System

Abstract

An object of the system according to the embodiment is to provide an environment in which an employee can comfortably consult with a private worry or anxiety.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an introduction unit. The reception unit receives consultation contents of the employee. The analysis unit analyzes the consultation content received by the reception unit. The providing unit provides advice or support based on the content analyzed by the analyzing unit. The introduction unit introduces a specialist on the basis of the advice or support provided by the provision unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for employees to discuss their personal worries or anxieties within the company.

[0005] The system according to the embodiment aims to provide an environment in which employees can feel safe to consult about their private worries and anxieties. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an introduction unit. The reception unit receives the consultation content of the employee. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides advice or support based on the content analyzed by the analysis unit. The introduction unit introduces an expert based on the advice or support provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which employees can feel safe discussing their private worries and anxieties. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An AI social worker system according to an embodiment of the present invention allows employees to consult with confidence about their personal worries and anxieties. In the AI ​​social worker system, employees input their consultation details, and an AI social worker analyzes the details and provides appropriate advice and support. This mechanism allows employees to consult with confidence about private issues that are difficult to discuss within the company. For example, if an employee consults about a family member's disability, dementia, or financial concerns, the AI ​​social worker analyzes the details and suggests appropriate support measures, thereby alleviating the employee's anxiety. This allows the AI ​​social worker system to provide appropriate advice and support while protecting the employee's privacy. For example, if an employee consults about a family member's disability or dementia, the AI ​​social worker analyzes the details and suggests appropriate support measures, thereby alleviating the employee's anxiety.

[0029] The AI ​​social worker system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an introduction unit. The reception unit receives consultation content from employees. The consultation content from employees includes, but is not limited to, family disabilities, dementia, and financial concerns. The reception unit, for example, receives the consultation content entered by the employees in text format. The reception unit can also receive the consultation content using voice input or image input. For example, if the employee inputs the consultation content by voice, the reception unit converts the voice data into text data and accepts it. The analysis unit analyzes the consultation content received by the reception unit. The analysis unit, for example, uses text analysis technology to analyze the consultation content and understand the employee's consultation content. The analysis unit can also analyze the employee's emotions using emotion analysis technology. For example, the analysis unit analyzes the text data of the consultation content and estimates the employee's emotions. The provision unit provides appropriate advice and support based on the content analyzed by the analysis unit. The provision unit, for example, proposes care methods for a family member with dementia and support measures for financial concerns. The providing unit can also provide advice according to the employee's emotions. For example, if the employee is feeling stressed, the providing unit provides advice to help the employee relax. The referral unit introduces an expert as needed based on the advice and support provided by the providing unit. For example, the referral unit introduces an expert who provides specialized care services for family members with dementia or counseling for financial concerns. In this way, the AI ​​social worker system according to the embodiment can provide an environment where employees can feel safe consulting about their private worries and anxieties.

[0030] The reception unit can accept private worries and anxieties entered by employees, such as a family member's disability or dementia, or financial worries. For example, an employee can enter content such as, "My family member has dementia and needs care, but I don't know what to do." In addition, when an employee enters their consultation content by voice, the reception unit can convert the voice data into text data and accept it. This allows employees to consult about specific private worries and anxieties.

[0031] The analysis unit analyzes the consultation content received by the reception unit and can understand the consultation content of the employee. The analysis unit analyzes the consultation content received by the reception unit using, for example, text analysis technology. For example, the analysis unit analyzes text data of the consultation content and understands the consultation content of the employee. The analysis unit can also analyze the emotions of the employee using emotion analysis technology. For example, the analysis unit analyzes the text data of the consultation content and estimates the emotions of the employee. This makes it possible to accurately understand the consultation content of the employee.

[0032] The provision unit can propose care methods for family members with dementia or support measures for financial anxiety based on the content analyzed by the analysis unit. The provision unit, for example, proposes care methods for family members with dementia based on the content analyzed by the analysis unit. For example, the provision unit proposes methods of supporting daily life for family members with dementia or dementia care techniques. The provision unit can also propose support measures for financial anxiety based on the content analyzed by the analysis unit. For example, the provision unit proposes financial support, psychological support, legal support, etc. This makes it possible to provide specific advice and support measures to employees.

[0033] The referral department can introduce a specialist who provides specialized nursing care services or counseling based on the advice and support provided by the provision department. The referral department, for example, introduces a specialist who provides specialized nursing care services based on the advice and support provided by the provision department. For example, the referral department introduces a specialist who provides specialized nursing care services such as home care, day care, and rehabilitation for family members with dementia. The referral department can also introduce a specialist who provides counseling based on the advice and support provided by the provision department. For example, the referral department introduces a specialist who provides individual counseling, group counseling, online counseling, etc. for financial anxiety. This makes it possible to provide specialized support to employees.

[0034] The reception unit can analyze the employee's past consultation history and select the reception method. The reception unit, for example, analyzes the employee's past consultation history and selects the optimal reception method. For example, it prioritizes the reception method that the employee has frequently used in the past. It can also select the optimal reception method for a specific time period based on the employee's past consultation history. It can also customize an appropriate reception method based on the content of the employee's past consultation. This makes it possible to provide the optimal reception method based on the employee's past consultation history.

[0035] The reception unit can filter the consultation content based on the employee's current living situation and areas of interest when receiving the consultation content. The reception unit, for example, filters the consultation content based on the employee's current living situation and areas of interest when receiving the consultation content. For example, the reception unit preferentially receives relevant consultation content based on the employee's current living situation. It can also filter appropriate consultation content based on the employee's areas of interest. It can also filter to provide appropriate advice based on the employee's living situation and areas of interest. This makes it possible to receive appropriate consultation content based on the employee's living situation and areas of interest.

[0036] The reception unit can select a reception means according to the input method of the employee when receiving the consultation content. For example, the reception unit selects a reception means according to the input method of the employee when receiving the consultation content. For example, if the employee desires voice input, the reception unit can prioritize voice input. Also, if the employee desires text input, the reception unit can prioritize text input. Also, if the employee desires consultation using images, the reception unit can prioritize image input. This makes it possible to provide the optimal reception means according to the input method of the employee.

[0037] The reception unit can prioritize reception of highly relevant consultations based on the employee's geographical location information when receiving consultation content. For example, when receiving consultation content, the reception unit prioritizes reception of highly relevant consultations based on the employee's geographical location information. For example, if an employee is in a specific area, the reception unit prioritizes reception of consultation content related to that area. Furthermore, the reception unit can prioritize reception of consultations regarding issues specific to the area based on the employee's geographical location information. Furthermore, if an employee is traveling, the reception unit can prioritize reception of consultation content related to the employee's current location. In this way, it is possible to prioritize reception of highly relevant consultation content based on the employee's geographical location information.

[0038] The reception unit can analyze the employee's social media activity when receiving a consultation content and receive related consultations. For example, the reception unit analyzes the employee's social media activity when receiving a consultation content and receive related consultations. For example, the reception unit analyzes the employee's social media posts and receives related consultation content on a priority basis. The reception unit can also suggest appropriate consultation content based on the employee's social media activity history. The reception unit can also receive related consultation content by taking into account the activities of the employee's friends on social media. In this way, it is possible to receive related consultation content based on the employee's social media activity.

[0039] The reception unit can customize the reception method by reflecting the employee's past feedback when receiving the consultation content. The reception unit, for example, customizes the reception method by reflecting the employee's past feedback when receiving the consultation content. For example, the reception unit can suggest the optimal reception method based on the employee's past feedback. It can also preferentially select a specific reception method based on the employee's past feedback. It can also customize the reception method by reflecting the employee's past feedback. This makes it possible to provide the optimal reception method based on the employee's past feedback.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the consultation content during analysis. For example, a detailed analysis is performed for consultation content with a high level of importance. Also, a concise analysis can be performed for consultation content with a low level of importance. Furthermore, the level of detail of the analysis can be adjusted in stages depending on the importance. This makes it possible to provide a detailed analysis according to the importance of the consultation content.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the consultation content during analysis. For example, an analysis algorithm based on economic data can be applied to a consultation content about financial anxiety. An analysis algorithm based on medical data can also be applied to a consultation content about dementia. An analysis algorithm based on welfare data can also be applied to a consultation content about a family member's disability. This makes it possible to provide the optimal analysis algorithm depending on the category of the consultation content.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results during the analysis. For example, the analysis unit can analyze the current consultation content based on the employee's past analysis results. It can also extract patterns for improving the accuracy of the analysis from the employee's past analysis results. It can also optimize the analysis algorithm by referring to the employee's past analysis results. This makes it possible to improve the accuracy of the analysis based on the employee's past analysis results.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the consultation contents during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the consultation contents during analysis. For example, consultation contents with high relevance are analyzed preferentially. Also, consultation contents with low relevance can be analyzed later. Also, the order of analysis can be adjusted in stages according to the relevance of the consultation contents. This allows analysis to be performed preferentially based on the relevance of the consultation contents.

[0044] The analysis unit can adjust the use of technical terms in the analysis according to the employee's level of expertise during analysis. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the employee's level of expertise during analysis. For example, if the employee's level of expertise is high, the analysis can be performed using a lot of technical terms. On the other hand, if the employee's level of expertise is low, the analysis can be performed without using technical terms. The use of technical terms in the analysis can also be adjusted in stages according to the employee's level of expertise. This makes it possible to provide appropriate analysis results according to the employee's level of expertise.

[0045] The providing unit can adjust the level of detail of the advice based on the importance of the consultation content when providing the advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the consultation content when providing the advice. For example, detailed advice is provided for consultation content with a high level of importance. Also, concise advice can be provided for consultation content with a low level of importance. Furthermore, the level of detail of the advice can be adjusted in stages depending on the importance. This makes it possible to provide detailed advice according to the importance of the consultation content.

[0046] The providing unit can apply different advice algorithms depending on the category of the consultation content when providing the advice. For example, the providing unit applies different advice algorithms depending on the category of the consultation content when providing the advice. For example, an advice algorithm based on economic data can be applied to a consultation content about financial anxiety. Also, an advice algorithm based on medical data can be applied to a consultation content about dementia. Also, an advice algorithm based on welfare data can be applied to a consultation content about a family member's disability. This makes it possible to provide the optimal advice algorithm depending on the category of the consultation content.

[0047] The providing unit can improve the accuracy of advice by referring to the employee's past advice results when providing the advice. The providing unit, for example, improves the accuracy of advice by referring to the employee's past advice results when providing the advice. For example, the providing unit provides advice on the current consultation content based on the employee's past advice results. In addition, patterns for improving the accuracy of advice can be extracted from the employee's past advice results. In addition, the advice algorithm can be optimized by referring to the employee's past advice results. This makes it possible to improve the accuracy of advice based on the employee's past advice results.

[0048] The providing unit can determine the priority of advice based on the time of submission of the consultation content when providing the advice. For example, the providing unit determines the priority of advice based on the time of submission of the consultation content when providing the advice. For example, advice is given priority to advice submitted earlier than the time of submission of the consultation content. Also, advice can be given later than advice submitted later than the time of submission of the consultation content. Also, the priority of advice can be adjusted in stages depending on the time of submission. This makes it possible to provide advice preferentially based on the time of submission of the consultation content.

[0049] The providing unit can adjust the order of advice based on the relevance of the consultation content when providing the advice. For example, the providing unit adjusts the order of advice based on the relevance of the consultation content when providing the advice. For example, advice with a high relevance to the consultation content is given priority. Also, advice with a low relevance to the consultation content can be given later. Also, the order of advice can be adjusted in stages depending on the relevance of the consultation content. In this way, advice can be given priority based on the relevance of the consultation content.

[0050] The providing unit can adjust the use of technical terms in the advice depending on the employee's level of expertise when providing the advice. For example, the providing unit can adjust the use of technical terms in the advice depending on the employee's level of expertise when providing the advice. For example, if the employee's level of expertise is high, advice that uses a lot of technical terms can be provided. Also, if the employee's level of expertise is low, advice that avoids technical terms can be provided. Furthermore, the use of technical terms in the advice can be gradually adjusted depending on the employee's level of expertise. This makes it possible to provide appropriate advice depending on the employee's level of expertise.

[0051] The introduction unit can adjust the order of expert introduction based on the importance of the consultation content at the time of introduction. For example, the introduction unit adjusts the order of expert introduction based on the importance of the consultation content at the time of introduction. For example, an expert can be introduced preferentially for consultation content with a high importance. Also, an expert can be introduced later for consultation content with a low importance. Also, the order of expert introduction can be adjusted in stages depending on the importance. This makes it possible to introduce an expert preferentially depending on the importance of the consultation content.

[0052] The referral unit can introduce different experts depending on the category of the consultation content at the time of referral. For example, the referral unit can introduce different experts depending on the category of the consultation content at the time of referral. For example, an economic expert can be introduced for a consultation content regarding financial anxiety. A medical expert can also be introduced for a consultation content regarding dementia. A welfare expert can also be introduced for a consultation content regarding a disability of a family member. This makes it possible to introduce the most appropriate expert depending on the category of the consultation content.

[0053] The referral unit can improve the accuracy of expert selection by referring to the employee's past referral results when making a referral. For example, the referral unit can improve the accuracy of expert selection by referring to the employee's past referral results when making a referral. For example, the referral unit selects an expert for the current consultation content based on the employee's past referral results. In addition, patterns for improving the accuracy of expert selection can be extracted from the employee's past referral results. In addition, the expert selection algorithm can be optimized by referring to the employee's past referral results. This makes it possible to improve the accuracy of expert selection based on the employee's past referral results.

[0054] The introduction unit can adjust the order of expert introduction based on the time of submission of the consultation content at the time of introduction. For example, the introduction unit can adjust the order of expert introduction based on the time of submission of the consultation content at the time of introduction. For example, the introduction unit can prioritize experts who have submitted their consultation content earlier. Also, the introduction unit can postpone the introduction of experts who have submitted their consultation content later. Also, the introduction order of experts can be adjusted in stages depending on the time of submission. This makes it possible to prioritize experts based on the time of submission of the consultation content.

[0055] The introduction unit can select an expert based on the relevance of the consultation content at the time of introduction. The introduction unit, for example, selects an expert based on the relevance of the consultation content at the time of introduction. For example, the introduction unit can prioritize experts who are highly relevant to the consultation content. Also, experts who are less relevant to the consultation content can be introduced later. Also, the selection of experts can be adjusted in stages depending on the relevance of the consultation content. This makes it possible to select the most appropriate expert based on the relevance of the consultation content.

[0056] The introduction department can adjust the content of the expert introduction according to the employee's level of expertise when making the introduction. For example, the introduction department can adjust the content of the expert introduction according to the employee's level of expertise when making the introduction. For example, if the employee's level of expertise is high, the introduction can include specialized content. On the other hand, if the employee's level of expertise is low, the introduction can include easy-to-understand content. The introduction content can also be adjusted in stages according to the employee's level of expertise. This makes it possible to introduce an appropriate expert according to the employee's level of expertise.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The AI ​​social worker system may further include a feedback unit. The feedback unit collects feedback from employees regarding the advice and support they have received and provides it to the analysis unit. For example, the employee may rate their satisfaction with the advice provided. The feedback unit may also report the results of the employee's implementation of the advice. This allows the analysis unit to improve the accuracy of the advice based on the feedback. Furthermore, the feedback unit may adjust the content of the advice provided by the advice providing unit based on the employee's feedback. This allows the provision of more appropriate advice that meets the employee's needs.

[0059] The AI ​​social worker system can further be equipped with a learning unit. The learning unit trains the entire system based on the consultation content and feedback from employees. For example, the learning unit can analyze patterns in the consultation content of employees and predict future consultation content. The learning unit can also evaluate the effectiveness of advice based on employee feedback and use this to improve the system. This allows the AI ​​social worker system to continuously learn and improve the quality of support provided to employees. Furthermore, the learning unit can also propose new support measures based on the consultation content of employees. This makes it possible to respond to the diverse needs of employees.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts the employee's consultation content. Employee consultation content may include, but is not limited to, family disability, dementia, and financial anxiety. The reception unit accepts the consultation content entered by the employee in text format. Consultation content can also be accepted using voice input or image input. For example, if the employee enters the consultation content by voice, the reception unit converts the voice data into text data and accepts it. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis unit uses text analysis technology to analyze the consultation content and understand the content of the employee's consultation. It can also analyze the employee's emotions using emotion analysis technology. For example, it analyzes the text data of the consultation content and estimates the employee's emotions. Step 3: The provider provides appropriate advice and support based on the results of the analysis. For example, it can suggest care methods for a family member with dementia or support measures for financial concerns. It can also provide advice based on the employee's emotions. For example, if an employee is feeling stressed, it can provide advice to help them relax. Step 4: Based on the advice and support provided by the provider, the referral unit will refer the patient to a specialist if necessary, for example, a specialist providing specialized care services for family members with dementia or counselling for financial concerns.

[0062] (Example 2) An AI social worker system according to an embodiment of the present invention allows employees to consult with confidence about their personal worries and anxieties. In the AI ​​social worker system, employees input their consultation details, and an AI social worker analyzes the details and provides appropriate advice and support. This mechanism allows employees to consult with confidence about private issues that are difficult to discuss within the company. For example, if an employee consults about a family member's disability, dementia, or financial concerns, the AI ​​social worker analyzes the details and suggests appropriate support measures, thereby alleviating the employee's anxiety. This allows the AI ​​social worker system to provide appropriate advice and support while protecting the employee's privacy. For example, if an employee consults about a family member's disability or dementia, the AI ​​social worker analyzes the details and suggests appropriate support measures, thereby alleviating the employee's anxiety.

[0063] The AI ​​social worker system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an introduction unit. The reception unit receives consultation content from employees. The consultation content from employees includes, but is not limited to, family disabilities, dementia, and financial concerns. The reception unit, for example, receives the consultation content entered by the employees in text format. The reception unit can also receive the consultation content using voice input or image input. For example, if the employee inputs the consultation content by voice, the reception unit converts the voice data into text data and accepts it. The analysis unit analyzes the consultation content received by the reception unit. The analysis unit, for example, uses text analysis technology to analyze the consultation content and understand the employee's consultation content. The analysis unit can also analyze the employee's emotions using emotion analysis technology. For example, the analysis unit analyzes the text data of the consultation content and estimates the employee's emotions. The provision unit provides appropriate advice and support based on the content analyzed by the analysis unit. The provision unit, for example, proposes care methods for a family member with dementia and support measures for financial concerns. The providing unit can also provide advice according to the employee's emotions. For example, if the employee is feeling stressed, the providing unit provides advice to help the employee relax. The referral unit introduces an expert as needed based on the advice and support provided by the providing unit. For example, the referral unit introduces an expert who provides specialized care services for family members with dementia or counseling for financial concerns. In this way, the AI ​​social worker system according to the embodiment can provide an environment where employees can feel safe consulting about their private worries and anxieties.

[0064] The reception unit can accept private worries and anxieties entered by employees, such as a family member's disability or dementia, or financial worries. For example, an employee can enter content such as, "My family member has dementia and needs care, but I don't know what to do." In addition, when an employee enters their consultation content by voice, the reception unit can convert the voice data into text data and accept it. This allows employees to consult about specific private worries and anxieties.

[0065] The analysis unit analyzes the consultation content received by the reception unit and can understand the consultation content of the employee. The analysis unit analyzes the consultation content received by the reception unit using, for example, text analysis technology. For example, the analysis unit analyzes text data of the consultation content and understands the consultation content of the employee. The analysis unit can also analyze the emotions of the employee using emotion analysis technology. For example, the analysis unit analyzes the text data of the consultation content and estimates the emotions of the employee. This makes it possible to accurately understand the consultation content of the employee.

[0066] The provision unit can propose care methods for family members with dementia or support measures for financial anxiety based on the content analyzed by the analysis unit. The provision unit, for example, proposes care methods for family members with dementia based on the content analyzed by the analysis unit. For example, the provision unit proposes methods of supporting daily life for family members with dementia or dementia care techniques. The provision unit can also propose support measures for financial anxiety based on the content analyzed by the analysis unit. For example, the provision unit proposes financial support, psychological support, legal support, etc. This makes it possible to provide specific advice and support measures to employees.

[0067] The referral department can introduce a specialist who provides specialized nursing care services or counseling based on the advice and support provided by the provision department. The referral department, for example, introduces a specialist who provides specialized nursing care services based on the advice and support provided by the provision department. For example, the referral department introduces a specialist who provides specialized nursing care services such as home care, day care, and rehabilitation for family members with dementia. The referral department can also introduce a specialist who provides counseling based on the advice and support provided by the provision department. For example, the referral department introduces a specialist who provides individual counseling, group counseling, online counseling, etc. for financial anxiety. This makes it possible to provide specialized support to employees.

[0068] The reception unit can estimate the employee's emotions and adjust the timing of accepting consultation content based on the estimated employee emotions. The reception unit, for example, estimates the employee's emotions and adjusts the timing of accepting consultation content based on the estimated employee emotions. For example, if an employee is feeling stressed, the reception unit can accept consultation content during a time when the employee is able to relax. Also, if an employee is nervous, the reception unit can provide a sense of security by accepting consultation content immediately. Also, if an employee is relaxed, the reception unit can take time to accept detailed consultation content. This makes it possible to accept consultation content at an appropriate time according to the employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the employee's past consultation history and select the reception method. The reception unit, for example, analyzes the employee's past consultation history and selects the optimal reception method. For example, it prioritizes the reception method that the employee has frequently used in the past. It can also select the optimal reception method for a specific time period based on the employee's past consultation history. It can also customize an appropriate reception method based on the content of the employee's past consultation. This makes it possible to provide the optimal reception method based on the employee's past consultation history.

[0070] The reception unit can filter the consultation content based on the employee's current living situation and areas of interest when receiving the consultation content. The reception unit, for example, filters the consultation content based on the employee's current living situation and areas of interest when receiving the consultation content. For example, the reception unit preferentially receives relevant consultation content based on the employee's current living situation. It can also filter appropriate consultation content based on the employee's areas of interest. It can also filter to provide appropriate advice based on the employee's living situation and areas of interest. This makes it possible to receive appropriate consultation content based on the employee's living situation and areas of interest.

[0071] The reception unit can select a reception means according to the input method of the employee when receiving the consultation content. For example, the reception unit selects a reception means according to the input method of the employee when receiving the consultation content. For example, if the employee desires voice input, the reception unit can prioritize voice input. Also, if the employee desires text input, the reception unit can prioritize text input. Also, if the employee desires consultation using images, the reception unit can prioritize image input. This makes it possible to provide the optimal reception means according to the input method of the employee.

[0072] The reception unit can estimate the employee's emotions and determine the priority of the consultation contents to be received based on the estimated employee emotions. The reception unit, for example, estimates the employee's emotions and determines the priority of the consultation contents to be received based on the estimated employee emotions. For example, if the employee is feeling stressed, the reception unit can prioritize consultation contents with high urgency. Also, if the employee is relaxed, the reception unit can prioritize consultation contents with detailed information. Also, if the employee is feeling anxious, the reception unit can quickly accept consultation contents to provide a sense of security. In this way, the consultation contents can be received in priority order according to the employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can prioritize reception of highly relevant consultations based on the employee's geographical location information when receiving consultation content. For example, when receiving consultation content, the reception unit prioritizes reception of highly relevant consultations based on the employee's geographical location information. For example, if an employee is in a specific area, the reception unit prioritizes reception of consultation content related to that area. Furthermore, the reception unit can prioritize reception of consultations regarding issues specific to the area based on the employee's geographical location information. Furthermore, if an employee is traveling, the reception unit can prioritize reception of consultation content related to the employee's current location. In this way, it is possible to prioritize reception of highly relevant consultation content based on the employee's geographical location information.

[0074] The reception unit can analyze the employee's social media activity when receiving a consultation content and receive related consultations. For example, the reception unit analyzes the employee's social media activity when receiving a consultation content and receive related consultations. For example, the reception unit analyzes the employee's social media posts and receives related consultation content on a priority basis. The reception unit can also suggest appropriate consultation content based on the employee's social media activity history. The reception unit can also receive related consultation content by taking into account the activities of the employee's friends on social media. In this way, it is possible to receive related consultation content based on the employee's social media activity.

[0075] The reception unit can customize the reception method by reflecting the employee's past feedback when receiving the consultation content. The reception unit, for example, customizes the reception method by reflecting the employee's past feedback when receiving the consultation content. For example, the reception unit can suggest the optimal reception method based on the employee's past feedback. It can also preferentially select a specific reception method based on the employee's past feedback. It can also customize the reception method by reflecting the employee's past feedback. This makes it possible to provide the optimal reception method based on the employee's past feedback.

[0076] The analysis unit can estimate the employee's emotions and adjust the way the analysis is expressed based on the estimated employee's emotions. The analysis unit, for example, estimates the employee's emotions and adjusts the way the analysis is expressed based on the estimated employee's emotions. For example, if the employee is feeling stressed, a simple and easy-to-understand expression is used. If the employee is relaxed, detailed analysis results can be provided. If the employee is feeling anxious, an expression that gives a sense of security can be used. This makes it possible to provide analysis results in an appropriate expression method according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the consultation content during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the consultation content during analysis. For example, a detailed analysis is performed for consultation content with a high level of importance. Also, a concise analysis can be performed for consultation content with a low level of importance. Furthermore, the level of detail of the analysis can be adjusted in stages depending on the importance. This makes it possible to provide a detailed analysis according to the importance of the consultation content.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the consultation content during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the consultation content during analysis. For example, an analysis algorithm based on economic data can be applied to a consultation content about financial anxiety. An analysis algorithm based on medical data can also be applied to a consultation content about dementia. An analysis algorithm based on welfare data can also be applied to a consultation content about a family member's disability. This makes it possible to provide the optimal analysis algorithm depending on the category of the consultation content.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results during the analysis. For example, the analysis unit can analyze the current consultation content based on the employee's past analysis results. It can also extract patterns for improving the accuracy of the analysis from the employee's past analysis results. It can also optimize the analysis algorithm by referring to the employee's past analysis results. This makes it possible to improve the accuracy of the analysis based on the employee's past analysis results.

[0080] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee emotions. The analysis unit, for example, estimates the employee's emotions and adjusts the length of the analysis based on the estimated employee emotions. For example, if the employee is in a hurry, a short and to-the-point analysis can be performed. If the employee is relaxed, a detailed analysis can be performed. If the employee is feeling anxious, a moderately long analysis can be performed to provide a sense of security. This makes it possible to provide analysis results of an appropriate length according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the consultation contents during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the consultation contents during analysis. For example, consultation contents with high relevance are analyzed preferentially. Also, consultation contents with low relevance can be analyzed later. Also, the order of analysis can be adjusted in stages according to the relevance of the consultation contents. This allows analysis to be performed preferentially based on the relevance of the consultation contents.

[0082] The analysis unit can adjust the use of technical terms in the analysis according to the employee's level of expertise during analysis. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the employee's level of expertise during analysis. For example, if the employee's level of expertise is high, the analysis can be performed using a lot of technical terms. On the other hand, if the employee's level of expertise is low, the analysis can be performed without using technical terms. The use of technical terms in the analysis can also be adjusted in stages according to the employee's level of expertise. This makes it possible to provide appropriate analysis results according to the employee's level of expertise.

[0083] The providing unit can estimate the employee's emotions and adjust the way in which advice is expressed based on the estimated employee emotions. The providing unit, for example, estimates the employee's emotions and adjusts the way in which advice is expressed based on the estimated employee emotions. For example, if the employee is feeling stressed, simple and easy-to-understand advice can be provided. If the employee is relaxed, detailed advice can be provided. If the employee is feeling anxious, advice that gives a sense of security can be provided. This makes it possible to provide advice in an appropriate way according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The providing unit can adjust the level of detail of the advice based on the importance of the consultation content when providing the advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the consultation content when providing the advice. For example, detailed advice is provided for consultation content with a high level of importance. Also, concise advice can be provided for consultation content with a low level of importance. Furthermore, the level of detail of the advice can be adjusted in stages depending on the importance. This makes it possible to provide detailed advice according to the importance of the consultation content.

[0085] The providing unit can apply different advice algorithms depending on the category of the consultation content when providing the advice. For example, the providing unit applies different advice algorithms depending on the category of the consultation content when providing the advice. For example, an advice algorithm based on economic data can be applied to a consultation content about financial anxiety. Also, an advice algorithm based on medical data can be applied to a consultation content about dementia. Also, an advice algorithm based on welfare data can be applied to a consultation content about a family member's disability. This makes it possible to provide the optimal advice algorithm depending on the category of the consultation content.

[0086] The providing unit can improve the accuracy of advice by referring to the employee's past advice results when providing the advice. The providing unit, for example, improves the accuracy of advice by referring to the employee's past advice results when providing the advice. For example, the providing unit provides advice on the current consultation content based on the employee's past advice results. In addition, patterns for improving the accuracy of advice can be extracted from the employee's past advice results. In addition, the advice algorithm can be optimized by referring to the employee's past advice results. This makes it possible to improve the accuracy of advice based on the employee's past advice results.

[0087] The providing unit can estimate the employee's emotions and adjust the length of the advice to be provided based on the estimated employee emotions. The providing unit, for example, estimates the employee's emotions and adjusts the length of the advice to be provided based on the estimated employee emotions. For example, if the employee is in a hurry, short and to the point advice can be provided. If the employee is relaxed, detailed advice can be provided. If the employee is feeling anxious, advice of an appropriate length can be provided to give a sense of security. This makes it possible to provide advice of an appropriate length according to the employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] The providing unit can determine the priority of advice based on the time of submission of the consultation content when providing the advice. For example, the providing unit determines the priority of advice based on the time of submission of the consultation content when providing the advice. For example, advice is given priority to advice submitted earlier than the time of submission of the consultation content. Also, advice can be given later than advice submitted later than the time of submission of the consultation content. Also, the priority of advice can be adjusted in stages depending on the time of submission. This makes it possible to provide advice preferentially based on the time of submission of the consultation content.

[0089] The providing unit can adjust the order of advice based on the relevance of the consultation content when providing the advice. For example, the providing unit adjusts the order of advice based on the relevance of the consultation content when providing the advice. For example, advice with a high relevance to the consultation content is given priority. Also, advice with a low relevance to the consultation content can be given later. Also, the order of advice can be adjusted in stages depending on the relevance of the consultation content. In this way, advice can be given priority based on the relevance of the consultation content.

[0090] The providing unit can adjust the use of technical terms in the advice depending on the employee's level of expertise when providing the advice. For example, the providing unit can adjust the use of technical terms in the advice depending on the employee's level of expertise when providing the advice. For example, if the employee's level of expertise is high, advice that uses a lot of technical terms can be provided. Also, if the employee's level of expertise is low, advice that avoids technical terms can be provided. Furthermore, the use of technical terms in the advice can be gradually adjusted depending on the employee's level of expertise. This makes it possible to provide appropriate advice depending on the employee's level of expertise.

[0091] The introduction unit can estimate the employee's emotions and select an expert to introduce based on the estimated employee emotions. The introduction unit, for example, estimates the employee's emotions and selects an expert to introduce based on the estimated employee emotions. For example, if the employee is feeling stressed, the introduction unit can introduce an expert who can help the employee relax. Also, if the employee is feeling anxious, the introduction unit can introduce an expert who gives the employee a sense of security. Also, if the employee is relaxed, the introduction unit can introduce an expert who can provide detailed consultation. This makes it possible to introduce an appropriate expert according to the employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The introduction unit can adjust the order of expert introduction based on the importance of the consultation content at the time of introduction. For example, the introduction unit adjusts the order of expert introduction based on the importance of the consultation content at the time of introduction. For example, an expert can be introduced preferentially for consultation content with a high importance. Also, an expert can be introduced later for consultation content with a low importance. Also, the order of expert introduction can be adjusted in stages depending on the importance. This makes it possible to introduce an expert preferentially depending on the importance of the consultation content.

[0093] The referral unit can introduce different experts depending on the category of the consultation content at the time of referral. For example, the referral unit can introduce different experts depending on the category of the consultation content at the time of referral. For example, an economic expert can be introduced for a consultation content regarding financial anxiety. A medical expert can also be introduced for a consultation content regarding dementia. A welfare expert can also be introduced for a consultation content regarding a disability of a family member. This makes it possible to introduce the most appropriate expert depending on the category of the consultation content.

[0094] The referral unit can improve the accuracy of expert selection by referring to the employee's past referral results when making a referral. For example, the referral unit can improve the accuracy of expert selection by referring to the employee's past referral results when making a referral. For example, the referral unit selects an expert for the current consultation content based on the employee's past referral results. In addition, patterns for improving the accuracy of expert selection can be extracted from the employee's past referral results. In addition, the expert selection algorithm can be optimized by referring to the employee's past referral results. This makes it possible to improve the accuracy of expert selection based on the employee's past referral results.

[0095] The introduction unit can estimate the employee's emotions and determine the priority of experts to introduce based on the estimated employee emotions. The introduction unit, for example, estimates the employee's emotions and determines the priority of experts to introduce based on the estimated employee emotions. For example, if the employee is feeling stressed, it can prioritize introducing experts who can help the employee relax. Also, if the employee is feeling anxious, it can prioritize introducing experts who can give the employee a sense of security. Also, if the employee is relaxed, it can prioritize introducing experts who can provide detailed consultation. This makes it possible to prioritize introducing experts according to the employee's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The introduction unit can adjust the order of expert introduction based on the time of submission of the consultation content at the time of introduction. For example, the introduction unit can adjust the order of expert introduction based on the time of submission of the consultation content at the time of introduction. For example, the introduction unit can prioritize experts who have submitted their consultation content earlier. Also, the introduction unit can postpone the introduction of experts who have submitted their consultation content later. Also, the introduction order of experts can be adjusted in stages depending on the time of submission. This makes it possible to prioritize experts based on the time of submission of the consultation content.

[0097] The introduction unit can select an expert based on the relevance of the consultation content at the time of introduction. The introduction unit, for example, selects an expert based on the relevance of the consultation content at the time of introduction. For example, the introduction unit can prioritize experts who are highly relevant to the consultation content. Also, experts who are less relevant to the consultation content can be introduced later. Also, the selection of experts can be adjusted in stages depending on the relevance of the consultation content. This makes it possible to select the most appropriate expert based on the relevance of the consultation content.

[0098] The introduction department can adjust the content of the expert introduction according to the employee's level of expertise when making the introduction. For example, the introduction department can adjust the content of the expert introduction according to the employee's level of expertise when making the introduction. For example, if the employee's level of expertise is high, the introduction can include specialized content. On the other hand, if the employee's level of expertise is low, the introduction can include easy-to-understand content. The introduction content can also be adjusted in stages according to the employee's level of expertise. This makes it possible to introduce an appropriate expert according to the employee's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and introduction unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the employee's consultation content in text format or as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using text analysis technology or emotion analysis technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice and support based on the analyzed content. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an expert as needed. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and introduction unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the employee's consultation content as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using text analysis technology and emotion analysis technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice and support based on the analyzed content. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an expert as needed. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and introduction unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the employee's consultation content as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using text analysis technology and emotion analysis technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice and support based on the analyzed content. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an expert as needed. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and introduction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the employee's consultation content as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using text analysis technology and emotion analysis technology. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice and support based on the analyzed content. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces an expert as needed.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The AI ​​social worker system may further include a feedback unit. The feedback unit collects feedback from employees regarding the advice and support they have received and provides it to the analysis unit. For example, the employee may rate their satisfaction with the advice provided. The feedback unit may also report the results of the employee's implementation of the advice. This allows the analysis unit to improve the accuracy of the advice based on the feedback. Furthermore, the feedback unit may adjust the content of the advice provided by the advice providing unit based on the employee's feedback. This allows the provision of more appropriate advice that meets the employee's needs.

[0101] The AI ​​social worker system can further be equipped with a learning unit. The learning unit trains the entire system based on the consultation content and feedback from employees. For example, the learning unit can analyze patterns in the consultation content of employees and predict future consultation content. The learning unit can also evaluate the effectiveness of advice based on employee feedback and use this to improve the system. This allows the AI ​​social worker system to continuously learn and improve the quality of support provided to employees. Furthermore, the learning unit can also propose new support measures based on the consultation content of employees. This makes it possible to respond to the diverse needs of employees.

[0102] The AI ​​social worker system can also be equipped with a notification unit. The notification unit notifies employees of important information and advice in a timely manner. For example, after an employee inputs the content of their consultation, the notification unit can be sent once the analysis unit has completed. Advice can also be sent periodically based on a schedule set by the employee. This allows employees to receive the information they need at the appropriate time. Furthermore, the notification unit can adjust the timing of notifications according to the employee's emotional state. For example, if an employee is feeling stressed, the notification unit can be sent at a time when they are able to relax.

[0103] The AI ​​social worker system can also be equipped with a reminder unit. The reminder unit reminds employees of the goals and tasks they have set. For example, it can remind them of the implementation schedule for the care methods they have set. It can also remind them of the deadline for implementing support measures for financial anxiety they have set. This helps employees remember to complete the goals and tasks they have set. Furthermore, the reminder unit can adjust the timing of reminders according to the employee's emotional state. For example, if the employee is relaxed, it can send detailed reminders.

[0104] The AI ​​social worker system can further include a history unit. The history unit stores the employee's past consultation and advice history and can be referenced as needed. For example, an employee can review past consultations. The history unit can also be used to evaluate the effectiveness of advice received in the past. This allows employees to more effectively resolve their current consultations based on past consultations and advice. Furthermore, the history unit can filter past history according to the employee's emotional state. For example, if an employee is feeling stressed, past success stories can be displayed preferentially.

[0105] The AI ​​social worker system can further include a prediction unit. The prediction unit predicts future problems and needs based on the content of the employee's consultation and past data. For example, it can predict problems that may arise in the future based on the content of the employee's consultation and provide advice in advance. The prediction unit can also predict future support needs based on the employee's past data. This allows employees to prepare in advance for future problems. Furthermore, the prediction unit can adjust the prediction results according to the employee's emotional state. For example, if an employee is feeling anxious, it can provide a prediction result that gives them a sense of security.

[0106] The AI ​​social worker system can further include a customization unit. The customization unit customizes the system settings according to the individual needs and preferences of the employee. For example, if an employee prefers a specific advice format, the system can provide advice based on that format. Also, if an employee prefers consultation during a specific time of day, the system settings can be adjusted to suit that time of day. This allows employees to use the system in a way that suits their needs. Furthermore, the customization unit can adjust the customization content according to the employee's emotional state. For example, if the employee is relaxed, detailed customization can be performed.

[0107] The AI ​​social worker system can further include a statistics unit. The statistics unit collects statistical data on employee consultation content and advice and provides it to the analysis unit. For example, it can analyze trends in employee consultation content and identify common problems. The statistics unit can also provide data for evaluating the effectiveness of employee advice. This allows the analysis unit to improve the accuracy of advice based on the statistical data. Furthermore, the statistics unit can adjust the way statistical data is displayed depending on the employee's emotional state. For example, if an employee is feeling stressed, simple and easy-to-understand statistical data can be displayed.

[0108] The AI ​​social worker system can further include a collaboration unit. The collaboration unit collaborates with other systems and services to expand support for employees. For example, the collaboration unit can collaborate with medical institutions and counseling services to provide specialized support to employees. The collaboration unit can also collaborate with employee health management systems to provide advice based on their health status. This allows employees to receive multifaceted support. Furthermore, the collaboration unit can select services to collaborate with depending on the employee's emotional state. For example, if an employee is feeling anxious, it can collaborate with a service that provides reassurance.

[0109] The AI ​​social worker system can further include an evaluation unit. The evaluation unit evaluates the effectiveness of the advice and support received by the employee and provides the evaluation results to the analysis unit. For example, the effectiveness of the advice received by the employee can be quantitatively evaluated. The evaluation unit can also evaluate the effectiveness of the advice based on the employee's feedback. This allows the analysis unit to improve the accuracy of the advice based on the evaluation results. Furthermore, the evaluation unit can adjust the evaluation method according to the employee's emotional state. For example, if the employee is relaxed, a detailed evaluation can be performed.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit accepts the employee's consultation content. Employee consultation content may include, but is not limited to, family disability, dementia, and financial anxiety. The reception unit accepts the consultation content entered by the employee in text format. Consultation content can also be accepted using voice input or image input. For example, if the employee enters the consultation content by voice, the reception unit converts the voice data into text data and accepts it. Step 2: The analysis unit analyzes the consultation content received by the reception unit. The analysis unit uses text analysis technology to analyze the consultation content and understand the content of the employee's consultation. It can also analyze the employee's emotions using emotion analysis technology. For example, it analyzes the text data of the consultation content and estimates the employee's emotions. Step 3: The provider provides appropriate advice and support based on the results of the analysis. For example, it can suggest care methods for a family member with dementia or support measures for financial concerns. It can also provide advice based on the employee's emotions. For example, if an employee is feeling stressed, it can provide advice to help them relax. Step 4: Based on the advice and support provided by the provider, the referral unit will refer the patient to a specialist if necessary, for example, a specialist providing specialized care services for family members with dementia or counselling for financial concerns.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0177] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception department that accepts inquiries from employees, an analysis unit that analyzes the consultation content received by the reception unit; a providing unit that provides advice or assistance based on the content analyzed by the analyzing unit; An introduction unit that introduces experts based on the advice and support provided by the providing unit. A system characterized by:

2. The reception unit Accepts private worries and anxieties entered by employees about family members' disabilities or dementia, and financial insecurity 2. The system of claim 1.

3. The analysis unit Analyzing the consultation content received by the reception unit and understanding the consultation content of the employee 2. The system of claim 1.

4. The providing unit Based on the content analyzed by the analysis unit, a method of caring for a family member with dementia or a support measure for financial anxiety is proposed.

2. The system of claim 1.

5. The introduction unit Referrals to professionals who provide specialized care services or counseling based on the advice and assistance provided by the provider.

2. The system of claim 1.

6. The reception unit Estimate employee emotions and adjust the timing of accepting consultation requests based on the estimated employee emotions.

2. The system of claim 1.

7. The reception unit Analyze employees' past consultation history and select the reception method 2. The system of claim 1.

8. The reception unit When receiving inquiries, filter them based on the employee's current life situation and areas of interest.

2. The system of claim 1.

Images