System

Abstract

To provide a system that estimates the emotion of a user and provides appropriate responses according thereto.SOLUTION: A system comprises an acquisition unit, an estimation unit, a decision unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a user. The estimation unit estimates the emotion of the user on the basis of the acquired information for estimating the emotion. The determination unit determines a response according to the emotion of the user estimated by the estimation unit. The response unit executes the determined response.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately estimate a user's emotions accurately and respond appropriately accordingly, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the user's emotions and take appropriate action accordingly. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating an emotion of a user. The estimation unit estimates an emotion of the user based on the acquired emotion estimation information. The determination unit determines a response according to the emotion of the user estimated by the estimation unit. The response unit executes the determined response. Effect of the Invention

[0007] The system according to the embodiment can estimate the user's emotions and take appropriate action accordingly. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that estimates the user's emotion and responds according to the emotion. The system includes an acquisition unit that acquires emotion estimation information for estimating the user's emotion. Next, the system includes an estimation unit that estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The system further includes a determination unit that determines a response according to the user's emotion estimated by the estimation unit. Finally, the system includes a response unit that executes the response determined by the determination unit. For example, one or more pieces of information among the user's facial expression, movement, voice, heart rate, and blood pressure are acquired as emotion estimation information, and the user's emotion is estimated based on this. Depending on the estimated emotion, a response such as a dialogue or relaxation technique is suggested, or music or video is provided. This can reduce the user's stress and worries and can be useful for mental care. This allows the system to estimate the user's emotion and respond according to the emotion.

[0029] The system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a user. The emotion estimation information includes, for example, facial expression data, voice data, and biometric information, but is not limited to such examples. For example, the acquisition unit photographs the user's facial expression using a camera and acquires the facial expression data. The acquisition unit can also record the user's voice using a microphone and acquire the voice data. Furthermore, the acquisition unit can measure the user's heart rate using a heart rate sensor and acquire the biometric information. The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. For example, a machine learning algorithm or a rule-based estimation method is used for estimating the emotion, but is not limited to such examples. The estimation unit estimates the user's emotion from the facial expression data using, for example, a machine learning algorithm. Furthermore, the estimation unit can also estimate the user's emotion from the voice data. Furthermore, the estimation unit can estimate the user's emotion from the biometric information. The determination unit decides a response according to the user's emotion estimated by the estimation unit. The response includes, for example, a dialogue, an action, a notification, and the like, but is not limited to such examples. For example, the determination unit determines a dialogue according to the user's emotion. Furthermore, the determination unit can also suggest a relaxation technique according to the user's emotion. Furthermore, the determination unit can also determine the provision of music and video according to the user's emotion. The response unit executes the response determined by the determination unit. For example, the response unit executes a dialogue according to the user's emotion. Furthermore, the response unit can also execute a relaxation technique according to the user's emotion. Furthermore, the response unit can also provide music and video according to the user's emotion. In this way, the system can estimate the user's emotion and perform a response according to the emotion.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the user. The emotion estimation information includes, for example, facial expression data, voice data, and biometric information, but is not limited to such examples. For example, the acquisition unit uses a camera to capture the facial expression of the user and acquires the facial expression data. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also record the user's voice using a microphone and acquire voice data. The microphone has a noise canceling function and eliminates ambient noise to acquire clear voice data. Furthermore, the acquisition unit can measure the user's heart rate using a heart rate sensor and acquire biometric information. There are types of heart rate sensors that directly contact the user's skin and types that are non-contact, and can monitor heart rate fluctuations in real time. This allows the acquisition unit to collect various emotion estimation information of the user with high accuracy and provide basic data for emotion estimation. Furthermore, the acquisition unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the estimation unit and the determination unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the acquisition unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. For example, a machine learning algorithm or a rule-based estimation method is used to estimate the emotion, but is not limited to such examples. For example, the estimation unit estimates the user's emotion from facial expression data using a machine learning algorithm. Specifically, image recognition technology using deep learning is used to analyze subtle changes in the user's facial expression. As a result, emotions such as joy, sadness, anger, and surprise can be estimated with high accuracy. The estimation unit can also estimate the user's emotion from voice data. For voice analysis, features such as tone, pitch, speed, and strength of the voice are extracted, and emotions are estimated based on these features. Furthermore, the estimation unit can also estimate the user's emotion from bioinformation. Biometric information such as heart rate and electrodermal activity is analyzed to estimate emotions such as stress and relaxation. As a result, the estimation unit can comprehensively analyze the collected various data and estimate the user's emotion with high accuracy. Furthermore, the estimation unit can also analyze long-term emotional fluctuations and trends by utilizing past data and statistical information. For example, based on past emotion data, it can predict emotion fluctuations in specific time periods or situations and plan future countermeasures. The estimation unit can also use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This enables the estimation unit to handle not only real-time emotion estimation, but also long-term emotion management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The determination unit determines a response according to the user's emotion estimated by the estimation unit. The response includes, for example, a dialogue, an action, a notification, and the like, but is not limited to such examples. The determination unit, for example, determines a dialogue according to the user's emotion. Specifically, when the user is feeling stressed, a dialogue for relaxing the user is selected, and kind words are spoken to the user. The determination unit can also suggest a relaxation technique according to the user's emotion. For example, a method of deep breathing or meditation is suggested, and the user is supported to relax. Furthermore, the determination unit can also determine the provision of music or video according to the user's emotion. For example, when the user is sad, music or video for brightening the mood is selected and provided to the user. This allows the determination unit to quickly determine the optimal response according to the user's emotion and suggest specific actions to improve the user's emotion. Furthermore, the determination unit can collect user feedback and continuously improve the accuracy and effectiveness of the response content. For example, the change in emotion after the user performs the proposed relaxation technique is monitored, and the next proposed content is adjusted based on the result. The determination unit can also simulate multiple response options and select the most effective response. This enables the decision unit to provide an optimal response to the user and support improvement of emotions.

[0033] The response unit executes the response determined by the determination unit. For example, the response unit executes a dialogue according to the user's emotions. Specifically, when the user is feeling stressed, the response unit executes a dialogue to relax the user and speaks kind words to the user. The response unit can also execute a relaxation technique according to the user's emotions. For example, the response unit instructs the user on how to take deep breaths or meditate, and actually supports the user in performing relaxation. Furthermore, the response unit can provide music and images according to the user's emotions. For example, when the user is sad, the response unit selects music and images to brighten the mood and provides them to the user. This allows the response unit to quickly execute an optimal response according to the user's emotions and implement specific actions to improve the user's emotions. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of the response content. For example, the response unit monitors changes in emotions after the user executes a suggested relaxation technique, and adjusts the next suggested content based on the results. The response unit can also simulate multiple response options and select the most effective response. This allows the response unit to provide the optimal response to the user and support the improvement of emotions.

[0034] The determination unit determines a dialogue according to the user's emotion as a response, and the response unit can perform a dialogue according to the user's emotion. For example, when the user is feeling stressed, the determination unit determines a dialogue for relaxing the user. Furthermore, when the user is relaxed, the determination unit can also determine a dialogue for digging deeper into the emotion. Furthermore, when the user is in a hurry, the determination unit can also determine a dialogue for quickly responding. The response unit executes the dialogue determined by the determination unit. For example, when the user is feeling stressed, the response unit performs a dialogue for relaxing the user. Furthermore, when the user is relaxed, the response unit can also perform a dialogue for digging deeper into the emotion. Furthermore, when the user is in a hurry, the response unit can also perform a dialogue for quickly responding. This allows the system to perform a dialogue according to the user's emotion.

[0035] The determination unit determines a dialogue in a mode corresponding to the user's emotion as a response, and the response unit can perform the dialogue in a mode corresponding to the user's emotion. For example, when the user is stressed, the determination unit determines a dialogue in a gentle mode for relaxing the user. Furthermore, when the user is relaxed, the determination unit can also determine a dialogue in an active mode for digging deeper into the emotion. Furthermore, when the user is in a hurry, the determination unit can also determine a dialogue in a concise mode for quickly responding. The response unit executes the dialogue in the mode determined by the determination unit. For example, when the user is stressed, the response unit performs a dialogue in a gentle mode for relaxing the user. Furthermore, when the user is relaxed, the response unit can also perform a dialogue in an active mode for digging deeper into the emotion. Furthermore, when the user is in a hurry, the response unit can also perform a dialogue in a concise mode for quickly responding. This allows the system to perform a dialogue in a mode corresponding to the user's emotion.

[0036] The response unit can conduct a dialogue based on a counseling program according to the user's emotions. For example, when the user is feeling stressed, the response unit conducts a dialogue based on a counseling program including relaxation techniques. Furthermore, when the user is relaxed, the response unit can also conduct a dialogue based on a counseling program for digging deeper into the user's emotions. Furthermore, when the user is in a hurry, the response unit can also conduct a dialogue based on a counseling program for responding quickly. This allows the system to conduct a dialogue based on a counseling program according to the user's emotions.

[0037] The corresponding unit can suggest a relaxation technique according to the user's emotion. For example, when the user feels stressed, the corresponding unit can suggest a relaxation technique such as deep breathing or meditation. Also, when the user is relaxed, the corresponding unit can suggest a relaxation technique such as yoga or stretching. Furthermore, when the user is in a hurry, the corresponding unit can suggest a relaxation technique that is effective in a short time. In this way, the system can suggest a relaxation technique according to the user's emotion.

[0038] The determination unit can determine to provide different music or images depending on the user's emotions. For example, when the user is feeling stressed, the determination unit determines to provide relaxing music or images of natural scenery. In addition, when the user is relaxed, the determination unit can also determine to provide music or images for digging deeper into emotions. Furthermore, when the user is in a hurry, the determination unit can also determine to provide music or images for a quick response. In this way, the system can determine to provide different music or images depending on the user's emotions.

[0039] The determination unit can determine a response according to the user's emotion based on the estimated user's emotion and the user's attributes. The determination unit determines a response according to the emotion based on attributes such as the user's age, sex, and hobbies. For example, if the user is young and feeling stressed, games and activities can be suggested. Also, if the user is elderly and relaxing, quiet music and reading can be suggested. Furthermore, if the user has a specific hobby, activities related to the hobby can be suggested. This allows the system to determine a response based on the user's emotion and attributes.

[0040] The acquisition unit can acquire one or more pieces of information among the user's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. For example, the acquisition unit captures the user's facial expression using a camera to acquire facial expression data. The acquisition unit can also record the user's voice using a microphone to acquire voice data. Furthermore, the acquisition unit can measure the user's heart rate using a heart rate sensor to acquire biometric information. This allows the system to acquire one or more pieces of information among the user's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information.

[0041] The counseling robot can function as a counseling robot having an acquisition unit, an estimation unit, a determination unit, and a response unit. The counseling robot has an acquisition unit, an estimation unit, a determination unit, and a response unit, estimates the user's emotions, and responds according to the emotions. For example, the counseling robot acquires the user's facial expressions and voice and estimates the emotions. Based on the estimated emotions, the counseling robot can suggest relaxation techniques and dialogue. The counseling robot can also measure the user's heart rate and blood pressure and evaluate the stress level. Furthermore, the counseling robot can provide music and images according to the user's emotions. This allows the counseling robot to grasp the user's emotions in detail and respond appropriately.

[0042] The acquisition unit can analyze the user's past emotion data and select the optimal acquisition method. For example, the acquisition unit analyzes time periods when the user felt stress in the past and acquires emotion estimation information focusing on those time periods. The acquisition unit can also find a tendency to react to a specific trigger (e.g., specific music) from the user's past emotion data and acquire information based on that trigger. Furthermore, the acquisition unit can enhance information acquisition in situations where emotions change drastically (e.g., during a meeting) based on the user's past emotion data. This allows the system to analyze the user's past emotion data and select the optimal acquisition method.

[0043] The acquisition unit can perform filtering based on the user's current activity status and environment when acquiring the emotion estimation information. For example, when the user is exercising, the acquisition unit prioritizes acquiring heart rate and motion data. In addition, when the user is in a quiet environment, the acquisition unit can also prioritize acquiring voice data. Furthermore, when the user is in a meeting, the acquisition unit can also prioritize acquiring facial expression data. This allows the system to filter the emotion estimation information based on the user's current activity status and environment.

[0044] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user. For example, when the user is at home, the acquisition unit preferentially acquires data based on a relaxed environment. In addition, when the user is at work, the acquisition unit can also preferentially acquire data indicating signs of stress. Furthermore, when the user is in a public place, the acquisition unit can also preferentially acquire surrounding voice and movement data. This allows the system to preferentially acquire highly relevant information in consideration of the geographical location information of the user.

[0045] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the emotion deduction information. For example, when the user posts on social media that he / she is feeling stressed, the acquisition unit acquires emotion deduction information based on the content of the post. In addition, when the user posts on social media that he / she is relaxing, the acquisition unit can also acquire emotion deduction information based on the content of the post. Furthermore, when the user posts on social media that he / she is in a hurry, the acquisition unit can also acquire emotion deduction information based on the content of the post. This allows the system to analyze the user's social media activity and acquire related information.

[0046] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. The estimation unit performs detailed emotion estimation based on important data such as heart rate and blood pressure. The estimation unit can also perform simple emotion estimation based on auxiliary data such as facial expression and voice. Furthermore, the estimation unit can also perform emotion estimation with an appropriate level of detail based on motion data. This allows the system to adjust the level of detail of the estimation based on the importance of the emotion estimation information.

[0047] The estimation unit can apply different estimation algorithms depending on the emotion category at the time of estimation. For example, when the user is feeling stressed, the estimation unit applies an algorithm specialized for stress. Also, when the user is relaxed, the estimation unit can apply an algorithm specialized for relaxation. Furthermore, when the user is in a hurry, the estimation unit can apply an algorithm that quickly estimates the emotion. This allows the system to apply different estimation algorithms depending on the emotion category.

[0048] The estimation unit can determine the priority order of estimation at the time of estimation based on the acquisition time of the emotion estimation information. For example, the estimation unit performs estimation by prioritizing the latest emotion estimation information. The estimation unit can also perform estimation by focusing on current information while referring to past emotion estimation information. Furthermore, the estimation unit can also perform estimation by prioritizing emotion estimation information acquired in a specific time period. This allows the system to determine the priority order of estimation based on the acquisition time of the emotion estimation information.

[0049] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by prioritizing information that is most relevant to the user's current situation. The estimation unit can also perform estimation by prioritizing information that is highly relevant to the user's past emotion data. Furthermore, the estimation unit can also perform estimation by prioritizing information that is most relevant to the user's environment. This allows the system to adjust the order of estimation based on the relevance of the emotion estimation information.

[0050] At the time of making a decision, the determination unit can analyze the user's past response history and select the optimal response method. For example, if the user has liked relaxation techniques in the past, the determination unit can give priority to and suggest them. Also, if the user has liked dialogue in the past, the determination unit can also give priority to and suggest them. Furthermore, if the user has liked music or video in the past, the determination unit can also give priority to and suggest them. This allows the system to analyze the user's past response history and select the optimal response method.

[0051] At the time of the determination, the decision unit can customize the response measures based on the user's current situation. For example, the decision unit can suggest short and effective relaxation techniques when the user is at work. The decision unit can also suggest long relaxation techniques when the user is at home. Furthermore, the decision unit can also suggest a response in an unobtrusive manner when the user is in a public place. This allows the system to customize the response measures based on the user's current situation.

[0052] At the time of making the decision, the decision unit can select the optimal response method in consideration of the geographical location information of the user. For example, when the user is at home, the decision unit can suggest relaxing music or video. Furthermore, when the user is at work, the decision unit can also suggest short and effective relaxation techniques. Furthermore, when the user is in a public place, the decision unit can also suggest an unobtrusive response method. This allows the system to select the optimal response method in consideration of the geographical location information of the user.

[0053] The response unit can select the optimal execution method by referring to the user's past response history when responding. For example, if the user has liked relaxation techniques in the past, the response unit can execute them with priority. Also, if the user has liked dialogue in the past, the response unit can execute them with priority. Furthermore, if the user has liked music or video in the past, the response unit can execute them with priority. This allows the system to select the optimal execution method by referring to the user's past response history.

[0054] The response unit can customize the response measures based on the user's current situation when responding. For example, the response unit can perform short, effective relaxation techniques when the user is at work. The response unit can also perform long, effective relaxation techniques when the user is at home. Furthermore, the response unit can perform a response in an unobtrusive manner when the user is in a public place. This allows the system to customize the response measures based on the user's current situation.

[0055] The response unit can select the optimal response method in consideration of the geographical location information of the user when responding. For example, when the user is at home, the response unit can provide relaxing music and images. Furthermore, when the user is at work, the response unit can also provide short and effective relaxation techniques. Furthermore, when the user is in a public place, the response unit can also provide an unobtrusive response. This allows the system to select the optimal response method in consideration of the geographical location information of the user.

[0056] The response unit can analyze the user's social media activity and suggest a means of response when responding. For example, if the user posts on social media that he / she feels stressed, the response unit can provide a relaxation technique. Also, if the user posts on social media that he / she feels relaxed, the response unit can provide a method of digging deeper into the emotion through dialogue. Furthermore, if the user posts on social media that he / she feels rushed, the response unit can provide a method of responding quickly. This allows the system to analyze the user's social media activity and suggest a means of response.

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

[0058] When acquiring the emotion estimation information of the user, the acquisition unit can analyze the user's past behavioral patterns and determine the optimal acquisition timing. For example, if the user was prone to feeling stress during a specific time period in the past, emotion estimation information is acquired with a focus on that time period. In addition, if the user's emotions fluctuate greatly during a specific activity (e.g., exercise, work), emotion estimation information can also be acquired during that activity. Furthermore, if the user's emotions fluctuate greatly in a specific location (e.g., home, workplace), emotion estimation information at that location can also be acquired with priority. This allows the system to optimize the timing of acquiring emotion estimation information based on the user's past behavioral patterns.

[0059] When estimating the user's emotion, the estimation unit can adjust the estimation algorithm based on the user's current activity status and environment. For example, when the user is exercising, the emotion can be estimated by prioritizing the heart rate and motion data. When the user is in a quiet environment, the emotion can be estimated by prioritizing the voice data and facial expression data. Furthermore, when the user is in a meeting, the emotion can be estimated by prioritizing the facial expression data and voice data. This allows the system to optimize the emotion estimation algorithm based on the user's current activity status and environment.

[0060] When determining a response according to the user's emotion, the determination unit can analyze the user's past emotion data and select the optimal response method. For example, if the user liked relaxation techniques in the past, it can give priority to suggesting them. Also, if the user liked dialogue in the past, it can give priority to suggesting them. Furthermore, if the user liked music or video in the past, it can give priority to suggesting them. In this way, the system can analyze the user's past emotion data and select the optimal response method.

[0061] When performing a response according to the user's emotion, the response unit can customize the response measures based on the user's current situation. For example, when the user is at work, a short and effective relaxation technique can be performed. Also, when the user is at home, a long relaxation technique can be performed. Furthermore, when the user is in a public place, a response in an unobtrusive manner can be performed. This allows the system to customize the response measures based on the user's current situation.

[0062] When acquiring the emotion deduction information of the user, the acquisition unit can analyze the user's social media activity and acquire related information. For example, if the user posts on social media that he / she is feeling stressed, the emotion deduction information can be acquired based on the content of the post. Also, if the user posts on social media that he / she is relaxing, the emotion deduction information can be acquired based on the content of the post. Furthermore, if the user posts on social media that he / she is in a hurry, the emotion deduction information can be acquired based on the content of the post. In this way, the system can analyze the user's social media activity and acquire related information.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a user. The emotion estimation information includes facial expression data, voice data, biometric information, and the like. For example, a camera is used to capture the user's facial expression to acquire facial expression data. A microphone can also be used to record the user's voice to acquire voice data. Furthermore, a heart rate sensor can be used to measure the user's heart rate to acquire biometric information. Step 2: The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. A machine learning algorithm or a rule-based estimation method is used to estimate the emotion. For example, the user's emotion is estimated from facial expression data using a machine learning algorithm. The user's emotion can also be estimated from voice data and biometric information. Step 3: The determination unit determines a response according to the user's emotion estimated by the estimation unit. The response includes dialogue, action, notification, etc. For example, a dialogue according to the user's emotion is determined. It is also possible to suggest relaxation techniques or provide music or video. Step 4: The response unit executes the response determined by the determination unit. For example, the response unit executes a dialogue according to the user's emotions. It can also execute relaxation techniques or provide music or video.

[0065] (Example 2) The system according to the embodiment of the present invention is a system that estimates the user's emotion and responds according to the emotion. The system includes an acquisition unit that acquires emotion estimation information for estimating the user's emotion. Next, the system includes an estimation unit that estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The system further includes a determination unit that determines a response according to the user's emotion estimated by the estimation unit. Finally, the system includes a response unit that executes the response determined by the determination unit. For example, one or more pieces of information among the user's facial expression, movement, voice, heart rate, and blood pressure are acquired as emotion estimation information, and the user's emotion is estimated based on this. Depending on the estimated emotion, a response such as a dialogue or relaxation technique is suggested, or music or video is provided. This can reduce the user's stress and worries and can be useful for mental care. This allows the system to estimate the user's emotion and respond according to the emotion.

[0066] The system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and a corresponding unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a user. The emotion estimation information includes, for example, facial expression data, voice data, and biometric information, but is not limited to such examples. For example, the acquisition unit photographs the user's facial expression using a camera and acquires the facial expression data. The acquisition unit can also record the user's voice using a microphone and acquire the voice data. Furthermore, the acquisition unit can measure the user's heart rate using a heart rate sensor and acquire the biometric information. The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. For example, a machine learning algorithm or a rule-based estimation method is used for estimating the emotion, but is not limited to such examples. The estimation unit estimates the user's emotion from the facial expression data using, for example, a machine learning algorithm. Furthermore, the estimation unit can also estimate the user's emotion from the voice data. Furthermore, the estimation unit can estimate the user's emotion from the biometric information. The determination unit decides a response according to the user's emotion estimated by the estimation unit. The response includes, for example, a dialogue, an action, a notification, and the like, but is not limited to such examples. For example, the determination unit determines a dialogue according to the user's emotion. Furthermore, the determination unit can also suggest a relaxation technique according to the user's emotion. Furthermore, the determination unit can also determine the provision of music and video according to the user's emotion. The response unit executes the response determined by the determination unit. For example, the response unit executes a dialogue according to the user's emotion. Furthermore, the response unit can also execute a relaxation technique according to the user's emotion. Furthermore, the response unit can also provide music and video according to the user's emotion. In this way, the system can estimate the user's emotion and perform a response according to the emotion.

[0067] The acquisition unit acquires emotion estimation information for estimating the emotion of the user. The emotion estimation information includes, for example, facial expression data, voice data, and biometric information, but is not limited to such examples. For example, the acquisition unit uses a camera to capture the facial expression of the user and acquires the facial expression data. The camera has high resolution and can capture even subtle changes in facial expression. The acquisition unit can also record the user's voice using a microphone and acquire voice data. The microphone has a noise canceling function and eliminates ambient noise to acquire clear voice data. Furthermore, the acquisition unit can measure the user's heart rate using a heart rate sensor and acquire biometric information. There are types of heart rate sensors that directly contact the user's skin and types that are non-contact, and can monitor heart rate fluctuations in real time. This allows the acquisition unit to collect various emotion estimation information of the user with high accuracy and provide basic data for emotion estimation. Furthermore, the acquisition unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the estimation unit and the determination unit. In addition, by adjusting the frequency and accuracy of data collection, it is possible to respond flexibly to specific situations and conditions. This allows the acquisition unit to collect data efficiently and effectively, improving the performance of the entire system.

[0068] The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. For example, a machine learning algorithm or a rule-based estimation method is used to estimate the emotion, but is not limited to such examples. For example, the estimation unit estimates the user's emotion from facial expression data using a machine learning algorithm. Specifically, image recognition technology using deep learning is used to analyze subtle changes in the user's facial expression. As a result, emotions such as joy, sadness, anger, and surprise can be estimated with high accuracy. The estimation unit can also estimate the user's emotion from voice data. For voice analysis, features such as tone, pitch, speed, and strength of the voice are extracted, and emotions are estimated based on these features. Furthermore, the estimation unit can also estimate the user's emotion from bioinformation. Biometric information such as heart rate and electrodermal activity is analyzed to estimate emotions such as stress and relaxation. As a result, the estimation unit can comprehensively analyze the collected various data and estimate the user's emotion with high accuracy. Furthermore, the estimation unit can also analyze long-term emotional fluctuations and trends by utilizing past data and statistical information. For example, based on past emotion data, it can predict emotion fluctuations in specific time periods or situations and plan future countermeasures. The estimation unit can also use an anomaly detection algorithm to detect unusual patterns or abnormal data and issue an early warning. This enables the estimation unit to handle not only real-time emotion estimation, but also long-term emotion management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The determination unit determines a response according to the user's emotion estimated by the estimation unit. The response includes, for example, a dialogue, an action, a notification, and the like, but is not limited to such examples. The determination unit, for example, determines a dialogue according to the user's emotion. Specifically, when the user is feeling stressed, a dialogue for relaxing the user is selected, and kind words are spoken to the user. The determination unit can also suggest a relaxation technique according to the user's emotion. For example, a method of deep breathing or meditation is suggested, and the user is supported to relax. Furthermore, the determination unit can also determine the provision of music or video according to the user's emotion. For example, when the user is sad, music or video for brightening the mood is selected and provided to the user. This allows the determination unit to quickly determine the optimal response according to the user's emotion and suggest specific actions to improve the user's emotion. Furthermore, the determination unit can collect user feedback and continuously improve the accuracy and effectiveness of the response content. For example, the change in emotion after the user performs the proposed relaxation technique is monitored, and the next proposed content is adjusted based on the result. The determination unit can also simulate multiple response options and select the most effective response. This enables the decision unit to provide an optimal response to the user and support improvement of emotions.

[0070] The response unit executes the response determined by the determination unit. For example, the response unit executes a dialogue according to the user's emotions. Specifically, when the user is feeling stressed, the response unit executes a dialogue to relax the user and speaks kind words to the user. The response unit can also execute a relaxation technique according to the user's emotions. For example, the response unit instructs the user on how to take deep breaths or meditate, and actually supports the user in performing relaxation. Furthermore, the response unit can provide music and images according to the user's emotions. For example, when the user is sad, the response unit selects music and images to brighten the mood and provides them to the user. This allows the response unit to quickly execute an optimal response according to the user's emotions and implement specific actions to improve the user's emotions. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of the response content. For example, the response unit monitors changes in emotions after the user executes a suggested relaxation technique, and adjusts the next suggested content based on the results. The response unit can also simulate multiple response options and select the most effective response. This allows the response unit to provide the optimal response to the user and support the improvement of emotions.

[0071] The determination unit determines a dialogue according to the user's emotion as a response, and the response unit can perform a dialogue according to the user's emotion. For example, when the user is feeling stressed, the determination unit determines a dialogue for relaxing the user. Furthermore, when the user is relaxed, the determination unit can also determine a dialogue for digging deeper into the emotion. Furthermore, when the user is in a hurry, the determination unit can also determine a dialogue for quickly responding. The response unit executes the dialogue determined by the determination unit. For example, when the user is feeling stressed, the response unit performs a dialogue for relaxing the user. Furthermore, when the user is relaxed, the response unit can also perform a dialogue for digging deeper into the emotion. Furthermore, when the user is in a hurry, the response unit can also perform a dialogue for quickly responding. This allows the system to perform a dialogue according to the user's emotion.

[0072] The determination unit determines a dialogue in a mode corresponding to the user's emotion as a response, and the response unit can perform the dialogue in a mode corresponding to the user's emotion. For example, when the user is stressed, the determination unit determines a dialogue in a gentle mode for relaxing the user. Furthermore, when the user is relaxed, the determination unit can also determine a dialogue in an active mode for digging deeper into the emotion. Furthermore, when the user is in a hurry, the determination unit can also determine a dialogue in a concise mode for quickly responding. The response unit executes the dialogue in the mode determined by the determination unit. For example, when the user is stressed, the response unit performs a dialogue in a gentle mode for relaxing the user. Furthermore, when the user is relaxed, the response unit can also perform a dialogue in an active mode for digging deeper into the emotion. Furthermore, when the user is in a hurry, the response unit can also perform a dialogue in a concise mode for quickly responding. This allows the system to perform a dialogue in a mode corresponding to the user's emotion.

[0073] The response unit can conduct a dialogue based on a counseling program according to the user's emotions. For example, when the user is feeling stressed, the response unit conducts a dialogue based on a counseling program including relaxation techniques. Furthermore, when the user is relaxed, the response unit can also conduct a dialogue based on a counseling program for digging deeper into the user's emotions. Furthermore, when the user is in a hurry, the response unit can also conduct a dialogue based on a counseling program for responding quickly. This allows the system to conduct a dialogue based on a counseling program according to the user's emotions.

[0074] The corresponding unit can suggest a relaxation technique according to the user's emotion. For example, when the user feels stressed, the corresponding unit can suggest a relaxation technique such as deep breathing or meditation. Also, when the user is relaxed, the corresponding unit can suggest a relaxation technique such as yoga or stretching. Furthermore, when the user is in a hurry, the corresponding unit can suggest a relaxation technique that is effective in a short time. In this way, the system can suggest a relaxation technique according to the user's emotion.

[0075] The determination unit can determine to provide different music or images depending on the user's emotions. For example, when the user is feeling stressed, the determination unit determines to provide relaxing music or images of natural scenery. In addition, when the user is relaxed, the determination unit can also determine to provide music or images for digging deeper into emotions. Furthermore, when the user is in a hurry, the determination unit can also determine to provide music or images for a quick response. In this way, the system can determine to provide different music or images depending on the user's emotions.

[0076] The determination unit can determine a response according to the user's emotion based on the estimated user's emotion and the user's attributes. The determination unit determines a response according to the emotion based on attributes such as the user's age, sex, and hobbies. For example, if the user is young and feeling stressed, games and activities can be suggested. Also, if the user is elderly and relaxing, quiet music and reading can be suggested. Furthermore, if the user has a specific hobby, activities related to the hobby can be suggested. This allows the system to determine a response based on the user's emotion and attributes.

[0077] The acquisition unit can acquire one or more pieces of information among the user's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. For example, the acquisition unit captures the user's facial expression using a camera to acquire facial expression data. The acquisition unit can also record the user's voice using a microphone to acquire voice data. Furthermore, the acquisition unit can measure the user's heart rate using a heart rate sensor to acquire biometric information. This allows the system to acquire one or more pieces of information among the user's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information.

[0078] The counseling robot can function as a counseling robot having an acquisition unit, an estimation unit, a determination unit, and a response unit. The counseling robot has an acquisition unit, an estimation unit, a determination unit, and a response unit, estimates the user's emotions, and responds according to the emotions. For example, the counseling robot acquires the user's facial expressions and voice and estimates the emotions. Based on the estimated emotions, the counseling robot can suggest relaxation techniques and dialogue. The counseling robot can also measure the user's heart rate and blood pressure and evaluate the stress level. Furthermore, the counseling robot can provide music and images according to the user's emotions. This allows the counseling robot to grasp the user's emotions in detail and respond appropriately.

[0079] The acquisition unit can estimate the emotion and adjust the timing of acquiring the emotion deduction information based on the estimated emotion. For example, when the user is feeling stressed, the acquisition unit increases the frequency of acquiring the emotion deduction information and tracks changes in emotion in real time. Also, when the user is relaxed, the acquisition unit can reduce the frequency of acquiring the emotion deduction information and acquire the minimum amount of information necessary. Furthermore, when the user is in a hurry, the acquisition unit can shorten the timing of acquiring the emotion deduction information and quickly infer the emotion. This allows the system to adjust the timing of acquiring the emotion deduction information based on the user's emotion.

[0080] The acquisition unit can analyze the user's past emotion data and select the optimal acquisition method. For example, the acquisition unit analyzes time periods when the user felt stress in the past and acquires emotion estimation information focusing on those time periods. The acquisition unit can also find a tendency to react to a specific trigger (e.g., specific music) from the user's past emotion data and acquire information based on that trigger. Furthermore, the acquisition unit can enhance information acquisition in situations where emotions change drastically (e.g., during a meeting) based on the user's past emotion data. This allows the system to analyze the user's past emotion data and select the optimal acquisition method.

[0081] The acquisition unit can perform filtering based on the user's current activity status and environment when acquiring the emotion estimation information. For example, when the user is exercising, the acquisition unit prioritizes acquiring heart rate and motion data. In addition, when the user is in a quiet environment, the acquisition unit can also prioritize acquiring voice data. Furthermore, when the user is in a meeting, the acquisition unit can also prioritize acquiring facial expression data. This allows the system to filter the emotion estimation information based on the user's current activity status and environment.

[0082] The acquisition unit can estimate the user's emotion and determine the priority of emotion estimating information to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit prioritizes acquisition of heart rate and blood pressure data. In addition, when the user is relaxed, the acquisition unit can also prioritize acquisition of facial expression and voice data. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquisition of motion data. This allows the system to determine the priority of emotion estimating information based on the user's emotion.

[0083] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user. For example, when the user is at home, the acquisition unit preferentially acquires data based on a relaxed environment. In addition, when the user is at work, the acquisition unit can also preferentially acquire data indicating signs of stress. Furthermore, when the user is in a public place, the acquisition unit can also preferentially acquire surrounding voice and movement data. This allows the system to preferentially acquire highly relevant information in consideration of the geographical location information of the user.

[0084] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the emotion deduction information. For example, when the user posts on social media that he / she is feeling stressed, the acquisition unit acquires emotion deduction information based on the content of the post. In addition, when the user posts on social media that he / she is relaxing, the acquisition unit can also acquire emotion deduction information based on the content of the post. Furthermore, when the user posts on social media that he / she is in a hurry, the acquisition unit can also acquire emotion deduction information based on the content of the post. This allows the system to analyze the user's social media activity and acquire related information.

[0085] The estimation unit can estimate the user's emotion and adjust the emotion estimation algorithm based on the estimated user's emotion. For example, when the user is feeling stressed, the estimation unit applies an emotion estimation algorithm specialized for stress. Also, when the user is relaxed, the estimation unit can apply an emotion estimation algorithm specialized for relaxation. Furthermore, when the user is in a hurry, the estimation unit can apply an algorithm that quickly estimates emotion. This allows the system to adjust the emotion estimation algorithm based on the user's emotion.

[0086] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. The estimation unit performs detailed emotion estimation based on important data such as heart rate and blood pressure. The estimation unit can also perform simple emotion estimation based on auxiliary data such as facial expression and voice. Furthermore, the estimation unit can also perform emotion estimation with an appropriate level of detail based on motion data. This allows the system to adjust the level of detail of the estimation based on the importance of the emotion estimation information.

[0087] The estimation unit can apply different estimation algorithms depending on the emotion category at the time of estimation. For example, when the user is feeling stressed, the estimation unit applies an algorithm specialized for stress. Also, when the user is relaxed, the estimation unit can apply an algorithm specialized for relaxation. Furthermore, when the user is in a hurry, the estimation unit can apply an algorithm that quickly estimates the emotion. This allows the system to apply different estimation algorithms depending on the emotion category.

[0088] The estimation unit can estimate the user's emotion and adjust the display method of the estimation result based on the estimated user's emotion. For example, when the user is feeling stressed, the estimation unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the estimation unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the estimation unit can also provide a display method that focuses on the main points. This allows the system to adjust the display method of the estimation result based on the user's emotion.

[0089] The estimation unit can determine the priority order of estimation at the time of estimation based on the acquisition time of the emotion estimation information. For example, the estimation unit performs estimation by prioritizing the latest emotion estimation information. The estimation unit can also perform estimation by focusing on current information while referring to past emotion estimation information. Furthermore, the estimation unit can also perform estimation by prioritizing emotion estimation information acquired in a specific time period. This allows the system to determine the priority order of estimation based on the acquisition time of the emotion estimation information.

[0090] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by prioritizing information that is most relevant to the user's current situation. The estimation unit can also perform estimation by prioritizing information that is highly relevant to the user's past emotion data. Furthermore, the estimation unit can also perform estimation by prioritizing information that is most relevant to the user's environment. This allows the system to adjust the order of estimation based on the relevance of the emotion estimation information.

[0091] The determination unit can estimate the user's emotion and adjust the response determination method based on the estimated user's emotion. For example, when the user is feeling stressed, the determination unit preferentially suggests relaxation techniques. In addition, when the user is relaxed, the determination unit can also suggest a method of digging deeper into the emotion through dialogue. Furthermore, when the user is in a hurry, the determination unit can also suggest a method of responding quickly. This allows the system to adjust the response determination method based on the user's emotion.

[0092] At the time of making a decision, the determination unit can analyze the user's past response history and select the optimal response method. For example, if the user has liked relaxation techniques in the past, the determination unit can give priority to and suggest them. Also, if the user has liked dialogue in the past, the determination unit can also give priority to and suggest them. Furthermore, if the user has liked music or video in the past, the determination unit can also give priority to and suggest them. This allows the system to analyze the user's past response history and select the optimal response method.

[0093] At the time of the determination, the decision unit can customize the response measures based on the user's current situation. For example, the decision unit can suggest short and effective relaxation techniques when the user is at work. The decision unit can also suggest long relaxation techniques when the user is at home. Furthermore, the decision unit can also suggest a response in an unobtrusive manner when the user is in a public place. This allows the system to customize the response measures based on the user's current situation.

[0094] The determination unit can estimate the user's emotion and determine the priority of the response based on the estimated user's emotion. For example, when the user is feeling stressed, the determination unit can give top priority to suggesting a relaxation technique. Also, when the user is relaxed, the determination unit can give top priority to a method of digging deeper into the emotion through dialogue. Furthermore, when the user is in a hurry, the determination unit can give top priority to a method that allows a quick response. This allows the system to determine the priority of the response based on the user's emotion.

[0095] At the time of making the decision, the decision unit can select the optimal response method in consideration of the geographical location information of the user. For example, when the user is at home, the decision unit can suggest relaxing music or video. Furthermore, when the user is at work, the decision unit can also suggest short and effective relaxation techniques. Furthermore, when the user is in a public place, the decision unit can also suggest an unobtrusive response method. This allows the system to select the optimal response method in consideration of the geographical location information of the user.

[0096] The response unit can estimate the user's emotion and adjust the response execution method based on the estimated user's emotion. For example, when the user is feeling stressed, the response unit prioritizes and executes relaxation techniques. Also, when the user is relaxed, the response unit can execute a method of digging deeper into the emotion through dialogue. Furthermore, when the user is in a hurry, the response unit can execute a method that allows a quick response. This allows the system to adjust the response execution method based on the user's emotion.

[0097] The response unit can select the optimal execution method by referring to the user's past response history when responding. For example, if the user has liked relaxation techniques in the past, the response unit can execute them with priority. Also, if the user has liked dialogue in the past, the response unit can execute them with priority. Furthermore, if the user has liked music or video in the past, the response unit can execute them with priority. This allows the system to select the optimal execution method by referring to the user's past response history.

[0098] The response unit can customize the response measures based on the user's current situation when responding. For example, the response unit can perform short, effective relaxation techniques when the user is at work. The response unit can also perform long, effective relaxation techniques when the user is at home. Furthermore, the response unit can perform a response in an unobtrusive manner when the user is in a public place. This allows the system to customize the response measures based on the user's current situation.

[0099] The response unit can estimate the user's emotion and determine the priority of the response based on the estimated user's emotion. For example, when the user is feeling stressed, the response unit can execute a relaxation technique as a top priority. Also, when the user is relaxed, the response unit can also execute a method of digging deeper into the emotion through dialogue as a top priority. Furthermore, when the user is in a hurry, the response unit can also execute a method that allows a quick response as a top priority. This allows the system to determine the priority of the response based on the user's emotion.

[0100] The response unit can select the optimal response method in consideration of the geographical location information of the user when responding. For example, when the user is at home, the response unit can provide relaxing music and images. Furthermore, when the user is at work, the response unit can also provide short and effective relaxation techniques. Furthermore, when the user is in a public place, the response unit can also provide an unobtrusive response. This allows the system to select the optimal response method in consideration of the geographical location information of the user.

[0101] The response unit can analyze the user's social media activity and suggest a means of response when responding. For example, if the user posts on social media that he / she feels stressed, the response unit can provide a relaxation technique. Also, if the user posts on social media that he / she feels relaxed, the response unit can provide a method of digging deeper into the emotion through dialogue. Furthermore, if the user posts on social media that he / she feels rushed, the response unit can provide a method of responding quickly. This allows the system to analyze the user's social media activity and suggest a means of response.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When acquiring the emotion estimation information of the user, the acquisition unit can analyze the user's past behavioral patterns and determine the optimal acquisition timing. For example, if the user was prone to feeling stress during a specific time period in the past, emotion estimation information is acquired with a focus on that time period. In addition, if the user's emotions fluctuate greatly during a specific activity (e.g., exercise, work), emotion estimation information can also be acquired during that activity. Furthermore, if the user's emotions fluctuate greatly in a specific location (e.g., home, workplace), emotion estimation information at that location can also be acquired with priority. This allows the system to optimize the timing of acquiring emotion estimation information based on the user's past behavioral patterns.

[0104] When estimating the user's emotion, the estimation unit can adjust the estimation algorithm based on the user's current activity status and environment. For example, when the user is exercising, the emotion can be estimated by prioritizing the heart rate and motion data. When the user is in a quiet environment, the emotion can be estimated by prioritizing the voice data and facial expression data. Furthermore, when the user is in a meeting, the emotion can be estimated by prioritizing the facial expression data and voice data. This allows the system to optimize the emotion estimation algorithm based on the user's current activity status and environment.

[0105] When determining a response according to the user's emotion, the determination unit can analyze the user's past emotion data and select the optimal response method. For example, if the user liked relaxation techniques in the past, it can give priority to suggesting them. Also, if the user liked dialogue in the past, it can give priority to suggesting them. Furthermore, if the user liked music or video in the past, it can give priority to suggesting them. In this way, the system can analyze the user's past emotion data and select the optimal response method.

[0106] When performing a response according to the user's emotion, the response unit can customize the response measures based on the user's current situation. For example, when the user is at work, a short and effective relaxation technique can be performed. Also, when the user is at home, a long relaxation technique can be performed. Furthermore, when the user is in a public place, a response in an unobtrusive manner can be performed. This allows the system to customize the response measures based on the user's current situation.

[0107] When acquiring the emotion deduction information of the user, the acquisition unit can analyze the user's social media activity and acquire related information. For example, if the user posts on social media that he / she is feeling stressed, the emotion deduction information can be acquired based on the content of the post. Also, if the user posts on social media that he / she is relaxing, the emotion deduction information can be acquired based on the content of the post. Furthermore, if the user posts on social media that he / she is in a hurry, the emotion deduction information can be acquired based on the content of the post. In this way, the system can analyze the user's social media activity and acquire related information.

[0108] The estimation unit can estimate the user's emotion and adjust the emotion estimation algorithm based on the estimated user's emotion. For example, when the user is stressed, an emotion estimation algorithm specialized for stress can be applied. When the user is relaxed, an emotion estimation algorithm specialized for relaxation can also be applied. Furthermore, when the user is in a hurry, an algorithm for quickly estimating emotion can also be applied. This allows the system to adjust the emotion estimation algorithm based on the user's emotion.

[0109] The decision unit can estimate the user's emotion and adjust the response decision method based on the estimated user's emotion. For example, if the user is feeling stressed, relaxation techniques can be suggested with priority. Also, if the user is relaxed, a method of digging deeper into the emotion through dialogue can be suggested. Furthermore, if the user is in a hurry, a method of responding quickly can be suggested. This allows the system to adjust the response decision method based on the user's emotion.

[0110] The response unit can estimate the user's emotion and adjust the method of executing the response based on the estimated user's emotion. For example, if the user is feeling stressed, a relaxation technique can be executed with priority. Also, if the user is relaxed, a method of digging deeper into the emotion through dialogue can be executed. Furthermore, if the user is in a hurry, a method of responding quickly can be executed. This allows the system to adjust the method of executing the response based on the user's emotion.

[0111] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated user's emotions. For example, if the user is feeling stressed, relaxation techniques can be executed with the highest priority. Also, if the user is relaxed, a method of digging deeper into the user's emotions through dialogue can be executed with the highest priority. Furthermore, if the user is in a hurry, a method that allows a quick response can be executed with the highest priority. This allows the system to determine the priority of responses based on the user's emotions.

[0112] The estimation unit can estimate the user's emotion and adjust the display method of the estimation result based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the system to adjust the display method of the estimation result based on the user's emotion.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a user. The emotion estimation information includes facial expression data, voice data, biometric information, and the like. For example, a camera is used to capture the user's facial expression to acquire facial expression data. A microphone can also be used to record the user's voice to acquire voice data. Furthermore, a heart rate sensor can be used to measure the user's heart rate to acquire biometric information. Step 2: The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. A machine learning algorithm or a rule-based estimation method is used to estimate the emotion. For example, the user's emotion is estimated from facial expression data using a machine learning algorithm. The user's emotion can also be estimated from voice data and biometric information. Step 3: The determination unit determines a response according to the user's emotion estimated by the estimation unit. The response includes dialogue, action, notification, etc. For example, a dialogue according to the user's emotion is determined. It is also possible to suggest relaxation techniques or provide music or video. Step 4: The response unit executes the response determined by the determination unit. For example, the response unit executes a dialogue according to the user's emotions. It can also execute relaxation techniques or provide music or video.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] For example, the acquisition unit can acquire the user's facial expression and voice using the camera 42 and microphone 38B of the smart device 14, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion based on the emotion estimation information obtained from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the user's emotion estimated by the estimation unit. The response unit is realized, for example, by the control unit 46A of the smart device 14, and executes the response determined by the determination unit. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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, the RAM 30, and the 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.

[0122] 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.

[0123] 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 the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] 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 performed in a secure state.

[0126] Fig. 4 shows an example of 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.

[0127] 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.

[0128] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0132] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] For example, the acquisition unit can acquire the user's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion based on the emotion estimation information obtained from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the user's emotion estimated by the estimation unit. The response unit is realized, for example, by the control unit 46A of the smart glasses 214, and executes the response determined by the determination unit. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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, the RAM 30, and the 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.

[0138] 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.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] 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 performed in a secure state.

[0142] 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.

[0143] 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.

[0144] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 voice indicating a user input for 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.

[0148] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] For example, the acquisition unit can acquire the user's facial expression and voice using the camera 42 and microphone 238 of the headset type terminal 314, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion based on the emotion estimation information obtained from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the user's emotion estimated by the estimation unit. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314, and executes the response determined by the determination unit. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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, the RAM 30, and the 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.

[0154] 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.

[0155] 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 the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] 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 performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0159] 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.

[0160] 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.

[0161] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0163] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] 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 a voice indicating a user input for 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.

[0165] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0167] For example, the acquisition unit can acquire the user's facial expression and voice using the camera 42 and microphone 238 of the robot 414, and acquire emotion estimation information by the specific processing unit 290 of the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion based on the emotion estimation information obtained from the acquisition unit. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a response according to the user's emotion estimated by the estimation unit. The response unit is realized, for example, by the control unit 46A of the robot 414, and executes the response determined by the determination unit. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0175] 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.

[0176] 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 Universal Serial Bus (USB) 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.

[0177] In addition, 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 upon request from the data processing device 12.

[0178] 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.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0181] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a user; an estimation unit that estimates a user's emotion based on the acquired emotion estimation information; A determination unit that determines a response according to the emotion of the user estimated by the estimation unit; A response unit that executes the determined response. A system characterized by: (Appendix 2) The determination unit is A dialogue is determined as a response according to the user's emotion, The corresponding portion is The dialogue is carried out according to the emotion of the user. 2. The system of claim 1, (Appendix 3) The determination unit is determining a dialogue in a manner corresponding to the user's emotion as a response; The corresponding portion is Dialogue with the user in a manner corresponding to the user's emotion 2. The system of claim 1, (Appendix 4) The corresponding portion is Conduct dialogue based on a counseling program according to the user's emotions 2. The system of claim 1, (Appendix 5) The corresponding portion is Proposing relaxation techniques according to the user's emotions 2. The system of claim 1, (Appendix 6) The determination unit is Depending on the user's emotion, different music or video is provided as a response. 2. The system of claim 1, (Appendix 7) The determination unit is Decide how to respond to a user's emotions based on the estimated user's emotions and user attributes. 2. The system of claim 1, (Appendix 8) The acquisition unit is One or more pieces of information from the user's facial expression, movement, voice, heart rate, and blood pressure are acquired as emotion estimation information. 2. The system of claim 1, (Appendix 9) The present invention functions as a counseling robot having an acquisition unit, an estimation unit, a decision unit, and a response unit. 2. The system of claim 1, (Appendix 10) The acquisition unit is To estimate an emotion and adjust timing of acquiring information for emotion estimation based on the estimated emotion. 2. The system of claim 1, (Appendix 11) The acquisition unit is Analyze the user's past emotion data and select the optimal acquisition method 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring emotion estimation information, filtering is performed based on the user's current activity status and environment. 2. The system of claim 1, (Appendix 13) The acquisition unit is A user's emotion is estimated, and a priority order of emotion estimation information to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is highly relevant to the user by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 15) The acquisition unit is When acquiring emotion estimation information, the system analyzes the user's social media activity and acquires related information. 2. The system of claim 1, (Appendix 16) The estimation unit is Estimating a user's emotion and adjusting an emotion estimation algorithm based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The estimation unit is During estimation, the level of detail of the estimation is adjusted based on the importance of the information for emotion estimation. 2. The system of claim 1, (Appendix 18) The estimation unit is During estimation, different estimation algorithms are applied depending on the emotion category. 2. The system of claim 1, (Appendix 19) The estimation unit is Inferring a user's emotions and adjusting the display method of the inferred results based on the inferred user's emotions 2. The system of claim 1, (Appendix 20) The estimation unit is During estimation, the priority of estimation is determined based on the time when emotion estimation information was acquired. 2. The system of claim 1, (Appendix 21) The estimation unit is During estimation, the order of estimation is adjusted based on the relevance of emotion estimation information. 2. The system of claim 1, (Appendix 22) The determination unit is Inferring a user's emotions and adjusting a response decision method based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The determination unit is When making a decision, analyze the user's past response history to select the optimal response method. 2. The system of claim 1, (Appendix 24) The determination unit is When making a decision, customize the response based on the user's current situation 2. The system of claim 1, (Appendix 25) The determination unit is Estimate the user's emotions and prioritize responses based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The determination unit is When making decisions, consider the user's geographic location to select the best course of action. 2. The system of claim 1, (Appendix 27) The corresponding portion is Inferring a user's emotions and adjusting how a response is executed based on the inferred user's emotions 2. The system of claim 1, (Appendix 28) The corresponding portion is When responding, select the optimal execution method by referring to the user's past response history 2. The system of claim 1, (Appendix 29) The corresponding portion is When responding, customize the response based on the user's current situation 2. The system of claim 1, (Appendix 30) The corresponding portion is Estimate the user's emotions and prioritize responses based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The corresponding portion is When responding, the system considers the user's geographic location to determine the optimal response method. 2. The system of claim 1, (Appendix 32) The corresponding portion is When responding, analyze users' social media activity to suggest ways to respond 2. The system of claim 1, [Explanation of symbols]

[0187] 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. An acquisition unit that acquires emotion estimation information for estimating an emotion of a user; an estimation unit that estimates an emotion of the user based on the acquired emotion estimation information; A determination unit that determines a response according to the emotion of the user estimated by the estimation unit; A response unit that executes the determined response. A system characterized by:

2. The determination unit is determining a dialogue corresponding to the user's emotion as a response; The corresponding portion is The dialogue is carried out according to the emotion of the user.

2. The system of claim 1 .

3. The determination unit is determining a dialogue in a manner corresponding to the emotion of the user as a response; The corresponding portion is Dialogue with the user in a manner corresponding to the user's emotion 2. The system of claim 1 .

4. The corresponding portion is A dialogue is carried out based on a counseling program according to the user's feelings.

2. The system of claim 1 .

5. The corresponding portion is Suggest relaxation techniques according to the user's emotions 2. The system of claim 1 .

6. The determination unit is Determining whether to provide different music or video depending on the user's emotion.

2. The system of claim 1 .

7. The determination unit is A response according to the user's emotion is determined based on the emotion of the user estimated by the estimation unit and an attribute of the user.

2. The system of claim 1 .

8. The acquisition unit is One or more pieces of information on the user's facial expression, movement, voice, heart rate, and blood pressure are acquired as emotion estimation information.

2. The system of claim 1 .

9. The acquisition unit, the estimation unit, the determination unit, and the corresponding unit function as a counseling robot.

2. The system of claim 1 .

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