System

Abstract

An object of the system according to the embodiment is to receive user's complaint and provide an appropriate response and advice.SOLUTION: A system according to an embodiment includes a reception unit, a response generation unit, and an advice provision unit. The complaint receiving unit receives user's complaint. The backchannel generator generates an appropriate backchannel based on the comment received by the comment receiver. The advice providing unit provides advice incorporating a cognitive behavioral therapy technique based on the response generated by the response generation 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] With conventional technology, there were issues such as fewer opportunities for people to feel comfortable venting their frustrations and a lack of emotional support.

[0005] The system according to the embodiment aims to receive complaints from users and provide appropriate responses and advice. [Means for solving the problem]

[0006] A system according to an embodiment includes a complaint receiving unit, a backchannel generating unit, and an advice providing unit. The complaint receiving unit receives a user's complaints. The backchannel generating unit generates an appropriate backchannel based on the complaint received by the complaint receiving unit. The advice providing unit provides advice incorporating cognitive behavioral therapy techniques based on the backchannel generated by the backchannel generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can receive a user's complaints and provide appropriate responses and advice. [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) The voice dialogue AI system according to an embodiment of the present invention automatically receives complaints from a user, and the generation AI responds appropriately and provides advice incorporating cognitive behavioral therapy techniques. By listening to the user's complaints and providing appropriate responses and advice, the voice dialogue AI system can prevent the user from experiencing depression or low mood and support a better life.

[0029] A voice dialogue AI system according to an embodiment includes a complaint receiving unit, a backchannel generation unit, and an advice providing unit. The complaint receiving unit receives a user's complaints. For example, the complaint receiving unit understands the user's speech using speech recognition technology and saves the complaints as text data. The complaint receiving unit also analyzes the user's complaints in real time and provides data for generating appropriate backchannels. The backchannel generation unit generates appropriate backchannels based on the complaints received by the complaint receiving unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate backchannels that show empathy or agreement with the user's complaint. The generation AI can also use a multimodal generation AI to analyze voice and facial expressions to generate more natural backchannels. The generation AI responds, for example, with a form such as "That must have been difficult" or "I understand how you feel." The advice providing unit provides advice incorporating cognitive behavioral therapy techniques based on the backchannels generated by the backchannel generation unit. For example, the generation AI analyzes the content of the user's complaints or worries and generates specific advice. The generation AI, for example, asks questions such as, "Which part specifically do you feel is not working?" to help the user specifically identify the problem. The generation AI also monitors the user's mood and provides support as needed. For example, if the user says, "I've been feeling down lately," the generation AI asks questions such as, "How long have you been feeling that way?" to understand the user's mood fluctuations. In this way, the voice dialogue AI system according to the embodiment can listen to the user's complaints and provide appropriate responses and advice, thereby preventing the user from experiencing depression or low mood and supporting a better life. For example, by having the user complain on a daily basis, stress can be reduced and low mood can be prevented. Furthermore, advice incorporating techniques from cognitive behavioral therapy can help the user specifically identify their own problems and find solutions.

[0030] The complaint receiving unit can refer to the user's past speech history to generate more personalized responses. The complaint receiving unit, for example, stores the user's past speech history in a database and refers to the past speech history depending on the content of the complaint. For example, if the complaint is about the same topic as a previous complaint, it generates a response such as, "You said something similar last time, didn't you?" The complaint receiving unit also analyzes the user's past speech history and extracts specific keywords and phrases. This generates a personalized response depending on the content of the complaint. For example, it responds with, "You faced a similar problem last time, didn't you?" The complaint receiving unit also refers to specific examples and episodes related to the content of the complaint based on the user's past speech history to generate a personalized response. For example, it responds with, "You were in trouble in a similar situation before." By referring to the user's past speech history, more personalized responses can be generated, improving user satisfaction.

[0031] The complaint receiving unit can analyze the content of the complaint, estimate the user's stress level, and suggest an appropriate relaxation method. For example, the complaint receiving unit performs text analysis of the content of the complaint and extracts keywords and phrases related to stress. This allows the unit to estimate the user's stress level and suggest an appropriate relaxation method. For example, the unit provides advice such as, "Why don't you try taking deep breaths?" The complaint receiving unit also performs audio analysis of the content of the complaint and estimates the stress level from the tone and speed of the voice. This allows the unit to suggest a relaxation method according to the user's stress level. For example, the unit provides advice such as, "Why don't you listen to music to relax?" The complaint receiving unit also performs emotional analysis of the content of the complaint and estimates the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, the unit provides advice such as, "Why don't you try meditation?" The complaint receiving unit also performs emotional analysis of the content of the complaint and estimates the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, the unit provides advice such as, "Why don't you try meditation?" The user's stress level is estimated and an appropriate relaxation method is suggested, thereby reducing the user's stress.

[0032] The complaint receiving unit can suggest related music or podcasts based on the content of the complaint. For example, the complaint receiving unit analyzes the content of the complaint and suggests related music. For example, if the user is feeling stressed, relaxing music is suggested. The complaint receiving unit also analyzes the content of the complaint and suggests related podcasts. For example, if the user is complaining about work, a podcast related to work is suggested. The complaint receiving unit also analyzes the content of the complaint and suggests both music and podcasts. For example, if the user is feeling stressed, relaxing music and a podcast related to stress management are suggested. In this way, suggesting related music or podcasts based on the content of the complaint improves the user's mood.

[0033] The advice providing unit can refer to the content of the user's past consultations and provide advice to encourage long-term behavioral change. The advice providing unit, for example, stores the content of the user's past consultations in a database and provides advice to encourage long-term behavioral change. For example, it poses a question such as, "What progress have you made toward the goal discussed in the previous consultation?" The advice providing unit also analyzes the content of the user's past consultations and provides specific advice to encourage long-term behavioral change. For example, it poses a question such as, "Have you tried the stress management method discussed in the previous consultation?" The advice providing unit also provides feedback to encourage long-term behavioral change based on the content of the user's past consultations. For example, it poses a question such as, "What progress have you made toward the goal discussed in the previous consultation?" In this way, the advice providing unit supports the user's behavioral change by referring to the content of the user's past consultations and providing advice to encourage long-term behavioral change.

[0034] When providing advice incorporating cognitive behavioral therapy techniques, the advice providing unit can suggest specific improvement measures that take into consideration the user's lifestyle habits and environmental factors. The advice providing unit, for example, stores the user's lifestyle habits in a database and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine?" The advice providing unit also analyzes the user's environmental factors and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try tidying up your desk to create a better work environment?" The advice providing unit also analyzes the user's lifestyle habits and environmental factors in combination and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine and tidying up your desk to create a better work environment?" In this way, the user's quality of life is improved by suggesting specific improvement measures that take into consideration the user's lifestyle habits and environmental factors.

[0035] The advice providing unit can provide advice that combines other psychotherapy techniques based on the content of the user's statements. The advice providing unit, for example, analyzes the content of the user's statements and provides advice that combines cognitive behavioral therapy techniques and mindfulness techniques. For example, the advice providing unit provides advice such as, "When you feel stressed, take a deep breath and try mindfulness meditation." The advice providing unit also analyzes the content of the user's statements and provides advice that combines cognitive behavioral therapy techniques and active listening techniques. For example, the advice providing unit provides advice such as, "When listening to others, try practicing active listening while empathizing with their feelings." The advice providing unit also analyzes the content of the user's statements and provides advice that combines cognitive behavioral therapy techniques and relaxation techniques. For example, the advice providing unit provides advice such as, "When you feel anxious, try cognitive restructuring while listening to relaxation music." In this way, by providing advice that combines other psychotherapy techniques based on the content of the user's statements, psychological support for the user is enhanced.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The complaint receiving unit can suggest related music or podcasts based on the content of the complaint. For example, the content of the complaint may be analyzed to suggest related music. For example, if the user is feeling stressed, relaxing music may be suggested. The complaint receiving unit may also analyze the content of the complaint to suggest related podcasts. For example, if the user is complaining about work, a podcast related to work may be suggested. The complaint receiving unit may also analyze the content of the complaint to suggest both music and podcasts. For example, if the user is feeling stressed, relaxing music and a podcast related to stress management may be suggested. In this way, suggesting related music or podcasts based on the content of the complaint improves the user's mood.

[0038] The complaint receiving unit can refer to the user's past speech history to generate more personalized responses. For example, the unit stores the content of the user's past speech in a database and references the past speech history depending on the content of the complaint. For example, if the complaint is about the same topic as a previous complaint, it generates a response such as, "You said something similar last time, didn't you?" The complaint receiving unit also analyzes the user's past speech history and extracts specific keywords and phrases. This generates a personalized response depending on the content of the complaint. For example, it responds with, "You faced a similar problem last time, didn't you?" The complaint receiving unit also references specific examples and episodes related to the content of the complaint based on the user's past speech history to generate a personalized response. For example, it responds with, "You were in trouble in a similar situation before." By referring to the user's past speech history, more personalized responses can be generated, improving user satisfaction.

[0039] The complaint receiving unit can analyze the content of the complaint, estimate the user's stress level, and suggest an appropriate relaxation method. For example, it can perform text analysis of the content of the complaint and extract keywords and phrases related to stress. This allows the user's stress level to be estimated and an appropriate relaxation method to be suggested. For example, it can provide advice such as, "Why don't you try taking deep breaths?" The complaint receiving unit can also perform audio analysis of the content of the complaint and estimate the stress level from the tone and speed of the voice. This allows the unit to suggest a relaxation method according to the user's stress level. For example, it can provide advice such as, "Why don't you listen to music to relax?" The complaint receiving unit can also perform emotional analysis of the content of the complaint and estimate the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, it can provide advice such as, "Why don't you try meditation?" The complaint receiving unit can also perform emotional analysis of the content of the complaint and estimate the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, it can provide advice such as, "Why don't you try meditation?" The user's stress level can be estimated and an appropriate relaxation method can be suggested, thereby reducing the user's stress.

[0040] The advice providing unit can refer to the content of the user's past consultations and provide advice to encourage long-term behavioral change. For example, the advice providing unit can store the content of the user's past consultations in a database and provide advice to encourage long-term behavioral change. For example, the advice providing unit can ask a question such as, "What progress have you made toward the goal discussed in the previous consultation?" The advice providing unit can also analyze the content of the user's past consultations and provide specific advice to encourage long-term behavioral change. For example, the advice providing unit can ask a question such as, "Have you tried the stress management method discussed in the previous consultation?" The advice providing unit can also provide feedback to encourage long-term behavioral change based on the content of the user's past consultations. For example, the advice providing unit can ask a question such as, "What progress have you made toward the goal discussed in the previous consultation?" In this way, the advice providing unit can refer to the content of the user's past consultations and provide advice to encourage long-term behavioral change, thereby supporting the user's behavioral change.

[0041] When providing advice incorporating cognitive behavioral therapy techniques, the advice providing unit can suggest specific improvement measures that take into account the user's lifestyle habits and environmental factors. For example, the advice providing unit stores the user's lifestyle habits in a database and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine?" The advice providing unit also analyzes the user's environmental factors and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try tidying up your desk to create a better work environment?" The advice providing unit also analyzes the user's lifestyle habits and environmental factors in combination and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine and tidying up your desk to create a better work environment?" In this way, the quality of life of the user is improved by suggesting specific improvement measures that take into account the user's lifestyle habits and environmental factors.

[0042] The advice providing unit can provide advice that combines other psychotherapy techniques based on the content of the user's statements. For example, the advice providing unit may analyze the content of the user's statements and provide advice that combines cognitive behavioral therapy techniques and mindfulness techniques. For example, the advice providing unit may provide advice such as, "When you feel stressed, take a deep breath and try mindfulness meditation." The advice providing unit may also analyze the content of the user's statements and provide advice that combines cognitive behavioral therapy techniques and active listening techniques. For example, the advice providing unit may provide advice such as, "When listening to others, try practicing active listening while empathizing with their feelings." The advice providing unit may also analyze the content of the user's statements and provide advice that combines cognitive behavioral therapy techniques and relaxation techniques. For example, the advice providing unit may provide advice such as, "When you feel anxious, try cognitive restructuring while listening to relaxation music." In this way, by providing advice that combines other psychotherapy techniques based on the content of the user's statements, psychological support for the user is enhanced.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The complaint receiving unit receives the user's complaints. For example, it uses voice recognition technology to understand what the user is saying and saves the complaints as text data. The complaint receiving unit also analyzes the user's complaints in real time and provides data for generating appropriate responses. Step 2: In the backchannel generation unit, the generation AI generates appropriate backchannels based on the complaints received by the complaint reception unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate backchannels that show empathy or agreement with the user's complaint. The generation AI can also use a multimodal generation AI to analyze voice and facial expressions to generate more natural backchannels. For example, the generation AI responds with something like, "That must have been tough," or "I understand how you feel." Step 3: The advice provider provides advice incorporating cognitive behavioral therapy techniques based on the backchannels generated by the backchannel generator. For example, the generator AI analyzes the content of the user's complaints and worries and generates specific advice. The generator AI poses questions such as, "What specifically do you feel is not going well?" to help the user identify the specific problem. The generator AI also monitors the user's mood and provides support as needed. For example, if the user says, "I've been feeling down lately," the generator AI will ask questions such as, "How long has that mood been going on for?" to understand the user's mood fluctuations.

[0045] (Example 2) The voice dialogue AI system according to an embodiment of the present invention automatically receives complaints from a user, and the generation AI responds appropriately and provides advice incorporating cognitive behavioral therapy techniques. By listening to the user's complaints and providing appropriate responses and advice, the voice dialogue AI system can prevent the user from experiencing depression or low mood and support a better life.

[0046] A voice dialogue AI system according to an embodiment includes a complaint receiving unit, a backchannel generation unit, and an advice providing unit. The complaint receiving unit receives a user's complaints. For example, the complaint receiving unit understands the user's speech using speech recognition technology and saves the complaints as text data. The complaint receiving unit also analyzes the user's complaints in real time and provides data for generating appropriate backchannels. The backchannel generation unit generates appropriate backchannels based on the complaints received by the complaint receiving unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate backchannels that show empathy or agreement with the user's complaint. The generation AI can also use a multimodal generation AI to analyze voice and facial expressions to generate more natural backchannels. The generation AI responds, for example, with a form such as "That must have been difficult" or "I understand how you feel." The advice providing unit provides advice incorporating cognitive behavioral therapy techniques based on the backchannels generated by the backchannel generation unit. For example, the generation AI analyzes the content of the user's complaints or worries and generates specific advice. The generation AI, for example, asks questions such as, "Which part specifically do you feel is not working?" to help the user specifically identify the problem. The generation AI also monitors the user's mood and provides support as needed. For example, if the user says, "I've been feeling down lately," the generation AI asks questions such as, "How long have you been feeling that way?" to understand the user's mood fluctuations. In this way, the voice dialogue AI system according to the embodiment can listen to the user's complaints and provide appropriate responses and advice, thereby preventing the user from experiencing depression or low mood and supporting a better life. For example, by having the user complain on a daily basis, stress can be reduced and low mood can be prevented. Furthermore, advice incorporating techniques from cognitive behavioral therapy can help the user specifically identify their own problems and find solutions.

[0047] The complaint receiving unit can refer to the user's past speech history to generate more personalized responses. The complaint receiving unit, for example, stores the user's past speech history in a database and refers to the past speech history depending on the content of the complaint. For example, if the complaint is about the same topic as a previous complaint, it generates a response such as, "You said something similar last time, didn't you?" The complaint receiving unit also analyzes the user's past speech history and extracts specific keywords and phrases. This generates a personalized response depending on the content of the complaint. For example, it responds with, "You faced a similar problem last time, didn't you?" The complaint receiving unit also refers to specific examples and episodes related to the content of the complaint based on the user's past speech history to generate a personalized response. For example, it responds with, "You were in trouble in a similar situation before." By referring to the user's past speech history, more personalized responses can be generated, improving user satisfaction.

[0048] The complaint receiving unit can analyze the content of the complaint, estimate the user's stress level, and suggest an appropriate relaxation method. For example, the complaint receiving unit performs text analysis of the content of the complaint and extracts keywords and phrases related to stress. This allows the unit to estimate the user's stress level and suggest an appropriate relaxation method. For example, the unit provides advice such as, "Why don't you try taking deep breaths?" The complaint receiving unit also performs audio analysis of the content of the complaint and estimates the stress level from the tone and speed of the voice. This allows the unit to suggest a relaxation method according to the user's stress level. For example, the unit provides advice such as, "Why don't you listen to music to relax?" The complaint receiving unit also performs emotional analysis of the content of the complaint and estimates the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, the unit provides advice such as, "Why don't you try meditation?" The complaint receiving unit also performs emotional analysis of the content of the complaint and estimates the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, the unit provides advice such as, "Why don't you try meditation?" The user's stress level is estimated and an appropriate relaxation method is suggested, thereby reducing the user's stress.

[0049] The complaint receiving unit can use the emotion estimation function to analyze the user's emotional state in real time and generate backchannel responses according to the emotion. The complaint receiving unit, for example, uses the emotion estimation function to analyze the user's emotional state from the user's voice in real time. This generates backchannel responses according to the emotion. For example, if the user speaks in a sad voice, it generates a backchannel such as "That must be really hard." The complaint receiving unit also analyzes the user's facial expression using a camera to estimate the user's emotional state in real time. This generates a backchannel response according to the emotion. For example, if the user has an angry expression, it generates a backchannel such as "That must be really annoying." The complaint receiving unit also measures the user's heart rate and galvanic skin response using a sensor to estimate the user's emotional state in real time. This generates a backchannel response according to the emotion. For example, if the user's heart rate is high, it generates a backchannel such as "That must be really stressful." This allows the user's emotional state to be analyzed in real time and generates a backchannel response according to the emotion, thereby enabling a response that is sensitive to the user's emotions.

[0050] The complaint receiving unit can analyze the tone and speed of the user's voice to detect changes in emotion. The complaint receiving unit, for example, analyzes the user's voice tone in real time to detect changes in emotion. For example, when the voice tone gets lower, it generates a response such as "You seem a little depressed." The complaint receiving unit also analyzes the user's speaking speed in real time to detect changes in emotion. For example, when the speaking speed gets faster, it generates a response such as "You seem a little anxious." The complaint receiving unit also analyzes the user's voice tone and speed in combination to detect changes in emotion. For example, when the voice tone gets lower and the speaking speed gets slower, it generates a response such as "You seem a little tired." In this way, by analyzing the user's voice tone and speed and detecting changes in emotion, it is possible to respond according to the user's emotions.

[0051] The complaint receiving unit can suggest related music or podcasts based on the content of the complaint. For example, the complaint receiving unit analyzes the content of the complaint and suggests related music. For example, if the user is feeling stressed, relaxing music is suggested. The complaint receiving unit also analyzes the content of the complaint and suggests related podcasts. For example, if the user is complaining about work, a podcast related to work is suggested. The complaint receiving unit also analyzes the content of the complaint and suggests both music and podcasts. For example, if the user is feeling stressed, relaxing music and a podcast related to stress management are suggested. In this way, suggesting related music or podcasts based on the content of the complaint improves the user's mood.

[0052] The complaint receiving unit can use the emotion estimation function to suggest exercises or meditations according to the user's emotions. For example, the complaint receiving unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest appropriate exercises. For example, if the user is feeling stressed, stretching or yoga is suggested. The complaint receiving unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggest appropriate meditation methods. For example, if the user is feeling anxious, deep breathing or mindfulness meditation is suggested. The complaint receiving unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggest both exercise and meditation. For example, if the user is tired, light exercise and relaxing meditation are suggested. In this way, by suggesting exercises or meditations according to the user's emotions, the user's stress is reduced and relaxation is promoted.

[0053] The advice providing unit can refer to the content of the user's past consultations and provide advice to encourage long-term behavioral change. The advice providing unit, for example, stores the content of the user's past consultations in a database and provides advice to encourage long-term behavioral change. For example, it poses a question such as, "What progress have you made toward the goal discussed in the previous consultation?" The advice providing unit also analyzes the content of the user's past consultations and provides specific advice to encourage long-term behavioral change. For example, it poses a question such as, "Have you tried the stress management method discussed in the previous consultation?" The advice providing unit also provides feedback to encourage long-term behavioral change based on the content of the user's past consultations. For example, it poses a question such as, "What progress have you made toward the goal discussed in the previous consultation?" In this way, the advice providing unit supports the user's behavioral change by referring to the content of the user's past consultations and providing advice to encourage long-term behavioral change.

[0054] The advice providing unit can use the emotion estimation function to select a cognitive behavioral therapy technique according to the user's emotional state and provide appropriate advice. The advice providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and select an appropriate cognitive behavioral therapy technique. For example, if the user is feeling anxious, it suggests a relaxation technique. The advice providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and select an appropriate cognitive behavioral therapy technique. For example, if the user is feeling anger, it suggests an emotion regulation technique. The advice providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and select an appropriate cognitive behavioral therapy technique. For example, if the user is feeling sad, it suggests a cognitive restructuring technique. In this way, by selecting a cognitive behavioral therapy technique according to the user's emotional state and providing appropriate advice, it is possible to respond in accordance with the user's emotions.

[0055] When providing advice incorporating cognitive behavioral therapy techniques, the advice providing unit can suggest specific improvement measures that take into consideration the user's lifestyle habits and environmental factors. The advice providing unit, for example, stores the user's lifestyle habits in a database and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine?" The advice providing unit also analyzes the user's environmental factors and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try tidying up your desk to create a better work environment?" The advice providing unit also analyzes the user's lifestyle habits and environmental factors in combination and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine and tidying up your desk to create a better work environment?" In this way, the user's quality of life is improved by suggesting specific improvement measures that take into consideration the user's lifestyle habits and environmental factors.

[0056] The advice providing unit can provide advice that combines other psychotherapy techniques based on the content of the user's statements. The advice providing unit, for example, analyzes the content of the user's statements and provides advice that combines cognitive behavioral therapy techniques and mindfulness techniques. For example, the advice providing unit provides advice such as, "When you feel stressed, take a deep breath and try mindfulness meditation." The advice providing unit also analyzes the content of the user's statements and provides advice that combines cognitive behavioral therapy techniques and active listening techniques. For example, the advice providing unit provides advice such as, "When listening to others, try practicing active listening while empathizing with their feelings." The advice providing unit also analyzes the content of the user's statements and provides advice that combines cognitive behavioral therapy techniques and relaxation techniques. For example, the advice providing unit provides advice such as, "When you feel anxious, try cognitive restructuring while listening to relaxation music." In this way, by providing advice that combines other psychotherapy techniques based on the content of the user's statements, psychological support for the user is enhanced.

[0057] The advice providing unit can use the emotion estimation function to suggest reframing techniques according to the user's emotions and promote positive thinking. For example, the advice providing unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest appropriate reframing techniques. For example, if the user is feeling negative emotions, the advice providing unit suggests reframing such as, "Try looking at the situation from a different perspective." The advice providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggest reframing techniques to promote positive thinking. For example, if the user is afraid of failure, the advice providing unit suggests reframing such as, "Try to see failure as part of growth." The advice providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggest specific reframing techniques to promote positive thinking. For example, if the user is feeling self-deprecating, the advice providing unit suggests reframing such as, "Try to focus on your good points." In this way, the advice providing unit suggests reframing techniques according to the user's emotions and promotes positive thinking, thereby improving the user's psychological health.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The complaint receiving unit not only receives the user's complaints, but also analyzes the tone and speed of the user's voice to detect changes in emotion. For example, when the tone of the voice becomes lower, it generates a response such as "You seem a little depressed." The complaint receiving unit also analyzes the user's speaking speed in real time to detect changes in emotion. For example, when the speaking speed becomes faster, it generates a response such as "You seem a little anxious." The complaint receiving unit also analyzes the user's voice tone and speed in combination to detect changes in emotion. For example, when the voice tone becomes lower and the speaking speed becomes slower, it generates a response such as "You seem a little tired." In this way, by analyzing the user's voice tone and speed and detecting changes in emotion, it is possible to respond according to the user's emotions.

[0060] The complaint receiving unit can suggest related music or podcasts based on the content of the complaint. For example, the content of the complaint may be analyzed to suggest related music. For example, if the user is feeling stressed, relaxing music may be suggested. The complaint receiving unit may also analyze the content of the complaint to suggest related podcasts. For example, if the user is complaining about work, a podcast related to work may be suggested. The complaint receiving unit may also analyze the content of the complaint to suggest both music and podcasts. For example, if the user is feeling stressed, relaxing music and a podcast related to stress management may be suggested. In this way, suggesting related music or podcasts based on the content of the complaint improves the user's mood.

[0061] The complaint receiving unit can use the emotion estimation function to analyze the user's emotional state in real time and generate backchannel responses according to the emotion. For example, the emotion estimation function is used to analyze the user's emotional state from the user's voice in real time. This generates backchannel responses according to the emotion. For example, if the user speaks in a sad voice, the backchannel responses are generated, such as "That must be really hard." The complaint receiving unit also analyzes the user's facial expressions using a camera to estimate the user's emotional state in real time. This generates backchannel responses according to the emotion. For example, if the user has an angry expression, the backchannel responses are generated, such as "That must be really annoying." The complaint receiving unit also measures the user's heart rate and galvanic skin response using a sensor to estimate the user's emotional state in real time. This generates backchannel responses according to the emotion. For example, if the user's heart rate is high, the backchannel responses are generated, such as "That must be really stressful." This allows the user's emotional state to be analyzed in real time and backchannel responses according to the emotion to be generated, enabling a response that is sensitive to the user's emotions.

[0062] The complaint receiving unit can refer to the user's past speech history to generate more personalized responses. For example, the unit stores the content of the user's past speech in a database and references the past speech history depending on the content of the complaint. For example, if the complaint is about the same topic as a previous complaint, it generates a response such as, "You said something similar last time, didn't you?" The complaint receiving unit also analyzes the user's past speech history and extracts specific keywords and phrases. This generates a personalized response depending on the content of the complaint. For example, it responds with, "You faced a similar problem last time, didn't you?" The complaint receiving unit also references specific examples and episodes related to the content of the complaint based on the user's past speech history to generate a personalized response. For example, it responds with, "You were in trouble in a similar situation before." By referring to the user's past speech history, more personalized responses can be generated, improving user satisfaction.

[0063] The complaint receiving unit can analyze the content of the complaint, estimate the user's stress level, and suggest an appropriate relaxation method. For example, it can perform text analysis of the content of the complaint and extract keywords and phrases related to stress. This allows the user's stress level to be estimated and an appropriate relaxation method to be suggested. For example, it can provide advice such as, "Why don't you try taking deep breaths?" The complaint receiving unit can also perform audio analysis of the content of the complaint and estimate the stress level from the tone and speed of the voice. This allows the unit to suggest a relaxation method according to the user's stress level. For example, it can provide advice such as, "Why don't you listen to music to relax?" The complaint receiving unit can also perform emotional analysis of the content of the complaint and estimate the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, it can provide advice such as, "Why don't you try meditation?" The complaint receiving unit can also perform emotional analysis of the content of the complaint and estimate the user's emotional state. This allows the unit to suggest a relaxation method according to the stress level. For example, it can provide advice such as, "Why don't you try meditation?" The user's stress level can be estimated and an appropriate relaxation method can be suggested, thereby reducing the user's stress.

[0064] The complaint receiving unit can use the emotion estimation function to suggest exercises or meditations according to the user's emotions. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest appropriate exercises. For example, if the user is feeling stressed, stretching or yoga can be suggested. The complaint receiving unit can also use the emotion estimation function to analyze the user's emotional state in real time and suggest appropriate meditation methods. For example, if the user is feeling anxious, deep breathing or mindfulness meditation can be suggested. The complaint receiving unit can also use the emotion estimation function to analyze the user's emotional state in real time and suggest both exercise and meditation. For example, if the user is tired, light exercise and relaxing meditation can be suggested. In this way, by suggesting exercises or meditations according to the user's emotions, the user's stress can be reduced and relaxation can be promoted.

[0065] The advice providing unit can refer to the content of the user's past consultations and provide advice to encourage long-term behavioral change. For example, the advice providing unit can store the content of the user's past consultations in a database and provide advice to encourage long-term behavioral change. For example, the advice providing unit can ask a question such as, "What progress have you made toward the goal discussed in the previous consultation?" The advice providing unit can also analyze the content of the user's past consultations and provide specific advice to encourage long-term behavioral change. For example, the advice providing unit can ask a question such as, "Have you tried the stress management method discussed in the previous consultation?" The advice providing unit can also provide feedback to encourage long-term behavioral change based on the content of the user's past consultations. For example, the advice providing unit can ask a question such as, "What progress have you made toward the goal discussed in the previous consultation?" In this way, the advice providing unit can refer to the content of the user's past consultations and provide advice to encourage long-term behavioral change, thereby supporting the user's behavioral change.

[0066] The advice providing unit can use the emotion estimation function to select a cognitive behavioral therapy technique according to the user's emotional state and provide appropriate advice. For example, the emotion estimation function is used to analyze the user's emotional state in real time and select an appropriate cognitive behavioral therapy technique. For example, if the user is feeling anxious, a relaxation technique is suggested. The advice providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and select an appropriate cognitive behavioral therapy technique. For example, if the user is feeling anger, an emotion regulation technique is suggested. The advice providing unit also uses the emotion estimation function to analyze the user's emotional state in real time and select an appropriate cognitive behavioral therapy technique. For example, if the user is feeling sad, a cognitive restructuring technique is suggested. This makes it possible to select a cognitive behavioral therapy technique according to the user's emotional state and provide appropriate advice, thereby responding in accordance with the user's emotions.

[0067] When providing advice incorporating cognitive behavioral therapy techniques, the advice providing unit can suggest specific improvement measures that take into account the user's lifestyle habits and environmental factors. For example, the advice providing unit stores the user's lifestyle habits in a database and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine?" The advice providing unit also analyzes the user's environmental factors and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try tidying up your desk to create a better work environment?" The advice providing unit also analyzes the user's lifestyle habits and environmental factors in combination and provides advice that incorporates cognitive behavioral therapy techniques. For example, it suggests a specific improvement measure such as, "Why don't you try incorporating daily exercise into your daily routine and tidying up your desk to create a better work environment?" In this way, the quality of life of the user is improved by suggesting specific improvement measures that take into account the user's lifestyle habits and environmental factors.

[0068] The advice providing unit can provide advice that combines other psychotherapy techniques based on the content of the user's statements. For example, the advice providing unit may analyze the content of the user's statements and provide advice that combines cognitive behavioral therapy techniques and mindfulness techniques. For example, the advice providing unit may provide advice such as, "When you feel stressed, take a deep breath and try mindfulness meditation." The advice providing unit may also analyze the content of the user's statements and provide advice that combines cognitive behavioral therapy techniques and active listening techniques. For example, the advice providing unit may provide advice such as, "When listening to others, try practicing active listening while empathizing with their feelings." The advice providing unit may also analyze the content of the user's statements and provide advice that combines cognitive behavioral therapy techniques and relaxation techniques. For example, the advice providing unit may provide advice such as, "When you feel anxious, try cognitive restructuring while listening to relaxation music." In this way, by providing advice that combines other psychotherapy techniques based on the content of the user's statements, psychological support for the user is enhanced.

[0069] The advice providing unit can use the emotion estimation function to suggest reframing techniques according to the user's emotions and promote positive thinking. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest appropriate reframing techniques. For example, if the user is feeling negative emotions, the advice providing unit can suggest reframing such as, "Try looking at the situation from a different perspective." The advice providing unit can also use the emotion estimation function to analyze the user's emotional state in real time and suggest reframing techniques to promote positive thinking. For example, if the user is afraid of failure, the advice providing unit can suggest reframing such as, "Try to see failure as part of growth." The advice providing unit can also use the emotion estimation function to analyze the user's emotional state in real time and suggest specific reframing techniques to promote positive thinking. For example, if the user is feeling self-deprecating, the advice providing unit can suggest reframing such as, "Try to focus on your good points." In this way, the advice providing unit can suggest reframing techniques according to the user's emotions and promote positive thinking, thereby improving the user's psychological health.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The complaint receiving unit receives the user's complaints. For example, it uses voice recognition technology to understand what the user is saying and saves the complaints as text data. The complaint receiving unit also analyzes the user's complaints in real time and provides data for generating appropriate responses. Step 2: In the backchannel generation unit, the generation AI generates appropriate backchannels based on the complaints received by the complaint reception unit. For example, the generation AI uses a text generation AI (e.g., LLM) to generate backchannels that show empathy or agreement with the user's complaint. The generation AI can also use a multimodal generation AI to analyze voice and facial expressions to generate more natural backchannels. For example, the generation AI responds with something like, "That must have been tough," or "I understand how you feel." Step 3: The advice provider provides advice incorporating cognitive behavioral therapy techniques based on the backchannels generated by the backchannel generator. For example, the generator AI analyzes the content of the user's complaints and worries and generates specific advice. The generator AI poses questions such as, "What specifically do you feel is not going well?" to help the user identify the specific problem. The generator AI also monitors the user's mood and provides support as needed. For example, if the user says, "I've been feeling down lately," the generator AI will ask questions such as, "How long has that mood been going on for?" to understand the user's mood fluctuations.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, 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 robot 414 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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. [Explanation of symbols]

[0139] 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 complaint receiving unit that receives complaints from users; a backchannel generating unit that generates an appropriate backchannel based on the complaint received by the complaint receiving unit; an advice providing unit that provides advice incorporating a cognitive behavioral therapy technique based on the backchannel generated by the backchannel generating unit. A system characterized by:

2. The complaint receiving unit Refer to the user's past speech history to generate more personalized responses 2. The system of claim 1.

3. The complaint receiving unit Analyzes complaints, estimates the user's stress level, and suggests appropriate relaxation methods 2. The system of claim 1.

4. The complaint receiving unit Analyzes the user's emotional state in real time and generates responses that correspond to the user's emotions 2. The system of claim 1.

5. The complaint receiving unit Analyzes the tone and rate of a user's voice to detect changes in emotion 2. The system of claim 1.

6. The complaint receiving unit Suggest related music and podcasts based on your complaints 2. The system of claim 1.

7. The complaint receiving unit Suggest exercises and meditations based on the user's emotions 2. The system of claim 1.

8. The advice providing unit Analyze user statements, identify cognitive distortions, and suggest specific ways to correct them 2. The system of claim 1.

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