indicators representing the state of the organization before and after the implementation of the organizational action plan, along with biometric data D1, mind-related data D2, and organizational action plans of multiple members of the organization. Specifically, as an example, the artificial intelligence model may use information such as the amount of change, improvement rate, and / or achievement level of indicators representing the state of the organization before and after the implementation of the organizational action plan as training data or reward information, and retrain or fine-tune based on this (e.g., supervised learning, reinforcement learning, etc.). However, the learning algorithm is not particularly limited. In this way, by incorporating the results of implementing the generated organizational action plan into the learning process, it becomes possible to generate a more effective organizational action plan that continuously reflects the organization's actual situation and improvement effects.

[0245] For example, the evaluation model and the action plan generation model may be provided as separate artificial intelligence models. For example, the evaluation model takes biometric data D1 and mind-related data D2 of multiple members belonging to an organization as input and outputs an evaluation result representing the state of the organization. The evaluation model learns the relationship between the biometric data D1 and mind-related data D2 of multiple members belonging to an organization and the evaluation result of the state of the organization, and generates (outputs) an evaluation result of the state of the organization according to the input biometric data D1 and mind-related data D2. For example, the evaluation result of the state of the organization output by the evaluation model may be an organizational evaluation report 500 or an organizational root cause map MP1. For example, the action plan generation model takes the evaluation result output by the evaluation model as input and generates or outputs an organizational action plan to solve the organization's problems. The action plan generation model learns the relationship between the evaluation result of the state of the organization and the organizational action plan, and generates (outputs) an organizational action plan according to the input evaluation result of the state of the organization. In this way, by executing the evaluation process and the action plan generation process with different artificial intelligence models, a learning method or model configuration suitable for each process can be adopted.

[0246] The processing unit 110 may input the member's bio-related data D1 and mind-related data D2 into the artificial intelligence model and generate (output) an individual action plan to solve the member's problem. The artificial intelligence model has learned the relationship between the member's bio-related data D1 and mind-related data D2 and the individual action plan, and generates (outputs) an individual action plan according to the input bio-related data D1 and mind-related data D2. In other words, the artificial intelligence model is trained to output an individual action plan when the member's bio-related data D1 and mind-related data D2 are input. In effect, the artificial intelligence model is trained to comprehensively analyze the member's bio-related data D1 and mind-related data D2, evaluate the individual's state based on the analysis results, and generate an individual action plan based on the evaluation results. The learning includes fine-tuning. The learning targets are past bio-related data D1, past mind-related data D2, and past individual action plans.

[0247] For example, the processing unit 110 inputs prompts and reference data (context data) to a generative artificial intelligence model to generate an individual action plan. The reference data includes, for example, the member's bio-related data D1 and mind-related data D2, and information on their analysis rules (analysis logic). The analysis rules are information that indicates the rules or logic for comprehensively analyzing the bio-related data D1 and mind-related data D2. The prompts include, for example, instructions to "comprehensively analyze the reference data according to the analysis rules, evaluate the member's state based on the analysis results, and generate an individual action plan to solve the member's problems based on the evaluation results."

[0248] For example, it is preferable that the artificial intelligence model learns (e.g., through self-learning, continuous learning) various information included in the individual evaluation report 800 (e.g., blood test information 801, biofunction analysis information 802, mind analysis information 803, wellness analysis information 804, and performance analysis information 805, etc.) along with the member's bio-related data D1, mind-related data D2, and individual action plan. For example, the artificial intelligence model may learn the amount of change, improvement rate, and / or achievement level of indicators representing the member's state before and after the implementation of the individual action plan, along with the member's bio-related data D1, mind-related data D2, and individual action plan. Specifically, as an example, the artificial intelligence model may use information such as the amount of change, improvement rate, and / or achievement level of indicators representing the member's state before and after the implementation of the individual action plan as training data or reward information, and retrain or fine-tune based on this (e.g., supervised learning, reinforcement learning, etc.). However, the learning algorithm is not particularly limited. In this way, by incorporating the results of implementing the generated individual action plans into the learning process, it becomes possible to generate more effective individual action plans that continuously reflect the actual situation of the members and the effects of improvement.

[0249] For example, the evaluation model and the action plan generation model may be provided as separate artificial intelligence models. For example, the evaluation model takes biometric data D1 and mind-related data D2 of the members as input and outputs an evaluation result representing the member's state. The evaluation model learns the relationship between the biometric data D1 and mind-related data D2 of the members and the evaluation result of the member's state, and generates (outputs) an evaluation result of the member's state according to the input biometric data D1 and mind-related data D2. For example, the evaluation result of the member's state output by the evaluation model may be an individual evaluation report 800 or an organizational root cause map MP2, MP20. For example, the action plan generation model takes the evaluation result output by the evaluation model as input and generates or outputs an individual action plan to solve the member's problems. The action plan generation model learns the relationship between the evaluation result of the member's state and the individual action plan, and generates (outputs) an individual action plan according to the input individual's evaluation result of the state. In this way, by executing the evaluation process and the action plan generation process with different artificial intelligence models, a learning method or model configuration suitable for each process can be adopted.

[0250] Furthermore, even in cases like the one described above, the processing unit 110 effectively functions as the analysis unit 112 and the generation unit 112 by having the artificial intelligence model perform the analysis and generation.

[0251] The information processing device 100 may generate organizational action plans using, for example, generative AI such as a Large Language Model (LLM) or a Vision Language Model (VLM). These are just other examples. In this case as well, the information processing device 100 (processing unit 110) essentially functions as an analysis unit 112 and a generation unit 112 by having the LLM perform analysis and generation. The following explanation will use the LLM as an example.

[0252] For example, the information processing device 100 inputs prompts and reference data (context data) to the LLM to generate an organizational action plan.

[0253] In this case, as a first example, the reference data includes bio-related data D1 for each member, analysis logic information for analyzing bio-related data D1 (e.g., analysis logic information LG1, LG2), aggregate analysis rules for the analysis results of bio-related data D1 of multiple members, a group of candidate action plans including candidate organizational action plans (e.g., group of candidate action plans PL10), and action plan selection rules (e.g., first action plan selection information). The action plan selection rules are information that associates the analysis results of bio-related data D1 based on the aggregate analysis rules with the optimal action plan candidate when such analysis results are obtained. The analysis results include, for example, the evaluation results of the state of the organization based on the scoring results (including aggregate results) of bio-related data D1, and / or the scoring results (including aggregate results) of bio-related data D1. Furthermore, bio-related data D1 includes at least biological data D121. Furthermore, the prompt includes (1) analyzing the bio-related data D1 for each member according to the analysis logic information, (2) aggregating and analyzing the analysis results for all members according to the aggregation analysis rules, (3) referring to the action plan selection rules and selecting action plan candidates associated with the analysis results obtained according to the aggregation analysis rules as the organizational action plan, (4) obtaining the specific content of the selected organizational action plan from the group of action plan candidates, and (5) generating image information of the organizational action plan according to a predetermined format.

[0254] For example, the analysis unit 112 may execute the processing corresponding to prompts (1) to (3) or (1) to (4), and the generation unit 113 may input the execution result (analysis result) to the LLM, causing the LLM to execute the processing corresponding to prompts (4), (5), or (5). Alternatively, for example, the analysis unit 112 may execute the processing corresponding to prompts (1) and (2), and the generation unit 113 may input the execution result (analysis result) to the LLM, causing the LLM to execute the processing corresponding to prompts (3) to (5).

[0255] As a second example, the reference data includes biometric data D1 for each member, analysis logic information for analyzing biometric data D1, mind-related data D2 for each member, analysis logic information for analyzing mind-related data D2 (e.g., analysis logic information Bn, EX, Fk), integration rules for the analysis results (scoring results) of biometric data D1 and the analysis results (scoring results) of mind-related data D2 (e.g., a predetermined function PF that defines the integration), aggregation and analysis rules, a group of candidate action plans including candidates for organizational action plans, and action plan selection rules.

[0256] In this case, the aggregate analysis rules are the rules for aggregating and analyzing the integrated analysis results (e.g., integrated score) of the analysis results (scoring results) of the bio-related data D1 obtained for each member according to the integration rules and the analysis results (scoring results) of the mind-related data D2. The action plan selection rules are information that associates the analysis results based on the aggregate analysis rules with the optimal action plan candidate when those analysis results are obtained. The analysis results based on the aggregate analysis rules include, for example, the evaluation results of the state of the organization and / or the aggregated results of the integrated score. Furthermore, the bio-related data D1 includes at least the biological data D121. The mind-related data D2 includes at least the mind data D21.

[0257] Furthermore, the prompt includes: (1) analyzing bio-related data D1 for each member according to the analysis logic information; (2) analyzing mind-related data D2 for each member according to the analysis logic information; (3) comprehensively analyzing the analysis results of bio-related data D1 and mind-related data D2 according to the integration rules; (4) aggregating and analyzing the integrated analysis results for all members according to the aggregation analysis rules; (5) selecting action plan candidates associated with the analysis results obtained according to the aggregation analysis rules as organizational action plans by referring to the action plan selection rules; (6) obtaining the specific content of the selected organizational action plan from the group of action plan candidates; and (7) generating image information of the organizational action plan according to a predetermined format.

[0258] For example, the information processing device 100 inputs prompts and reference data (context data) to the LLM to generate an individual action plan.

[0259] In this case, as a third example, the reference data includes member bio-related data D1, analysis logic information for analyzing bio-related data D1, member mind-related data D2, analysis logic information for analyzing mind-related data D2, integration rules for the analysis results (scoring results) of bio-related data D1 and the analysis results (scoring results) of mind-related data D2, aggregate analysis rules, a group of individual plan candidates including individual action plan candidates, and action plan selection rules.

[0260] Furthermore, the aggregate analysis rules are the rules for aggregating and analyzing the integrated analysis results (e.g., integrated score) of the analysis results (scoring results) of the member's bio-related data D1 and the analysis results (scoring results) of the member's mind-related data D2, obtained according to the integration rules. The action plan selection rules are information that associates the analysis results based on the aggregate analysis rules with the optimal action plan candidate when those analysis results are obtained. The analysis results based on the aggregate analysis rules include, for example, the evaluation results of the member's condition and / or the aggregated results of the integrated score. Also, the bio-related data D1 includes at least biological data D121. The mind-related data D2 includes at least mind data D21.

[0261] Furthermore, the prompt includes: (1) analyzing the member's biological data D1 according to the analysis logic information; (2) analyzing the member's mind-related data D2 according to the analysis logic information; (3) comprehensively analyzing the analysis results of the biological data D1 and the mind-related data D2 according to the integration rules; (4) aggregating and analyzing the integrated analysis results according to the aggregation analysis rules; (5) selecting action plan candidates associated with the analysis results obtained according to the aggregation analysis rules as individual action plans by referring to the action plan selection rules; (6) obtaining the specific content of the selected individual action plan from the group of individual plan candidates; and (7) generating image information of the individual action plan according to a predetermined format.

[0262] In the second and third examples, for example, the analysis unit 112 may execute the processing corresponding to prompts (1) to (4), and the generation unit 113 may input the execution result (analysis result) to the LLM, causing the LLM to execute the processing of prompts (5) to (7).

[0263] For example, the information processing device 100 (generation unit 113) inputs prompts and reference data (context data) to the LLM to generate an organizational assessment report 500 or an organizational root cause map MP1.

[0264] In this case, as a fourth example, the reference data includes the analysis results of bio-related data D1 from multiple members (scoring results, evaluation results of the state of tissue), the analysis results of mind-related data D2 from multiple members (scoring results, evaluation results of the state of tissue), the results of integrated analysis of bio-related data D1 and mind-related data for multiple members (integrated score, evaluation results of the state of tissue), and information on the visualization format (display format) of the analysis results. The visualization format information is information that defines the structure of the tissue evaluation report 500 or tissue root cause map MP1, such as the type of graph and configuration format. The prompt indicates that, according to the visualization format information, the analysis results of bio-related data D1 and mind-related data D2 from multiple members will be visualized to generate image information representing the tissue evaluation report 500 or tissue root cause map MP1.

[0265] In the fourth example, as in the first to third examples, the LLM may be used to perform tasks ranging from analysis to the generation of organizational assessment reports such as the 500.

[0266] Furthermore, for example, the information processing device 100 (generation unit 113) may input prompts and reference data (context data) to the LLM to generate a personal evaluation report 800 or a personal root cause map MP2. In this fifth example, the reference data includes the analysis results of the member's bio-related data D1, the analysis results of the member's mind-related data D2, the results of integrated analysis of the member's bio-related data D1 and mind-related data, and information on the visualization format (display format) of the analysis results. The prompt indicates that, according to the visualization format information, the analysis results of the member's bio-related data D1 and mind-related data D2 will be visualized to generate image information representing the personal evaluation report 800 or the personal root cause map MP2. In the fifth example as well, similar to the first to third examples, the LLM may be made to perform the analysis and generate the personal evaluation report 800, etc.

[0267] (Summary of biometric data D1 and mind-related data D2) The biological data D1 analyzed by the analysis unit 112 includes biological data D121. Preferably, the biological data D1 includes biological data D121 and vital data D122. More preferably, the biological data D1 includes biological data D121, self-assessment data D11, and / or vital data D122. The mind-related data D2 analyzed by the analysis unit 112 includes mind data D21. Mind data D21 includes thinking tendency data D211. Preferably, mind data D21 includes thinking tendency data D211 and behavioral tendency data D212. Also preferably, mind-related data D2 includes mind data D21 and interview data D22.

[0268] (modified version) A modified version of this embodiment will be described with reference to Figure 4. In the above embodiment, the focus was on the organization, and an organizational action plan PL1 was generated. However, in the modified version, the focus is on the individual, and an individual action plan PL2 is generated. In the modified version, an individual evaluation report 800 and / or an individual root cause map MP2 may also be generated. In the modified version, the organizational action plan PL1, organizational evaluation report 500, and organizational root cause map MP1 are not created.

[0269] In a modified example, the analysis unit 112 evaluates the subject's condition by analyzing at least the biological data D1. Preferably, the analysis unit 112 evaluates the subject's condition by analyzing the biological data D1 and the mind-related data D2. More preferably, the analysis unit 112 evaluates the subject's condition by comprehensively analyzing the biological data D1 and the mind-related data D2. The subject is an example of an "individual".

[0270] The generation unit 113 generates a personal action plan PL2 to solve the subject's problems based on the evaluation results of the subject's condition. The generation unit 113 may also generate a personal evaluation report 800 and / or a personal root cause map MP2 based on the analysis results of the bio-related data D1, the analysis results of the mind-related data D2, the integrated analysis results of the bio-related data D1 and the mind-related data D2, and / or the evaluation results of the subject's condition.

[0271] The contents of the bio-related data D1 and mind-related data D2 analyzed by the analysis unit 112 are as described with reference to Figure 3. In addition, in the description of the above embodiment, by replacing "members" with "subjects (individuals)", the description of the modified form can be replaced with the description of the modified form, except for the parts relating to organizations. For example, the processing shown in the flowchart of Figure 23 can be carried out similarly in the modified form. Furthermore, in the modified form as described above, the processing unit 110 may use an artificial intelligence model to create an individual action plan, an individual evaluation report, and an individual root cause map.

[0272] In the modified version, as in the embodiment described above, both biological data D1, which includes biochemical data D121, and mind-related data D2, which includes mind data D21 based on psychological factors, are analyzed. Therefore, mind data D21 complements psychological factors that cannot be captured by biological data D121 alone, while biological data D121 complements medical validity that cannot be obtained by mind data D21 alone. This synergistic effect improves the accuracy of the assessment of the subject's condition, making it possible to generate an effective, personalized individual action plan PL2 for the subject. In other words, the modified version improves the accuracy of the assessment of an individual's condition, making it possible to generate an effective individual action plan PL2 for solving the individual's problems. Furthermore, the modified version has the same effects as the effects related to the members in the embodiment described above.

[0273] Preferred embodiments and modifications of the present disclosure have been described in detail above with reference to the attached drawings, but the technical scope of the present disclosure is not limited to such examples. It is clear to any person with ordinary skill in the art of the present disclosure that various modifications or alterations can be conceived within the scope of the technical idea set forth in the claims, and these too are understood to fall within the technical scope of the present disclosure.

[0274] Furthermore, the devices or systems described herein may be implemented as a single device, or they may be implemented as a group of devices (e.g., cloud servers) that are partially or entirely connected by a network.

[0275] Furthermore, the series of processes performed by the apparatus described herein may be implemented using software, hardware, or a combination of software and hardware.

[0276] Furthermore, the processes described in this specification using flowcharts may not necessarily be executed in the order shown in the figures. Some process steps may be executed in parallel. Additionally, additional process steps may be adopted, or some process steps may be omitted.

[0277] Furthermore, the effects described in this specification are merely illustrative or exemplary and not limiting. That is, the technology according to the present disclosure may exhibit other effects that are apparent to those skilled in the art from the description in this specification, in addition to or instead of the above effects.

[0278] Note that the following configurations also belong to the technical scope of the present disclosure.

[0279] (Item 1) An analysis unit that evaluates the state of the organization by analyzing biological data related to a plurality of members belonging to the organization, and A generation unit that generates a first action plan for solving the problems of the organization based on the evaluation result of the state of the organization, and an information processing apparatus. The biological data related to the living body includes biological data indicating biological information extracted from a biological sample of the member.

[0280] (Item 2) The analysis unit evaluates the state of the organization by analyzing the biological data related to the living body and the mind-related data of the plurality of members, The mind-related data includes mind data, The mind data includes at least one of thinking tendency data indicating a thinking tendency based on the cognitive function of the member and behavior tendency data indicating a behavior tendency based on a psychological factor or a mental factor of the member. The information processing apparatus according to Item 1.

[0281] (Item 3) The analysis unit according to Item 2, which evaluates the state of the organization by integratively analyzing the biological data related to the living body and the mind-related data for the plurality of members.

[0282] (Item 4) The aforementioned analysis unit, Based on the scores of the evaluation items for the biological data and the evaluation items for the mind-related data, an integrated score is calculated for each member. The information processing device described in item 3, which aggregates the integrated scores for the aforementioned multiple members and evaluates the state of the organization based on the aggregated results.

[0283] (Item 5) The generation unit generates the first action plan according to the first action plan selection information, The first action plan selection information includes information that associates multiple organizational status information with multiple action plan candidates, The information processing device according to item 1 or item 2, wherein the generation unit refers to the first action plan selection information, obtains the action plan candidate associated with the organizational state information corresponding to the evaluation result of the organizational state, and adopts the obtained action plan candidate as the first action plan.

[0284] (Item 6) The aforementioned biological data includes self-assessment data based on input from the members, The aforementioned self-assessment data includes scores for each of several assessment items used to identify the biochemical causes of symptoms appearing in humans. The information processing device according to item 1 or item 2, wherein the biological data includes the biological information related to the evaluation item.

[0285] (Item 7) The information processing device according to item 1 or item 2, wherein the biological data includes vital data showing the vital signs of the member.

[0286] (Item 8) The aforementioned thinking tendency data includes the scores of each of a plurality of evaluation items for identifying the thinking tendency based on the cognitive function of the member, The information processing apparatus according to item 2 or item 3, wherein the score is based on the input of the member.

[0287] (Item 9) The behavior tendency data includes scores for each of a plurality of evaluation items for specifying a behavior tendency based on a psychological factor or a mental factor of the member. The information processing apparatus according to item 2 or item 3, wherein the score is based on the input of the member.

[0288] (Item 10) The mind-related data includes interview data. The interview data shows the results of an interview between an expert on the evaluation of human thinking tendencies and / or behavior tendencies and the member, and includes scores for each of a plurality of evaluation items for evaluating the thinking tendencies and / or behavior tendencies of the member. The information processing apparatus according to item 2 or item 3.

[0289] (Item 11) The analysis unit identifies the cause of the problem in the organization by analyzing at least the mind-related data, and estimates an error event that may occur in the organization based on the result of the cause identification. The information processing apparatus according to item 2 or item 3, further comprising an output control unit that generates the cause identification result and the estimated result of the error event, and image information indicating the problem of the organization, and displays the image information on a terminal of the organization.

[0290] (Item 12) The generation unit generates the basis of the first action plan based on the analysis results of the biological-related data and the mind-related data. The information processing apparatus according to item 2 or item 3.

[0291] (Item 13) The first action plan is an information processing device according to item 1 or item 2, which includes one or more pieces of information from among coaching information for resolving issues related to employee turnover, coaching information for resolving issues related to employees on leave, coaching information for resolving issues related to human resources, coaching information for resolving issues related to organizational culture, and coaching information for resolving issues related to sales, depending on the results of the evaluation of the state of the organization.

[0292] (Item 14) The analysis unit evaluates the state of each member by analyzing the biological data and mind-related data for each member. The information processing device according to item 2 or item 3, wherein the generation unit generates a second action plan for solving the problems of each member based on the evaluation results of the member's condition.

[0293] (Item 15) The information processing device described in item 14, wherein the generation unit generates the second action plan based on the evaluation results of the status of the members and the input information entered via the doctor's terminal.

[0294] (Item 16) The generation unit generates the second action plan according to the second action plan selection information, The second action plan selection information includes information that associates multiple personal status information with multiple action plan candidates, The information processing device according to item 14, wherein the generation unit refers to the second action plan selection information to obtain the action plan candidate associated with the personal status information corresponding to the evaluation result of the status of the member, and adopts the obtained action plan candidate as the second action plan.

[0295] (Item 17) The computer evaluates the state of the organization by analyzing biometric data of at least several members belonging to the organization, The computer includes the step of generating a first action plan for solving the organization's problems based on the results of an evaluation of the organization's state, The information processing method includes, for example, biological data representing biological information extracted from a biological sample of the member.

[0296] (Item 18) On the computer, At a minimum, the procedure includes the step of evaluating the state of the organization by analyzing biometric data of multiple members belonging to the organization, Based on the evaluation results of the state of the organization, the steps include generating a first action plan to solve the organization's problems and executing it. The aforementioned biological data includes a program that contains biological data representing biological information extracted from biological samples of the members. [Explanation of Symbols]

[0297] 100 Information processing device, 111 Acquisition unit, 112 Analysis unit, 113 Generation unit, 114 Output control unit, T1 Organization terminal, T2 Member terminal, T3 Doctor terminal, T4 Health coach terminal, T5 Mind coach terminal

Claims

1. A memory device storing an artificial intelligence model, The artificial intelligence model includes at least a processing unit that takes the subject's biometric data and mind-related data as input and generates information to be provided to the subject regarding the subject's state, The aforementioned biological data includes biological data showing biological information extracted from the subject's biological sample, and self-assessment data based on input from the subject. The aforementioned mind-related data includes mind data, The mind data includes at least one of the following: thinking tendency data indicating the thinking tendency based on the cognitive function of the subject, and behavioral tendency data indicating the behavioral tendency based on the psychological or mental factors of the subject. The aforementioned artificial intelligence model is trained to output information to be provided to the individual regarding the individual's state when the individual's biometric data and mind-related data are input. The aforementioned self-assessment data includes multiple evaluation items and multiple scores for identifying the biochemical causes of symptoms appearing in humans. The aforementioned biological data includes multiple evaluation items and multiple scores, An information processing system in which the multiple evaluation items of the biological data include evaluation items that are the same as the evaluation items of the self-assessment data.

2. The information provided to the subject regarding the subject's condition indicates an action plan to resolve the subject's problems, The information processing system according to claim 1, wherein the information provided to the individual regarding the individual's state indicates an action plan for solving the individual's problems.

3. The information processing system according to claim 2, wherein the artificial intelligence model learns the individual's biometric data, mind-related data, and action plan, along with the amount of change, improvement rate, and / or achievement level of indicators representing the individual's state before and after the implementation of the action plan.

4. The processing unit inputs prompts and reference data to the artificial intelligence model to generate the action plan. The reference data includes the subject's biological data and mind-related data, as well as information on the rules for analyzing the biological data and mind-related data. The information processing system according to claim 2 or 3, wherein the prompt includes an instruction to analyze the biological data and the mind-related data in accordance with the analysis rules, evaluate the subject's condition based on the analysis results, and generate the action plan based on the evaluation results.

5. The information processing system according to claim 1 or 2, wherein the biological data includes vital data indicating the vital signs of the subject.

6. The aforementioned mind-related data includes interview data, The information processing system according to claim 1, wherein the interview data shows the results of an interview between an expert on the evaluation of human thinking tendencies and / or behavioral tendencies and the subject, and includes the scores of each of a plurality of evaluation items for evaluating the subject's thinking tendencies and / or behavioral tendencies.

7. The information processing system according to claim 1 or claim 6, wherein the mind data includes the thinking tendency data and the behavioral tendency data.

8. The information processing system according to claim 1 or 2, wherein the artificial intelligence model is trained to analyze the individual's biometric data and mind-related data, evaluate the individual's state based on the analysis results, and generate an action plan based on the evaluation results.

9. The information provided to the subject regarding the subject's condition indicates the evaluation result of the subject's condition, The information processing system according to claim 1, wherein the information provided to the individual regarding the individual's state indicates the results of an evaluation of the individual's state.

10. A memory device storing an artificial intelligence model, The artificial intelligence model includes a processing unit that receives prompts, biometric data and mind-related data of the subject, and information on the rules for analyzing the biometric data and mind-related data to generate an action plan for solving the subject's problems. The aforementioned biological data includes biological data showing biological information extracted from the subject's biological sample, and self-assessment data based on input from the subject. The aforementioned mind-related data includes mind data, The mind data includes at least one of the following: thinking tendency data indicating the thinking tendency based on the cognitive function of the subject, and behavioral tendency data indicating the behavioral tendency based on the psychological or mental factors of the subject. The aforementioned artificial intelligence model is trained to output an action plan to solve the individual's problems when given an individual's biometric data and mind-related data as input. The prompt includes instructions to analyze the bio-related data and the mind-related data in accordance with the analysis rules, evaluate the subject's condition based on the analysis results, and generate the action plan based on the evaluation results. The aforementioned self-assessment data includes multiple evaluation items and multiple scores for identifying the biochemical causes of symptoms appearing in humans. The aforementioned biological data includes multiple evaluation items and multiple scores, The aforementioned multiple evaluation items for the biological data include evaluation items that are the same as those for the self-assessment data. An information processing system comprising the analysis rule, which includes determining the score of the evaluation item of the biological data based on the score of the evaluation item of the self-assessment data and the score of the evaluation item of the biological data having the same content as the evaluation item of the self-assessment data, and using the score as an evaluation of the subject's condition from a biochemical standpoint.

11. The computer inputs at least the subject's biometric data and mind-related data into an artificial intelligence model to generate information to be provided to the subject regarding the subject's state. The aforementioned biological data includes biological data showing biological information extracted from the subject's biological sample, and self-assessment data based on input from the subject. The aforementioned mind-related data includes mind data, The mind data includes at least one of the following: thinking tendency data indicating the thinking tendency based on the cognitive function of the subject, and behavioral tendency data indicating the behavioral tendency based on the psychological or mental factors of the subject. The aforementioned artificial intelligence model is trained to output information to be provided to the individual regarding the individual's state when the individual's biometric data and mind-related data are input. The aforementioned self-assessment data includes multiple evaluation items and multiple scores for identifying the biochemical causes of symptoms appearing in humans. The aforementioned biological data includes multiple evaluation items and multiple scores, An information processing method wherein the plurality of evaluation items for the biological data include evaluation items that have the same content as the evaluation items for the self-assessment data.

12. A computer inputs prompts, biometric data and mind-related data of a subject, and information on the rules for analyzing the biometric data and mind-related data into an artificial intelligence model to generate an action plan for solving the subject's problem. The aforementioned biological data includes biological data showing biological information extracted from the subject's biological sample, and self-assessment data based on input from the subject. The aforementioned mind-related data includes mind data, The mind data includes at least one of the following: thinking tendency data indicating the thinking tendency based on the cognitive function of the subject, and behavioral tendency data indicating the behavioral tendency based on the psychological or mental factors of the subject. The aforementioned artificial intelligence model is trained to output an action plan to solve the individual's problems when given an individual's biometric data and mind-related data as input. The prompt includes instructions to analyze the bio-related data and the mind-related data in accordance with the analysis rules, evaluate the subject's condition based on the analysis results, and generate the action plan based on the evaluation results. The aforementioned self-assessment data includes multiple evaluation items and multiple scores for identifying the biochemical causes of symptoms appearing in humans. The aforementioned biological data includes multiple evaluation items and multiple scores, The aforementioned multiple evaluation items for the biological data include evaluation items that are the same as those for the self-assessment data. The analysis rule is an information processing method that includes determining the score of the evaluation item of the biological data based on the score of the evaluation item of the self-assessment data and the score of the evaluation item of the biological data which has the same content as the evaluation item of the self-assessment data, and using the score as an evaluation of the subject's condition from a biochemical standpoint.

13. The computer is instructed to input at least the subject's biometric data and mind-related data into an artificial intelligence model and generate information to be provided to the subject regarding the subject's state. The aforementioned biological data includes biological data showing biological information extracted from the subject's biological sample, and self-assessment data based on input from the subject. The aforementioned mind-related data includes mind data, The mind data includes at least one of the following: thinking tendency data indicating the thinking tendency based on the cognitive function of the subject, and behavioral tendency data indicating the behavioral tendency based on the psychological or mental factors of the subject. The aforementioned artificial intelligence model is trained to output information to be provided to the individual regarding the individual's state when the individual's biometric data and mind-related data are input. The aforementioned self-assessment data includes multiple evaluation items and multiple scores for identifying the biochemical causes of symptoms appearing in humans. The aforementioned biological data includes multiple evaluation items and multiple scores, The program includes, for each of the aforementioned biological data, evaluation items that are the same as those for the aforementioned self-assessment data.

14. A computer is made to input prompts, biometric data and mind-related data of a subject, and information on the rules for analyzing the biometric data and mind-related data into an artificial intelligence model, and to generate an action plan for solving the subject's problem. The aforementioned biological data includes biological data showing biological information extracted from the subject's biological sample, and self-assessment data based on input from the subject. The aforementioned mind-related data includes mind data, The mind data includes at least one of the following: thinking tendency data indicating the thinking tendency based on the cognitive function of the subject, and behavioral tendency data indicating the behavioral tendency based on the psychological or mental factors of the subject. The aforementioned artificial intelligence model is trained to output an action plan to solve the individual's problems when given an individual's biometric data and mind-related data as input. The prompt includes instructions to analyze the bio-related data and the mind-related data in accordance with the analysis rules, evaluate the subject's condition based on the analysis results, and generate the action plan based on the evaluation results. The aforementioned self-assessment data includes multiple evaluation items and multiple scores for identifying the biochemical causes of symptoms appearing in humans. The aforementioned biological data includes multiple evaluation items and multiple scores, The aforementioned multiple evaluation items for the biological data include evaluation items that are the same as those for the self-assessment data. The analysis rule is a program that includes determining the score of the evaluation item of the biological data based on the score of the evaluation item of the self-assessment data and the score of the evaluation item of the biological data which has the same content as the evaluation item of the self-assessment data, and using the score as an evaluation of the subject's condition from a biochemical standpoint.

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