Information processing device, information processing system, information processing method and program

Abstract

To refer to information updated by another node.SOLUTION: An information processor includes an extraction section, a generation section, and a notification section. The extraction section refers to a database in which a category is associated with an instruction content when each purpose is successfully achieved, for each category into which an instruction content for each purpose is classified, and extracts an instruction content at the time of success, which is associated with the same category as the category into which the instruction content is classified, when an instruction content for a predetermined purpose fails in the achievement of the predetermined purpose. The generation section generates a new instruction content for the predetermined purpose on the basis of the extracted instruction content. The notification section notifies a user of the generated instruction content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and the like. [Background technology]

[0002] Specific instructions for a person or business to act in order to achieve a goal may be presented to the person or business.

[0003] For example, Patent Document 1 describes determining whether a user's health condition is poor based on health condition information such as blood pressure and weight, and describes presenting instructions for improving diet to lower blood pressure, for example, if the blood pressure is high. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 009057 [Patent Document 2] Japanese Patent Publication No. 2020-161187 [Patent Document 3] Japanese Patent Application Publication No. 2018-042811 [Patent Document 4] Japanese Patent Application Laid-Open No. 2009-009350 [Patent Document 5] Japanese Patent Application Laid-Open No. 2007-323527 Summary of the Invention [Problem to be solved by the invention]

[0005] It may be difficult to refer to information updated by other nodes, such as devices with AI (Artificial Intelligence) capabilities.

[0006] An example of an object of the present disclosure is to provide an information processing device or the like that can refer to information updated by other nodes. [Means for solving the problem]

[0007] An information processing device in one aspect of the present disclosure includes an extraction means that refers to a database in which categories into which instruction content for each objective are classified are associated with instruction content when each objective is successfully achieved, and when instruction content for a specified objective fails to achieve the specified objective, extracts the instruction content when the instruction content for the specified objective fails to achieve the specified objective, which is associated with the same category as the category into which the instruction content is classified; a generation means that generates new instruction content for the specified objective based on the extracted instruction content; and a notification means that notifies the user of the generated instruction content.

[0008] An information processing system according to one aspect of the present disclosure includes a plurality of nodes, and an extraction means for any one of the plurality of nodes referring to a database that associates categories into which instructions for each objective are classified with instructions for successful achievement of each objective, and extracting, when instructions for a specified objective fail to achieve the specified objective, the instruction for successful achievement that is associated with the same category as the category into which the instructions are classified; a generation means for generating new instruction content for the specified objective based on the extracted instruction content; and a notification means for notifying the generated instruction content, and a registration means for registering, when the instruction content generated by the node succeeds in achieving the objective of the instruction content, the category into which the instruction content is classified and the instruction content in the database.

[0009] An information processing method in one aspect of the present disclosure refers to a database in which instructions for each objective are classified into categories and associated with the instructions to be given when each objective is successfully achieved, and when instructions for a specified objective fail to achieve the specified objective, extracts the instructions to be given when the objective is successful that are associated with the same category as the category to which the instructions are classified, generates new instructions for the specified objective based on the extracted instructions, and notifies the user of the generated instructions.

[0010] A program in one aspect of the present disclosure causes a computer to refer to a database that associates categories into which instructions for each objective are classified with instructions for when each objective is successfully achieved, and when instructions for a specified objective fail to achieve the specified objective, extracts the instructions for when the objective is successful that are associated with the same category as the category into which the instructions are classified, generates new instructions for the specified objective based on the extracted instructions, and notifies the computer of the generated instructions.

[0011] The program may be stored in a non-transitory computer-readable recording medium. [Effects of the Invention]

[0012] According to the present disclosure, it is possible to refer to information updated by other nodes. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram showing a configuration example of an information processing device according to a first embodiment; [Figure 2] 1 is a flowchart illustrating an example of an operation of the information processing device according to the embodiment. [Figure 3] FIG. 1 is an explanatory diagram illustrating an example of an information processing system. [Figure 4] FIG. 10 is a block diagram showing a configuration example of a node according to a second embodiment. [Figure 5]10 is a flowchart illustrating an example of an operation of a node according to the second embodiment when a failure occurs; [Figure 6] 10 is a flowchart illustrating an example of an operation of a node according to the second embodiment when the node is successful. [Figure 7] FIG. 1 is an explanatory diagram illustrating an example of an information processing system. [Figure 8] FIG. 2 is an explanatory diagram showing an example of cooperation between a health promotion AI node and a health management AI node according to the first embodiment. [Figure 9] FIG. 10 is an explanatory diagram illustrating an example of storage in a success DB according to the first embodiment. [Figure 10A] FIG. 10 is an explanatory diagram illustrating an example (part 1) of updating a failure DB according to the first embodiment. [Figure 10B] FIG. 10 is an explanatory diagram illustrating an example (part 2) of updating the failure DB according to the first embodiment. [Figure 11] FIG. 10 is an explanatory diagram illustrating an example of generating new instruction content according to the first embodiment. [Figure 12] FIG. 10 is an explanatory diagram illustrating an example of storage in a success DB after update according to the first embodiment. [Figure 13] 1 is a flowchart (part 1) illustrating an operation example of the information processing system according to the first embodiment. [Figure 14] 10 is a flowchart (part 2) illustrating an operation example of the information processing system according to the first embodiment. [Figure 15] FIG. 10 is an explanatory diagram illustrating an example of storage in a success DB according to the second embodiment. [Figure 16A] FIG. 10 is an explanatory diagram (part 1) showing an example of updating a failure DB according to the second embodiment; [Figure 16B] FIG. 10 is an explanatory diagram (part 2) showing an example of updating the failure DB according to the second embodiment; [Figure 17] FIG. 10 is an explanatory diagram illustrating an example of generating new instruction content according to the second embodiment. [Figure 18] FIG. 11 is an explanatory diagram illustrating an example of storage in a success DB after updating according to the second embodiment. [Figure 19] FIG. 10 is an explanatory diagram illustrating an information processing system according to a third embodiment. [Figure 20]FIG. 11 is an explanatory diagram showing an example of cooperation between a health promotion AI node and a human resources management AI node according to a third embodiment. [Figure 21] FIG. 11 is an explanatory diagram illustrating an example of storage in a success DB according to the third embodiment. [Figure 22A] FIG. 11 is an explanatory diagram (part 1) showing an example of updating a failure DB according to the third embodiment. [Figure 22B] FIG. 13 is an explanatory diagram (part 2) showing an example of updating the failure DB according to the third embodiment. [Figure 23] FIG. 11 is an explanatory diagram illustrating an example of generating new instruction content according to the third embodiment. [Figure 24] FIG. 11 is an explanatory diagram illustrating an example of storage in a success DB after updating according to the third embodiment. [Figure 25] FIG. 10 is an explanatory diagram showing an example of an event tag. [Figure 26] FIG. 2 is an explanatory diagram illustrating an example of a hardware configuration of a computer device. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, with reference to the drawings, embodiments of an information processing device (node), an information processing system, an information processing method, a program, and a non-transitory recording medium for recording a program according to the present disclosure will be described in detail. The present embodiments do not limit the disclosed technology.

[0015] (Embodiment 1) First, in the first embodiment, basic functions of an information processing device will be described. Fig. 1 is a block diagram showing an example of a configuration of an information processing device 10 according to the first embodiment. The information processing device 10 includes an extraction unit 101, a generation unit 102, and a notification unit 103.

[0016] The extraction unit 101 refers to the database and extracts instruction contents for success associated with the same category as the category into which the instruction contents are classified when an action taken in accordance with the instruction contents for a predetermined purpose fails.

[0017] The predetermined purpose is not particularly limited, for example, health promotion such as dieting, employment, disease prediction, disaster response, etc. The target here is not particularly limited, for example, a person acting toward the predetermined purpose, a business operator acting toward the predetermined purpose, etc. For example, the business operator is a hospital, a company, etc.

[0018] Here, the database stores information that associates categories with instruction contents when each objective is successfully achieved. Specific database contents will be described in each example of embodiment 2. A category is a classification of instruction contents for each objective. A category may be, for example, a classification of events that occur when a subject acts in accordance with the instruction contents for each objective. A category may also be a classification of instruction contents and factors for each objective. In this case, factors refer to factors that led to failure in the case of failure, and factors that led to success in the case of success. The classification method is not particularly limited. For example, categories are predetermined. Examples of categories include moderation, learning, progress, and sales. These categories are also called event tags.

[0019] The generation unit 102 generates new instruction content for a predetermined purpose based on the extracted instruction content. Specifically, for example, the generation unit 102 extracts keywords from the instruction content using natural language processing. Then, the generation unit 102 generates new instruction content for achieving the predetermined purpose based on the extracted keywords and the instruction content at the time of failure.

[0020] The notification unit 103 notifies the generated instruction content. The notification method is not particularly limited and may be an email, an electronic message, or the like.

[0021] 2 is a flowchart illustrating an example of an operation of the information processing device 10 according to the embodiment. The extraction unit 101 refers to the database and extracts instruction contents for success associated with the same category as the category into which the instruction contents are classified when an action taken in accordance with the instruction contents for a predetermined purpose fails (step S101).

[0022] Next, the generating unit 102 generates new instruction content for a predetermined purpose based on the extracted instruction content (step S102). Then, the notifying unit 103 notifies the generated instruction content (step S103). After step S103, the information processing device 10 ends its operation.

[0023] Even if a person or business person acts according to the presented instructions, there are cases where the person or business fails. In such cases, it is difficult to create new instructions. As described above, in the first embodiment, when an instruction fails, the information processing device 10 generates new instructions based on other instructions classified in the same category. This allows the information processing device 10 to try new instructions using past success stories. This makes it easier to create instructions. Furthermore, for example, if the database has been updated by another node, such as a device with AI functionality, the database updated by the other node can be referenced.

[0024] (Embodiment 2) In the second embodiment, an example will be described in which the information processing device according to the first embodiment is used as a basic function and the information processing device described in the first embodiment is realized by at least one of the nodes in a system made up of a plurality of nodes.

[0025] 3 is an explanatory diagram showing an example of an information processing system 2. The information processing system 2 includes a plurality of nodes 20-1 to 20-n, where n is an integer of 2 or more, and is not particularly limited.

[0026] The plurality of nodes 20-1 to 20-n may be connected to one another via, for example, a communication network, etc. Furthermore, the plurality of nodes 20-1 to 20-n may be arranged in a hierarchical structure.

[0027] Some of the plurality of nodes 20-1 to 20-n may have a server-like function. The node 20 having the server-like function may be, for example, a node 20 having an AI processing function.

[0028] Furthermore, some of the nodes 20 of the plurality of nodes 20-1 to 20-n may be nodes 20 corresponding to businesses or people, respectively. The nodes 20 corresponding to businesses or people, respectively, are not particularly limited and may be, for example, wearable devices, smartphones, PCs, home appliances such as televisions and refrigerators, vehicles, etc.

[0029] Furthermore, some of the nodes 20 among the plurality of nodes 20-1 to 20-n may be various sensors or robots equipped with various sensors.

[0030] Furthermore, any one of the plurality of nodes 20-1 to 20-n may be, for example, a node 20 having a database. In Fig. 3, the base data node 20-1 has a failure DB (Database) 2011 and a success DB 2001. The failure DB 2011 and the success DB 2001 are updated, for example, by at least one of the plurality of nodes 20-1 to 20-n. Note that, for simplicity, in Fig. 3, the base data node 20-1 is connected to nodes 20-2 to 20-4, but it may also be connected to other nodes 20.

[0031] For the sake of simplicity, the base data node 20-1 is provided with a failure DB 2011 and a success DB 2001, but the failure DB 2011 and the success DB 2001 may be in different nodes 20.

[0032] In the following description, when not limited to a specific node, it may be simply referred to as a node 20. Furthermore, a node 20 having an AI function may be referred to as an AI node 20.

[0033] The success DB 2001 stores, for example, for each objective, the instruction content when the subject acts in accordance with the instruction and succeeds. Specifically, the success DB 2001 stores, for example, a success event table 2002 and a success content table 2003 associated with the success event table 2002. The success event table 2002 stores, in association with each other, the objective, a category into which the instruction content notified for the objective is classified, and an instruction content table that represents the notified instruction content.

[0034] The success content table 2003 stores instruction content, attribute tags, and instruction detail tags in association with each other. As mentioned above, attribute tags represent the attributes of the target of the instruction. Instruction detail tags represent a more detailed classification of the instruction content. Instruction detail tags and the cause tags in the failure cause table 2014, which will be described later, are assigned according to the same criteria.

[0035] Furthermore, the failure DB 2011 stores, for example, for each objective, the instruction content when a target acts in accordance with the instruction and fails. The failure DB 2011 stores, for example, a failure event table 2012, a failure content table 2013 associated with the failure event table 2012, and a failure cause table 2014 associated with the failure event table 2012. The failure event table 2012 stores, in association with each other, the objective, a category into which the instruction content notified for the objective is classified, the failure content table 2013 which represents the notified instruction content, and the failure cause table 2014. As mentioned above, this category is also called an event tag.

[0036] The failure content table 2013 stores instruction content, attribute tags that represent the attributes of the target of the instruction, and instruction detail tags that classify the details of the instruction content in association with each other. The failure cause table 2014 stores failure causes and cause tags in association with each other. Cause tags are subcategories that classify the causes of failure in detail.

[0037] Specific examples of the success DB 2001 and failure DB 2011 will be explained in the respective embodiments described later.

[0038] At least one of the nodes 20-1 to 20-n has the functions of the information processing device described in the first embodiment as a basic function.

[0039] 4 is a block diagram showing a configuration example of a node 20 according to the second embodiment. The node 20 shown in FIG. 4 is at least one of the plurality of nodes 20-1 to 20-n shown in FIG. 3. The node 20 includes, for example, an extracting unit 201, a generating unit 202, a notifying unit 203, a failure registering unit 204, and a success registering unit 205. In the second embodiment, the failure registering unit 204 and the success registering unit 205 are added to the functions described in the first embodiment. The extracting unit 201, the generating unit 202, and the notifying unit 203 are provided as basic functions of the extracting unit 201, the generating unit 202, and the notifying unit 203 described in the first embodiment.

[0040] The extraction unit 201, the generation unit 202, and the notification unit 203 may be the same as those in the first embodiment, and detailed description thereof will be omitted.

[0041] When a subject acts in accordance with an instruction and fails, the failure registration unit 204 registers in the failure DB 2011 the category into which the instruction is classified, the instruction, and the cause of the failure in association with each other.

[0042] The extraction unit 201 extracts, from the success DB 2001, instruction contents for successful cases that are associated with the same category as the category into which instruction contents for unsuccessful cases are classified.

[0043] For example, when there are multiple instruction contents for success associated with the same category, the extraction unit 201 may extract instruction contents having the same attribute of the instruction target. The attribute of the instruction target is, for example, the attribute of a person who is notified of the instruction content and acts in accordance with the instruction content. Examples of the attribute include "company employee," "self-employed," and "student," but the attribute is not particularly limited. Furthermore, as described above, the instruction targets may be classified by attribute tags.

[0044] For example, if the attribute tag of the instruction target in the case of failure is "office worker," the extraction unit 201 extracts from the success DB 2001 the instruction content in the case of success that is associated with the same category as the category into which the instruction content in the case of failure is classified, and that has the attribute tag "office worker."

[0045] Also, for example, if there are multiple instruction contents for success associated with the same category, the extraction unit 201 may extract instruction contents that are tagged with the same tag (instruction detail tag) as the factor tag that classifies the failure factors in more detail.

[0046] The generation unit 202 generates new instruction content for a predetermined purpose based on the extracted instruction content. Specifically, for example, the generation unit 202 extracts keywords from the instruction content using natural language processing. Then, the generation unit 202 generates new instruction content based on the extracted keywords and the instruction content at the time of failure. Furthermore, for example, the generation unit 202 may generate new instruction content based on the extracted keywords, the instruction content at the time of failure, and personal information of the instruction target.

[0047] Then, the notification unit 203 notifies the generated instruction content. As explained in the first embodiment, the notification method is not particularly limited.

[0048] Then, when the target acts in accordance with the instructions notified by the notification unit 203 and succeeds, the success registration unit 205 registers the notified instructions in the success DB 2001 in association with the category into which the notified instructions are classified.

[0049] 5 is a flowchart showing an example of an operation when a failure occurs in the node 20 according to the second embodiment. When a target fails when acting according to an instruction, the failure registration unit 204 registers the instruction in the failure DB 2011 in association with an event tag into which the instruction is classified (step S201).

[0050] The extraction unit 201 searches for an event tag identical to the event tag at the time of failure from the success event table 2002 included in the success DB 2001 (step S202). The extraction unit 201 extracts instruction content from the success content table 2003 associated with the searched event tag (step S203).

[0051] Furthermore, in step S203, the extraction unit 201 may extract, from the success content table 2003 associated with the searched event tag, instruction content associated with the same attribute tag as the attribute tag at the time of failure. Furthermore, in step S203, the extraction unit 201 may extract, from the success content table 2003 associated with the searched event tag, instruction content associated with the same instruction detail tag as the cause tag at the time of failure. Furthermore, in step S203, the attribute tag and the cause tag may be combined. For example, the extraction unit 201 may extract, from the success content table 2003 associated with the searched event tag, instruction content associated with the same attribute tag and instruction detail tag as the attribute tag and cause tag at the time of failure.

[0052] The generating unit 202 generates instruction content for the purpose from the extracted instruction content (step S204). Then, the notifying unit 203 notifies the generated instruction content (step S205). After step S205, the node 20 ends its operation.

[0053] 6 is a flowchart showing an example of the operation of the node 20 according to the second embodiment when the operation is successful. If the target's action according to the instruction content is successful, the success registration unit 205 registers the instruction content in association with an event tag into which the instruction content is classified in the success DB 2001 (step S211). After step S211, the node 20 ends its operation.

[0054] Next, a specific example of the success DB 2001 and failure DB 2011 and a specific example of the node 20 shown in Fig. 4 will be explained. Here, the explanation will be given taking health promotion, disease prediction, and employment as examples.

[0055] 4 may be realized by one node 20 or by multiple nodes 20. For example, each of the multiple nodes 20 may have each functional unit.

[0056] Example 1 In Example 1, a case will be described in which a patient is instructed to act in order to improve the patient's health. For example, a patient is presented with action instructions to improve their health. However, even if the patient acts according to the instructions, their health may not be improved. Alternatively, the patient may stop acting according to the instructions. In this way, if the improvement of health fails, it is preferable to incorporate a different approach and develop a new countermeasure.

[0057] Therefore, in Example 1, an example of generating new instruction content when a patient acts according to the instruction content but fails to improve his / her health will be described. More specifically, in Example 1, an example will be described in which the purpose is dieting as part of health improvement, and the target is a patient who is a target person for improving his / her health. Furthermore, the instruction content for health promotion will be described by taking as an example at least one of dietary instruction content and exercise instruction content for the target person.

[0058] 7 is an explanatory diagram showing an example of an information processing system 2. In addition to a base data node 20-1 that stores a failure DB 2011 and a success DB 2001, the information processing system 2 may also include a health management AI node 20-2, a health promotion AI node 20-3, an administrative procedure AI node 20-4, a hospital management AI node 20-5, a dietary management AI node 20-6, an information management AI node 20-7, an exercise management AI node 20-8, an administrative procedure node 20-9, a biological information measurement node 20-10, a personal information node 20-11, a behavior monitoring node 20-12, an exercise management node 20-13, a patient node 20-14, an instructor node 20-15, a registered dietitian node 20-16, hospital information management nodes 20-17 and 20-18, and a dietary management node 20-19. The information processing system 2 may also include a node 20 (not shown).

[0059] The patient node 20-14, instructor node 20-15, registered dietitian node 20-16, etc. are devices of each person. The type of device is not particularly limited. Furthermore, the hospital information management nodes 20-17 and 20-18 and the administrative procedure node 20-9, etc. are devices of each business operator. The type of device is not particularly limited. For example, the hospital information management node 20-17 is a node 20 corresponding to Hospital B. The hospital information management node 20-18 is a node 20 corresponding to Hospital A.

[0060] The information management AI node 20-7, the health management AI node 20-2, the health promotion AI node 20-3, the hospital management AI node 20-5, and the administrative procedure AI node 20-4 are the AI ​​nodes 20 described above.

[0061] In the first embodiment, it is assumed that the health promotion AI node 20-3 has the function units from the extraction unit 201 to the success registration unit 205 of the node 20 shown in FIG.

[0062] The biological information measurement node 20-10, the personal information node 20-11, and the behavior monitoring node 20-12 each collect various types of information.

[0063] For example, the biological information measurement node 20-10 acquires biological information of a patient. The biological information includes the patient's body temperature, blood pressure, height, weight, etc. More specifically, for example, the biological information measurement node 20-10 acquires information such as "body temperature: 37.5°C, blood pressure: 150-110, height: 170cm, weight: 80kg."

[0064] The personal information node 20-11 acquires the patient's personal information from the patient node 20-14. The personal information includes the patient's hospital visit history and hospital diagnosis results. Specifically, for example, the personal information node 20-11 acquires information such as "hospital visit history: (normal body temperature: 36.2°C, average blood pressure (180-120), diagnosis result: high blood pressure."

[0065] The behavior monitoring node 20-12 acquires the behavior history of the patient from the patient node 20-14. The behavior history is the patient's behavior, such as information on walking speed and stagger. Specifically, for example, the behavior monitoring node 20-12 acquires information such as "current walking speed: 3.8 km / h, current stagger: amplitude 30°, average walking speed: 3.3 km / h, average stagger: amplitude 10°" from the patient node 20-14.

[0066] 8 is an explanatory diagram showing an example of cooperation between the health promotion AI node 20-3 and the health management AI node 20-2 according to the first embodiment. The health management AI node 20-2 acquires collected information from various nodes 20 at predetermined intervals. The predetermined intervals here may be several seconds, several tens of seconds, several minutes, several hours, several days, or the like, but are not limited to these.

[0067] The health management AI node 20-2 calculates the patient's physical condition barometer using, for example, at least a portion of the biometric information, behavioral history, weather information, personal information, etc. (step S221). The physical condition barometer is a calculated value of an item related to the patient's physical condition. The physical condition barometer is not particularly limited. For example, the physical condition barometer may be a BMI (Body Mass Index) value or the like. The physical condition barometer may also be a calculated value of each of multiple items.

[0068] The health management AI node 20-2 then registers the calculated physical condition barometer for each patient in a physical condition barometer DB, etc. If there is an abnormality in the calculated physical condition barometer, the health management AI node 20-2 notifies the health promotion AI node 20-3 of the physical condition barometer.

[0069] When the generation unit 202 of the health promotion AI node 20-3 is notified for the first time that an abnormality has occurred in a patient, the generation unit 202 of the health promotion AI node 20-3 selects an optimal diet and exercise menu for the patient based on the diet recipes, exercise menu, and personal information such as the patient's physical condition and allergies (step S222).Then, the generation unit 202 of the health promotion AI node 20-3 generates instructions based on the selected diet recipes, exercise menu, and physical condition barometer (step S223).

[0070] Then, the notification unit 203 of the health promotion AI node 20-3 notifies the behavior monitoring node 20-12 of the generated instruction content. More specifically, for example, the notification unit 203 of the health promotion AI node 20-3 may notify the behavior monitoring node 20-12 of the instruction content via the health management AI node 20-2 (step S224).

[0071] In this way, the health management AI node 20-2 can detect patients who are behaving differently from usual at an early stage, and the health management AI node 20-2 and the health promotion AI node 20-3 can work together to provide guidance to the patients.

[0072] Furthermore, although not shown, if the health management AI node 20-2 determines based on the physical condition barometer that the injury is serious and that appropriate treatment is required at a hospital, it may cooperate with the hospital management AI node 20-5.

[0073] Next, we will explain an example in which the patient has been notified of the instructions but the improvement of their health has failed. The health management AI node 20-2 recalculates the physical condition barometer (step S221). Here, the failure of the improvement of health means, for example, that the health management AI node 20-2 has again determined that there is an abnormality in the physical condition barometer.

[0074] For example, suppose a patient's weight is increasing (e.g., currently 170 cm (centimeters) and 80 kg (kilograms)), and the health management AI node 20-2 and the health promotion AI node 20-3 notify the patient of instructions. The goal is to lose weight, and the instructions are instructions to improve exercise. For example, the instruction to improve exercise is to "run for 150 minutes once a week." For example, suppose the patient follows this instruction to improve exercise, but ends up gaining weight.

[0075] 9 is an explanatory diagram illustrating an example of storage in the success DB 2001 according to Example 1. The success DB 2001 stores the success event table 2002 and the success content table 2003, as described above.

[0076] The success event table 2002 stores, for each objective, an objective, an event tag, and a content in association with each other. As mentioned above, an event tag is a category into which instruction content is classified. An objective is the purpose of an instruction or suggestion. The content here is the success content table 2003.

[0077] In FIG. 9, the successful event table 2002 stores the objective "saving on food expenses," the event tag "moderateness," and the content "successful content table 2003-1," in association with each other. The successful content table 2003-1 stores the instruction detail tag, attribute tag, and instruction content, in association with each other. By setting information in each field, it is stored as one record in the successful content table 2003-1. In the example of FIG. 9, four records are stored.

[0078] The success content table 2003-1 stores, in association with each other, the instruction detail tag "strong will," the attribute tag "Self-Defense Forces," and the instruction content "limit daily food expenses to 1,000 yen, and if invited to a drinking party, decline."

[0079] In addition, the success content table 2003-1 stores, in association with each other, the instruction detail tag "schedule," the attribute tag "company employee," and the instruction content "Keep your daily food expenses to 1,000 yen, and if you are invited to a drinking party, take that amount and split it off from your food expenses for a later day, and balance the books on another day."

[0080] The success content table 2003-1 stores, in association with each other, the instruction detail tag "alternative," the attribute tag "self-employed," and the statement "Keep your daily food expenses to 1,000 yen, and if you are invited to a drinking party, save that amount on other expenses such as entertainment expenses, and balance out your total expenses."

[0081] The success content table 2003-1 stores, in association with each other, the instruction detail tag "schedule," the attribute tag "student," and the instruction content "Keep your daily food expenses to 1,000 yen, and if you have a drinking party, work a part-time job to balance the books."

[0082] Fig. 10A is an explanatory diagram illustrating an example (part 1) of updating the failure DB 2011 according to the embodiment 1. Fig. 10B is an explanatory diagram illustrating an example (part 2) of updating the failure DB 2011 according to the embodiment 1. As described above, the failure DB 2011 stores a failure event table 2012, a failure cause table 2014, and a failure content table 2013.

[0083] As shown in FIG. 10A, the failure registration unit 204 adds the objective "diet" and the event tag "moderation" into which the instruction content is classified to a failure event table 2012 included in the failure DB 2011. Note that the method for classifying the instruction content is not particularly limited. The failure registration unit 204 then generates a failure cause table 2014-1 and a failure content table 2013-1. Specifically, for example, as shown in FIG. 10B, the failure registration unit 204 generates a failure cause table 2014-1 linked to the objective "diet" and the event tag "moderation," and registers the failure causes in the generated failure cause table 2014-1.

[0084] Here, the failure registration unit 204 analyzes the behavior history and extracts from the behavior history any behavior that is different from the patient's usual behavior as a failure factor. Specifically, for example, the failure registration unit 204 analyzes the behavior history and detects that, as an unusual behavior, "the Olympics started, I watched so much TV that I became sleep-deprived, and therefore I was unable to secure enough time to run." Then, for example, the failure registration unit 204 extracts "Olympics," "TV," and "lack of sleep" as failure factors. Note that the process for extracting failure factors from the behavior history is not particularly limited. Then, the failure registration unit 204 registers each extracted failure factor and a factor tag, which is a category into which the failure factor is classified, in a failure factor table 2014-1. The factor tags for the factors "Olympics," "TV," and "lack of sleep" are "event," "entertainment," and "schedule," respectively.

[0085] Furthermore, the failure registration unit 204 registers the instruction content, attribute tag, and instruction detail tag when the diet fails in a failure content table 2013-1. As shown in Fig. 10, the failure content table 2013 stores an instruction detail tag "exercise" that classifies the details of the instruction content, an attribute tag "office worker," and an instruction content "running (run for 150 minutes once a week)" in association with each other.

[0086] Next, the extraction unit 201 searches for the event tag "moderate control" of the diet that failed this time from the successful event table 2002. Then, the extraction unit 201 identifies the successful content table 2003 associated with the event tag "moderate control" in the successful event table 2002. In the example of FIG. 9, the successful content table 2003-1 is identified.

[0087] 9, multiple instruction contents are registered in the success content table 2003-1. The extraction unit 201 only needs to extract at least one of the multiple instruction contents. For example, the extraction unit 201 may extract an instruction content from the success content table 2003-1 that has the same instruction detail tag as the cause tag at the time of failure. Alternatively, the extraction unit 201 may extract an instruction content from the success content table 2003-1 that has the same attribute tag as the attribute tag at the time of failure.

[0088] Here, the extraction unit 201 extracts instruction contents that include the factor tag "schedule" included in the failure factor table 2014 and the attribute tag "company employee" included in the failure content table 2013-1 in the instruction detail tag and attribute tag from the success content table 2003. Specifically, the extraction unit 201 extracts "keep daily food expenses to 1,000 yen, and if invited to a drinking party, allocate the amount from the food expenses of a later day and balance the books on another day" from the success content table 2003 shown in Fig. 9.

[0089] Next, the generating unit 202 generates new instruction content for the patient based on the extracted instruction content (step S225).

[0090] FIG. 11 is an explanatory diagram illustrating an example of generating new instruction content according to the first embodiment. The generating unit 202 extracts keywords from the extracted instruction content using natural language processing. Here, for example, "split" and "balance on another day" are extracted. Then, the generating unit 202 generates new instruction content based on the instruction content at the time of failure and the extracted keywords.

[0091] Specifically, for example, the generation unit 202 generates the instruction content "Running (run for 30 minutes five times a week)" based on the instruction content "Run (run for 150 minutes once a week)", the keyword "split" and the keyword "balance on another day".

[0092] The notification unit 203 then notifies the generated instruction content to the behavior monitoring node 20-12 that manages the patient's behavior (step S226). Specifically, the notification unit 203 notifies the behavior monitoring node 20-12 via the health management AI node 20-2. Furthermore, the behavior monitoring node 20-12 may notify the instruction content to the patient node 20-14 of the patient.

[0093] As described above, the vital information measurement node 20-10, the personal information node 20-11, and the behavior monitoring node 20-12 each collect various types of information. The health management AI node 20-2 then calculates a new physical condition barometer based on the information acquired from the various nodes 20.

[0094] If an abnormality is found in the physical condition barometer after notifying the health management AI node 20-2 of the instruction content, the health management AI node 20-2 may determine that the instruction has failed and notify the health promotion AI node 20-3 of the patient's physical condition barometer.

[0095] On the other hand, the health management AI node 20-2 may determine that the instruction has been successful if there is no abnormality in the physical condition barometer after notifying the instruction.The health management AI node 20-2 may notify the health promotion AI node 20-3 that the instruction has been successful.The success registration unit 205 of the health promotion AI node 20-3 updates the success event table 2002 and the success content table 2003 based on the instruction.

[0096] The health management AI node 20-2 may include a success registration unit 205, which may update the success event table 2002 and the success content table 2003 based on the instruction content.

[0097] 12 is an explanatory diagram showing an example of storage of the success DB 2001 after update according to the first embodiment. The success event table 2002 newly stores an objective "diet", an event tag "moderateness", and content "success content table 2003-1" in association with each other. The success content table 2003 newly stores an instruction detail tag "schedule", an attribute tag "office worker", and instruction content "running (run for 30 minutes five times a week)" in association with each other.

[0098] 13 and 14 are flowcharts illustrating an example of an operation of the information processing system 2 according to the first embodiment. The biological information measurement node 20-10 measures biological information (step S231). The biological information measurement node 20-10 notifies the health management AI node 20-2 of the biological information (step S232).

[0099] The personal information node 20-11 acquires the personal information (step S233), and then notifies the health management AI node 20-2 of the personal information (step S234).

[0100] The behavior monitoring node 20-12 acquires the behavior history (step S235), and then notifies the health management AI node 20-2 of the behavior history (step S236).

[0101] The order of steps S231 and S232, steps S233 and S234, and steps S251 and S236 is not particularly limited, and these steps may be performed simultaneously or at different times.

[0102] The health management AI node 20-2 calculates a physical condition barometer based on the acquired information (step S237). The health management AI node 20-2 determines whether or not there is an abnormality in the patient's physical condition based on the physical condition barometer (step S238).

[0103] If it is determined that an abnormality exists (step S238: Yes), health management AI node 20-2 notifies health promotion AI node 20-3 of the abnormality (step S239). In other words, if instructions for the patient have already been notified to behavior monitoring node 20-12, in step S238 health management AI node 20-2 notifies health promotion AI node 20-3 that the patient has failed to diet in accordance with the instructions.

[0104] On the other hand, if it is determined that there is no abnormality (step S238: No), health management AI node 20-2 terminates operation. However, if instructions for the patient have already been notified to behavior monitoring node 20-12, health management AI node 20-2 may notify health promotion AI node 20-3 that the patient has succeeded in dieting in accordance with the instructions. Then, success registration unit 205 of health promotion AI node 20-3 may update success event table 2002 and success content table 2003.

[0105] Next, when the health promotion AI node 20-3 receives a notification that the physical condition barometer is abnormal, it determines whether this is the first notification (step S240).

[0106] If this is the first notification (step S240: Yes), the generation unit 202 of the health promotion AI node 20-3 selects a diet and exercise menu (step S241). The generation unit 202 of the health promotion AI node 20-3 generates instructions based on the selected diet and exercise menu and the physical condition barometer (step S242). The notification unit 203 notifies the behavior monitoring node 20-12 of the instructions (step S243).

[0107] If this is not the first notification (step S240: No), the generation unit 202 of the health promotion AI node 20-3 performs notification processing (step S243). The notification processing shown in Fig. 14 may be the same as the processing from step S201 to step S205 shown in Fig. 5. Note that, for example, in step S205, the notification unit 203 may notify the behavior monitoring node 20-12 of the instruction content.

[0108] Then, upon receiving the instruction content, the behavior monitoring node 20-12 presents the instruction content to the patient node 20-14 (step S245).

[0109] Each node 20 repeats the operations shown in Figures 13 and 14. In this way, the success DB 2001 can be used to improve the appropriateness of the instructions to the patient.

[0110] As described above, in the first embodiment, the health promotion AI node 20-3 generates instructions for health promotion using instructions given when a goal unrelated to health promotion is achieved. This allows the patient's health to be improved. Ultimately, it also helps prevent poor health that the patient is unaware of.

[0111] <Example 2> In Example 2, an example of giving an instruction to predict an illness will be described. The purpose is to predict an illness, and the target is a patient who is a target person who may become ill. The instruction content for predicting an illness is an instruction to the patient to take action. The target may also be a hospital visited by a person who may become ill. In this case, the instruction content is an instruction to the hospital to take action. In Example 2, an example of a purpose of predicting an illness will be described, in which visiting a doctor is taken as an example.

[0112] The information processing system 2 may be the same as the example in FIG. 7, and detailed description thereof will be omitted. In the second embodiment, similarly to the first embodiment, the health management AI node 20-2 has the functional units of the node 20 shown in FIG. 4. In the second embodiment, the health management AI node 20-2 cooperates with the information management AI node 20-7. The biological information measurement node 20-10, the personal information node 20-11, and the behavior monitoring node 20-12 are as described in the first embodiment.

[0113] The personal information node 20-11 may acquire information about the patient's current location from the patient node 20-14. The personal information node 20-11 then notifies the health management AI node 20-2 of the acquired information. The vital information measurement node 20-10 may also acquire vital information related to a heart attack from the patient node 20-14. The vital information measurement node 20-10 notifies the health management AI node 20-2 of the acquired information.

[0114] Furthermore, each of the hospital information management nodes 20-17 and 20-18 notifies the hospital management AI node 20-5 of electronic medical records, hospital availability, and human resources such as doctors.

[0115] The health management AI node 20-2 notifies the information management AI node 20-7 of abnormal biological information. The information management AI node 20-7 notifies the hospital management AI node 20-5 based on the received biological information. The hospital management AI node 20-5 collects the latest information from the hospital information management nodes 20-17 and 20-18. The hospital management AI node 20-5 may search for hospitals that can accept the patient or are appropriate for accepting the patient.

[0116] Meanwhile, the health management AI node 20-2 notifies the patient node 20-14 of the biological abnormality. For example, the health management AI node 20-2 may notify the patient node 20-14 of the biological abnormality via the behavior monitoring node 20-12. The health management AI node 20-2 may also notify the patient node 20-14 of the biological abnormality along with instructions such as "rest."

[0117] Here, health management AI node 20-2 detects a biological abnormality, such as a momentary abnormality in the patient's pulse. Health management AI node 20-2 then notifies patient node 20-14 of an instruction to go to a hospital. Meanwhile, health management AI node 20-2 notifies hospital information management node 20-18 of an instruction to admit the patient with the biological abnormality.

[0118] Following the instruction to the patient node 20-14 to "go to the hospital," the patient headed to the hospital, but was unable to get there. On the other hand, in response to the instruction to the hospital information management node 20-18 to "prepare to receive a patient with an abnormal condition," the patient did not arrive at the hospital at the scheduled time. In this way, the patient failed to see the doctor. Next, an example of the storage of the success DB 2001 used to generate new instruction content in the event of a failure will be explained using FIG. 15.

[0119] 15 is an explanatory diagram illustrating an example of storage in the success DB 2001 according to Example 2. The success DB 2001 stores the success event table 2002 and the success content table 2003, as described above.

[0120] In FIG. 15, the successful event table 2002 stores the purpose "regular medical checkup", the event tag "health management", and the content "success content table 2003-2" in association with each other.

[0121] In Figure 15, the success content table 2003-2 stores the instruction detail tag "schedule," the attribute tag "company employee," the instruction to the hospital "Instruct the hospital to conduct the regular medical checkup at the patient's location," and the instruction to the patient "Instruct the patient not to rush to the medical checkup venue, but to calmly arrive five minutes early." in association with each other.

[0122] In Figure 15, the success content table 2003-2 stores the instruction detail tag "strong will," the attribute tag "self-employed," the instruction to the hospital "instruct the hospital to conduct regular medical checkups," and the instruction to the patient "instruct the patient to decline any sudden invitations to entertain clients and prioritize regular medical checkups." in association with each other.

[0123] In Figure 15, the success content table 2003-2 stores the instruction detail tag "schedule," the attribute tag "student," the instruction to the hospital "Instruct the hospital to conduct regular health checkups," and the instruction to the patient "Instruct the patient to visit the hospital on a weekday afternoon when the hospital is less busy in order to reduce waiting time," in association with each other.

[0124] Fig. 16A is an explanatory diagram (part 1) illustrating an example of updating the failure DB 2011 according to the embodiment 2. Fig. 16B is an explanatory diagram (part 2) illustrating an example of updating the failure DB 2011 according to the embodiment 2. As described above, the failure DB 2011 stores a failure event table 2012, a failure cause table 2014-2, and a failure content table 2013-2.

[0125] As shown in FIG. 16A, the failure registration unit 204 adds the purpose "visit a doctor" and the event tag "health management" to the failure event table 2012 included in the failure DB 2011.

[0126] Then, as shown in Fig. 16B, the failure registration unit 204 generates a failure cause table 2014-2 and a failure content table 2013-2. Specifically, for example, as shown in Fig. 16B, the failure registration unit 204 generates a failure cause table 2014-2 linked to the purpose "visit a doctor" and the event tag "health management," and registers the failure causes in the failure cause table 2014-2.

[0127] Here, the failure registration unit 204 analyzes the patient's behavior history and extracts from the behavior history any behavior that is different from the patient's usual behavior as a failure factor. For example, the failure registration unit 204 analyzes the behavior history and detects that "I immediately walked to the hospital as instructed, but my pulse rate increased and my condition worsened further. I was late for my hospital appointment." Then, the failure registration unit 204 extracts "heart rate," "delay in medical examination," "immediateness," and the like as failure factors. Note that the process for extracting failure factors from the behavior history is not particularly limited. Then, the failure registration unit 204 registers each extracted failure factor and a factor tag that classifies the failure factor in a failure factor table 2014-2. The factor tags for the factors "heart rate," "delay in medical examination," and "immediateness" are "fatigue," "delay," and "schedule," respectively.

[0128] 16, the failure content table 2013-2 stores, in association with one another, an instruction detail tag "visit the hospital" that classifies the details of the instruction, an attribute tag "office worker", an instruction to the hospital "A patient with abnormal health conditions is coming soon, so instruct them to prepare to receive the patient", and an instruction to the patient "The patient appears to be in abnormal health conditions, so instruct them to go to the hospital immediately".

[0129] Next, the extraction unit 201 searches the successful event table 2002 for the event tag "health management" of the "medical examination" that failed this time. Then, the extraction unit 201 identifies the successful content table 2003 associated with the event tag "health management" in the successful event table 2002. In the example of FIG. 15, the successful content table 2003-2 is identified.

[0130] 15, a plurality of instruction contents are registered in the success content table 2003-2. For example, the extraction unit 201 extracts instruction contents from the success content table 2003, the instruction contents having the attribute tag "office worker" of the failure content table 2013-2 and the cause tag of the failure cause table 2014-2 in the attribute tag and the instruction detail tag. In this example, the extraction unit 201 extracts instruction contents having the attribute tag "office worker" and the instruction detail tag "schedule."

[0131] The generating unit 202 generates new instruction contents for the hospital and instruction contents for the patient based on the instruction contents at the time of failure and the extracted instruction contents.

[0132] FIG. 17 is an explanatory diagram illustrating an example of generating new instruction content according to the second embodiment. For example, the generating unit 202 extracts keywords by natural language processing from an instruction content to the hospital, "Instruct the hospital to conduct a regular medical checkup at the patient's location," and an instruction content to the patient, "Instruct the hospital to not rush to the medical checkup venue but to calmly arrive five minutes early." For example, the generating unit 202 extracts "at the patient's location" and "calm down" as keywords. Then, based on the instruction content at the time of failure and the keywords, the generating unit 202 generates new instruction content to the hospital, "Instruct the hospital to rush to the patient's location," and new instruction content to the patient, "Instruct the hospital to calm down, rest, and wait for an ambulance."

[0133] The notification unit 203 notifies the patient node 20-14 and the hospital information management node 20-18 of the generated instruction content.

[0134] If the patient does not visit a hospital after notifying the health management AI node 20-2 of new instruction content, the health management AI node 20-2 may regard the instruction as a failure and generate new instruction content again.

[0135] On the other hand, if the patient visits a hospital after notifying the health management AI node 20-2 of the instruction content, the health management AI node 20-2 considers the instruction to be successful and updates the success DB 2001. The success registration unit 205 of the health management AI node 20-2 updates the success event table 2002 and the success content table 2003 based on the instruction content.

[0136] 18 is an explanatory diagram showing an example of storage in the success DB 2001 after updating according to the second embodiment. The success event table 2002 newly stores, in association with each other, the purpose "visit a doctor," the event tag "health management," and the content "success content table 2003-2." The success content table 2003-2 also newly stores, in association with each other, the instruction detail tag "schedule," the attribute tag "company employee," the instruction content to the hospital "instruct them to rush to the patient's side," and the instruction content to the patient "instruct them to stay calm, rest, and wait for an ambulance."

[0137] As described above, in the second embodiment, the health promotion AI node 20-3 generates instructions for disease prediction using instructions given when another objective unrelated to disease prediction is successful. This facilitates the determination of instructions for predicting a patient's disease. Note that the operation of generating new instructions in the second embodiment may be similar to that described in FIG. 5 and in the first embodiment, and therefore a detailed description using a flowchart will be omitted.

[0138] Example 3 In Example 3, an example of giving instructions regarding employment will be described. The purpose is employment, and the target is a job-hunting person who wants to work. In particular, Example 3 takes as an example employment (job change) for elderly people who make use of their physical abilities. Therefore, the target is elderly people, and the instructions are suggested occupations.

[0139] Specifically, for example, elderly people experience physical declines such as hearing loss, poor eyesight, and reduced physical strength. For this reason, some elderly people may find it difficult to continue their current jobs, resulting in fewer employment opportunities. In this way, it is desirable for individuals to engage in work that suits their physical abilities. However, elderly people may not be aware of their declining physical abilities. It is also difficult to introduce jobs that suit their physical abilities.

[0140] Therefore, in the information processing system 2 according to the third embodiment, an example will be described in which an occupation according to a physical ability index is proposed to an elderly person.

[0141] 19 is an explanatory diagram illustrating an information processing system according to Example 3. The information processing system 2 according to Example 3 may be configured by, for example, a health management AI node 20-2, a base data node 20-1, etc., similar to the information processing system 2 illustrated in FIG.

[0142] Furthermore, the information processing system 2 may be configured, for example, similar to the information processing system 2 shown in Figure 7, with biometric information measurement nodes 20-30, 20-40, personal information nodes 20-31, 20-41, behavior monitoring nodes 20-32, 20-42, etc. for each person such as an elderly person or a salesperson.

[0143] The information processing system 2 according to the third embodiment may be configured with, for example, an elderly node 20-36 and a sales node 20-43 corresponding to elderly people and salespeople, respectively.

[0144] The information processing system 2 according to the third embodiment may include, for example, a human resources management AI node 20-33, a medical support AI node 20-35, and a worker data node 20-34 for managing workers. For example, the worker data node 20-34 has a function of introducing jobs to job seekers.

[0145] In the third embodiment, a case where the human resource management AI node 20-33 has each of the functional units of the node 20 shown in FIG. 4 will be described as an example.

[0146] 20 is an explanatory diagram showing an example of cooperation between a health promotion AI node 20-3 and a human resources management AI node 20-33 according to Example 3. The health management AI node 20-2 acquires the elderly person's biometric information, personal information, and behavioral history by receiving notifications from the biometric information measurement node 20-30, personal information node 20-31, and behavior monitoring node 20-32, respectively.

[0147] For example, personal information may include information about age, occupation, occupational skills, etc. Specifically, for example, personal information may include information such as: 55 years old, 33 years of service, furniture craftsman, mainly produces custom-ordered products, and has excellent customer service skills.

[0148] The biometric information may include information such as eyesight in addition to the examples described in the first embodiment. Specifically, for example, the biometric information may include information such as declining eyesight. Furthermore, the behavioral history may include information indicating the behavior of the elderly person. For example, the behavioral history may include information such as tendency to be late with work.

[0149] Health management AI node 20-2 calculates a health index for the elderly person based on the elderly person's biometric information, behavioral history, personal information, etc. (step S251). The health index is not particularly limited, but may be the degree of vision, hearing, grip strength, etc. The health index may also be information indicating, for example, stress tolerance or communication ability. Furthermore, health management AI node 20-2 may derive information indicating stress tolerance or communication ability based on the results of a diagnostic questionnaire or the like given to the elderly person.

[0150] The health management AI node 20-2 notifies the elderly node 20-36 of the calculated health index. In the example of Figure 19, the health management AI node 20-2 may notify the elderly node 20-36 via the vital information measurement node 20 or the like. The health management AI node 20-2 also notifies the human resource management AI node 20-33 of the calculated health index.

[0151] When the generation unit 202 of the human resource management AI node 20-33 receives the health index of the elderly person, it determines that it has received an instruction to introduce a job to the elderly person.Then, the generation unit 202 of the human resource management AI node 20-33 extracts candidate jobs to introduce to the elderly person from the skill ownership information and job information.The generation unit 202 of the human resource management AI node 20-33 then calculates the health index required to work the extracted job (referred to as the required health index) (step S252).

[0152] The generation unit 202 of the human resource management AI node 20-33 then searches for an occupation that matches the notified health index based on the notified health index and the required health index for each occupation (step S253). The notification unit 203 then notifies the elderly node 20-36 of a suggestion to work in the found occupation as an instruction (step S254). Specifically, for example, in the information processing system 2 of FIG. 19, the notification unit 203 notifies the elderly node 20-36 of the instruction via the worker data node 20-34.

[0153] For example, the instruction here might be, "Introduce a job change to real estate sales, which requires strong customer service skills, has many job postings, and does not require significant eyesight." However, an elderly person may decide that the job change recommended to them is not suitable. In this case, for example, the job change to real estate sales, which was suggested by the human resources management AI node 20-33, was not suitable for the elderly person. In this way, the job introduction may fail. As shown in FIG. 20, for example, the elderly person node 20-36 requests a re-introduction. Specifically, for example, in the information processing system 2 of FIG. 19, the elderly person node 20-36 notifies the human resources management AI node 20-33 of the request for re-introduction via the worker data node 20-34.

[0154] This notification triggers the human resource management AI node 20-33 to propose a new occupation to the elderly. Note that the trigger for the human resource management AI node 20-33 to propose a new occupation to the elderly is not limited to this example, and may include, for example, when it detects that the occupation suggestion has been ignored, or when the elderly node 20-36 receives a notification from another AI node 20 related to the health index other than the occupation suggestion.

[0155] Next, an example of the storage of the success DB 2001 used to generate new instruction content in the event of failure will be described with reference to FIG.

[0156] 21 is an explanatory diagram illustrating an example of storage in the success DB 2001 according to Example 3. The success DB 2001 stores the success event table 2002 and the success content table 2003, as described above.

[0157] In FIG. 21, the successful event table 2002 stores the purpose "eye examination", the event tag "declining vision", and the content "success content table 2003-3" in association with each other.

[0158] In FIG. 21, the success content table 2003-3 stores, in association with each other, the instruction detail tag "Alternative," the attribute tag "Sales," and the instruction content "His eyesight has deteriorated, so he considered wearing glasses, but in order to continue working in sales, a job he is well acquainted with, we suggested using contact lenses as an alternative so as not to change his appearance."

[0159] In Figure 21, the success content table 2003-3 stores the instruction detail tag "treatment", the attribute tag "elderly man", and the instruction content "During a medical checkup, it was discovered that the elderly man's eyesight had deteriorated due to glaucoma, so surgery to prevent the deterioration of eyesight was suggested as a treatment." in correspondence with each other.

[0160] In Figure 21, the success content table 2003-3 stores the instruction detail tag "correction," the attribute tag "student," and the instruction content "The student's physical examination revealed a decline in his eyesight, so we suggested that he have his eyesight corrected using glasses, taking into account cost and convenience." in correspondence with each other.

[0161] For example, the success content table 2003-3 shown in Fig. 21 is registered by the medical support AI node 20-35. In this way, the success content table 2003 registered by another AI node 20 can be used.

[0162] Next, as shown in FIG. 20, the human resources management AI node 20-33 searches for another occupation (step S255).

[0163] Fig. 22A is an explanatory diagram (part 1) illustrating an example of updating the failure DB 2011 according to the embodiment 3. Fig. 22B is an explanatory diagram (part 2) illustrating an example of updating the failure DB 2011 according to the embodiment 3. The failure DB 2011 stores a failure event table 2012, a failure cause table 2014-3, and a failure content table 2013-3.

[0164] As shown in FIG. 22A, the failure registration unit 204 adds the purpose "changing jobs" and the event tag "deteriorating eyesight" to the failure event table 2012 included in the failure DB 2011.

[0165] Then, as shown in Fig. 22B, the failure registration unit 204 generates a failure cause table 2014-3 and a failure content table 2013-3. Specifically, for example, as shown in Fig. 22B, the failure registration unit 204 generates a failure cause table 2014-2 linked to the purpose "changing jobs" and the event tag "deteriorating eyesight", and registers the failure causes in the failure cause table 2014-2.

[0166] Here, the failure registration unit 204 analyzes the behavioral history of the elderly person and extracts from the behavioral history any unusual behavior of the patient as a failure factor. For example, the failure registration unit 204 analyzes the behavioral history and detects that "I didn't get along with my colleagues. The salary was insufficient. The job in an unfamiliar industry was not a good fit." Then, the failure registration unit 204 extracts "I didn't get along with my colleagues," "The salary was insufficient," and "The job was not a good fit" as failure factors. Note that the process for extracting failure factors from the behavioral history is not particularly limited. Then, the failure registration unit 204 registers each extracted failure factor and a factor tag that classifies the failure factor in more detail in a failure factor table 2014-3. Note that in FIG. 22, the factor tags for the factors "I didn't get along with my colleagues," "The salary was insufficient," and "The job was not a good fit" are "Interpersonal Relationships," "Salary," and "Alternatives," respectively.

[0167] Furthermore, the failure registration unit 204 registers the instruction content, attribute tag, and instruction detail tag when the patient fails to receive medical treatment in a failure content table 2013-3. As shown in Fig. 22, the failure content table 2013-3 stores, in association with each other, the instruction detail tag "job change" that classifies the details of the instruction content, the attribute tag "sales," and the instruction content "Introduce a job change to sales in the real estate industry, which has high customer service skills, has many job openings, and does not require much eyesight."

[0168] Next, the extraction unit 201 searches the successful event table 2002 for the event tag "health management" of the "medical examination" that failed this time. Then, the extraction unit 201 identifies the successful content table 2003 associated with the event tag "health management" in the successful event table 2002. In the example of FIG. 21, the successful content table 2003-3 is identified.

[0169] 21, a plurality of instruction contents are registered in the success content table 2003-2. For example, the extraction unit 201 extracts instruction contents from the success content table 2003, the instruction contents having the attribute tag "sales" of the failure content table 2013-3 and the cause tag of the failure cause table 2014-3 in the attribute tag and the instruction detail tag. In this example, the extraction unit 201 extracts instruction contents having the attribute tag "sales" and the instruction detail tag "alternative."

[0170] The generating unit 202 generates new instruction content based on the instruction content at the time of failure, the extracted instruction content, and the personal information.

[0171] FIG. 23 is an explanatory diagram illustrating an example of generating new instruction content according to the third embodiment. For example, the generation unit 202 extracts keywords by natural language processing from the extracted instruction content, "Since his eyesight is getting worse, he considered wearing glasses, but in order to continue working as a salesperson, which he is familiar with, he was suggested to wear contact lenses as an alternative so as not to change his appearance." For example, the generation unit 202 extracts "familiar with" and "alternative" as keywords. Then, the generation unit 202 generates a new instruction content, "Introduce a furniture consultant as an alternative to the furniture-related profession, which he is familiar with," based on the instruction content at the time of failure and the keywords.

[0172] The notification unit 203 notifies the elderly node 20-36 of the generated instruction content.

[0173] If the human resources management AI node 20-33 receives another job introduction request from the elderly node 20-36 after notifying the new instruction content, the human resources management AI node 20-33 may regard the instruction as a failure and generate new instruction content again.

[0174] On the other hand, if the elderly node 20-36 does not send another job introduction request within a predetermined period of time after notifying the personnel management AI node 20-33 of the instruction content, the personnel management AI node 20-33 considers the instruction to be successful and updates the success DB 2001. The success registration unit 205 of the personnel management AI node 20-33 updates the success event table 2002 and the success content table 2003 based on the instruction content.

[0175] 24 is an explanatory diagram showing an example of storage in the success DB 2001 after updating according to the third embodiment. The success event table 2002 newly stores, in association with each other, the purpose "change jobs," the event tag "deteriorating eyesight," and the content "success content table 2003-3." The success content table 2003-3 also newly stores, in association with each other, the instruction detail tag "schedule," the attribute tag "company employee," the instruction content to the hospital "instruct them to rush to the patient's side," and the instruction content to the patient "instruct them to stay calm, rest, and wait for an ambulance."

[0176] As described above, in the third embodiment, the human resource management AI node 20-33 generates instructions for employment using instructions obtained when other objectives unrelated to employment are successful. This facilitates the determination of instructions for employment. Furthermore, according to the third embodiment, it is possible to assist in introducing occupations according to physical abilities. Note that, in the third embodiment, the operation of generating new instructions may be the same as that described in FIG. 5 and the first embodiment, and therefore a detailed description using a flowchart will be omitted.

[0177] This concludes the description of each example. As described above, in the second embodiment, each node 20 generates new instruction content using instruction content generated by other nodes 20, etc. In this way, in the information processing system 2, each node 20 can refer to each other's success stories and can grow together.

[0178] Furthermore, in the example shown, the success content table 2003 includes instruction content in the event of success, but it may also include success factors.

[0179] As mentioned above, event tags may also be a classification of instructions for a goal. As mentioned above, event tags may also be a classification of instructions for a goal and the factors that lead to failure or success when a subject acts according to the instructions. For example, Figure 25 shows an example of event tags that classify factors that lead to failure or success.

[0180] FIG. 25 is an explanatory diagram showing examples of event tags. In FIG. 25, the content refers to the cause of failure or the cause of success. In FIG. 25, (failure) indicates the cause of failure, and (success) indicates the cause of success. As shown in FIG. 25, the same event tag may be assigned when classifying the causes of failure and the causes of success even if the objectives are different.

[0181] This concludes the description of each embodiment. Note that each embodiment and examples of each embodiment may be used in combination.

[0182] Furthermore, the method of realizing each functional unit in each embodiment is not particularly limited. For example, each node may have each functional unit.

[0183] In each embodiment, the information in each DB, table, etc. may include part of the information described above. Furthermore, the information in each DB, table, etc. may include information other than the information described above. There are no particular limitations on how the information in each DB, table, etc. is realized. For example, some of multiple tables may be realized by a single table. Furthermore, each table may be divided into multiple tables in more detail.

[0184] (Computer equipment) Next, an example of a hardware configuration in which each device such as an information processing device, a node, etc. is realized by a computer device will be described. Fig. 26 is an explanatory diagram showing an example of a hardware configuration of a computer device. For example, some or all of each device can be realized using any combination of a computer device 30 and a program as shown in Fig. 26.

[0185] The computer device 30 includes, for example, a processor 301, a read-only memory (ROM) 302, a random access memory (RAM) 303, a storage device 304, a communication interface 305, and an input / output interface 306. Each component is connected to the other via a bus 307.

[0186] The processor 301 controls the entire computer device 30. Examples of the processor 301 include a CPU (Central Processing Unit) and a DSP (Digital Signal Processor). There may be multiple processors 301. The computer device 30 has a storage unit including a ROM 302, a RAM 303, and a storage device 304. Examples of the storage device 304 include a semiconductor memory such as a flash memory, a hard disk drive (HDD), and a solid state drive (SSD). For example, the storage device 304 stores an operating system (OS) program, application programs, and programs according to the embodiments. Alternatively, the ROM 302 stores application programs and programs according to the embodiments. The RAM 303 is used as a work area for the processor 301.

[0187] The processor 301 also loads programs stored in the storage device 304, ROM 302, etc. The processor 301 then executes each process coded in the program. The processor 301 may also download various programs via the communication network 32. The processor 301 also functions as a part or all of the computer device 30. The processor 301 may then execute the processes or instructions in the illustrated flowchart based on the program.

[0188] The communication interface 305 is connected to a communication network 32, such as a LAN (Local Area Network) or WAN (Wide Area Network), via a wireless or wired communication line. This allows the computer device 30 to be connected to external devices and external computers via the communication network 32. The communication interface 305 serves as an interface between the communication network 32 and the inside of the computer device 30. The communication interface 305 also controls the input and output of data from external devices and external computers.

[0189] Furthermore, the input / output interface 306 is connected to at least one of an input device, an output device, and an input / output device. The connection method may be wireless or wired. Examples of the input device include a keyboard, a mouse, and a microphone. Examples of the output device include a display device, a lighting device, and a speaker that outputs sound. Examples of the input / output device include a touch panel display. The input device, output device, and input / output device may be built into the computer device 30 or may be external.

[0190] The hardware configuration of the computer device 30 is an example. The computer device 30 may have some of the components shown in FIG. 26. The computer device 30 may have components other than those shown in FIG. 26. For example, the computer device 30 may have a drive device or the like. The processor 301 may then read programs and data stored in a recording medium attached to the drive device or the like into the RAM 303. Examples of non-transitory tangible recording media include optical disks, flexible disks, magneto-optical disks, and USB (Universal Serial Bus) memories. As described above, the computer device 30 may have input devices such as a keyboard and a mouse. The computer device 30 may have an output device such as a display. The computer device 30 may also have an input device, an output device, and an input / output device.

[0191] The computer device 30 may have various sensors (not shown). The type of the sensors is not particularly limited.

[0192] This concludes the description of the hardware configuration of each device. There are various variations in the implementation of each device. For example, the information processing system may be implemented by any combination of different computers and programs for each component. Furthermore, multiple components included in each device may be implemented by any combination of a single computer and program.

[0193] Furthermore, some or all of the components of each device may be realized by circuits for specific applications. Furthermore, some or all of the information processing system may be realized by general-purpose circuits including a processor such as an FPGA (Field Programmable Gate Array). Furthermore, some or all of the information processing system may be realized by a combination of circuits for specific applications and general-purpose circuits. Furthermore, these circuits may be a single integrated circuit. Alternatively, these circuits may be divided into multiple integrated circuits. Furthermore, the multiple integrated circuits may be configured by being connected via a bus or the like.

[0194] Furthermore, when some or all of the components of each device are realized by a plurality of computers, circuits, etc., the plurality of computers, circuits, etc. may be centrally located or distributed.

[0195] The information processing method described in each embodiment is realized by execution by each device. Also, the information processing method is realized by execution of a prepared program by a computer such as each device. The program described in each embodiment is recorded on a computer-readable recording medium such as an HDD, SSD, flexible disk, optical disk, flexible disk, magneto-optical disk, or USB memory. Then, the program is executed by being read from the recording medium by the computer. Also, the program may be distributed via a communication network 32.

[0196] The functions of each of the components of the information processing system in each embodiment described above may be realized by hardware, such as a computer device, or may be realized by a computer device or firmware under program control.

[0197] Although the present disclosure has been described above with reference to various embodiments, the present disclosure is not limited to the above embodiments. The configuration and details of each of the present disclosures may include embodiments to which various modifications that would be apparent to those skilled in the art are applied within the scope of the present disclosure. The present disclosure may also include embodiments in which the details described herein are appropriately combined or substituted as necessary. For example, details described using a particular embodiment may also be applied to other embodiments to the extent that no contradiction occurs. For example, although multiple operations are described in sequence in the form of a flowchart, the order of the descriptions does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations may be changed as long as it does not interfere with the content. [Explanation of symbols]

[0198] 2. Information Processing Systems 10. Information processing equipment 20 nodes 20-1 Infrastructure Data Node 20-2 Health Management AI Node 20-3 Health Promotion AI Node 20-4 Administrative Procedure AI Node 20-5 Hospital Management AI Node 20-6 Meal management AI node 20-7 Information Management AI Node 20-8 Exercise Management AI Node 20-9 Administrative Procedure Node 20-10 Biometric information measurement node 20-11 Personal Information Node 20-12 Behavior Monitoring Node 20-13 Movement Management Node 20-14 Patient Node 20-15 Instructor node 20-16 Registered Dietitian Node 20-17 Hospital Information Management Node 20-18 Hospital Information Management Node 20-19 Meal Management Node 20-30 Biometric information measurement node 20-31 Personal Information Node 20-32 Behavior Monitoring Node 20-33 Human Resource Management AI Node 20-34 worker data nodes 20-35 Medical Support AI Node 20-36 Elderly Node 20-40 Biometric information measurement node 20-41 Personal Information Node 20-42 Behavior Monitoring Node 20-43 Sales Node 20-n nodes 30 Computer Equipment 32 Communication Network 101,201 Extraction part 102,202 Generation part 103,203 Notification Department 204 Failure Registration Department 205 Successful Registration Department 301 processor 302 ROM 303 RAM 304 Storage device 305 Communication Interface 306 Input / Output Interface 307 Bus 2001 Success DB 2002 Success Events Table 2003, 2003-1, 2003-2, 2003-3 Success Table 2011 failure DB 2012 Failure Event Table 2013, 2013-1, 2013-2, 2013-3 Failure Contents Table 2014, 2014-1, 2014-2, 2014-3 Failure Factor Table

Claims

1. an extraction means for referencing a database in which categories into which instruction contents for each objective are classified are associated with instruction contents for when each objective is successfully achieved, and for extracting instruction contents for when an instruction content for a predetermined objective fails to achieve the predetermined objective, the instruction contents for when the instruction content for the predetermined objective fails to achieve the predetermined objective, which are associated with the same category as the category into which the instruction content for the predetermined objective is classified; a generating means for generating new instruction content for the predetermined purpose based on the extracted instruction content; a notification means for notifying the generated instruction content; An information processing device comprising:

2. a registration means for registering in the database, when the target of the instruction acts in accordance with the notified instruction and succeeds, a category into which the notified instruction is classified and the notified instruction in association with each other; The information processing device according to claim 1 , comprising:

3. The target of the instruction is at least one of a person and a business operator who acts in accordance with the content of the instruction.

3. The information processing device according to claim 1.

4. the predetermined purpose is the promotion of health; The target of the instruction is a person whose health is to be improved, The instruction content for improving the health is a dietary instruction content or an exercise instruction content for the target person, 3. The information processing device according to claim 1.

5. the predetermined purpose is the prediction of disease; The target of the instruction is a target person to whom the disease may occur, The instruction content for predicting the illness is an instruction to the target person to take action.

3. The information processing device according to claim 1.

6. the predetermined purpose is the prediction of disease; The instruction is directed to a hospital attended by a person who may develop the disease, The instruction content for the prediction of the illness is an instruction to the hospital.

3. The information processing device according to claim 1.

7. The predetermined purpose is employment, The target of the instruction is the person who will be employed, The instruction content for the employment is a job suggestion content for the target person, 3. The information processing device according to claim 1.

8. Multiple nodes, Equipped with Any one of the plurality of nodes is an extraction means for referencing a database in which categories into which instruction contents for each objective are classified are associated with instruction contents for when each objective is successfully achieved, and for extracting instruction contents for when an instruction content for a predetermined objective fails to achieve the predetermined objective, the instruction contents for when the instruction content for the predetermined objective fails to achieve the predetermined objective, which are associated with the same category as the category into which the instruction content for the predetermined objective is classified; a generating means for generating new instruction content for the predetermined purpose based on the extracted instruction content; a notification means for notifying the generated instruction content; Equipped with Any one of the plurality of nodes is a registration means for registering the instruction content and the category into which the instruction content generated by the node is classified in the database when the instruction content has succeeded in achieving the purpose of the instruction content; Equipped with Information processing system.

9. A computer comprising: a database in which the instruction content for each objective is associated with the instruction content for when the objective is successfully achieved, for each category into which the instruction content for each objective is classified, and when the instruction content for a predetermined objective fails to achieve the predetermined objective, extracts the instruction content for when the objective is successful, which is associated with the same category as the category into which the instruction content for the predetermined objective is classified; generating new instruction content for the predetermined purpose based on the extracted instruction content; notifying the generated instruction content; An information processing method that performs processing.

10. On the computer, a database in which the instruction content for each objective is associated with the instruction content for when the objective is successfully achieved, for each category into which the instruction content for each objective is classified, and when the instruction content for a predetermined objective fails to achieve the predetermined objective, extracts the instruction content for when the objective is successful, which is associated with the same category as the category into which the instruction content for the predetermined objective is classified; generating new instruction content for the predetermined purpose based on the extracted instruction content; notifying the generated instruction content; A program that executes a process.

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