System

Abstract

To provide a system that estimates emotions of users with intellectual disabilities and provides appropriate responses according thereto.SOLUTION: A system comprises an acquisition unit, an estimation unit, a determination unit, and a response unit. The acquisition unit acquires emotion estimation information for estimating emotions of users with intellectual disabilities. The estimation unit estimates the emotion of a user on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines a response according to the emotion of the user estimated by the estimation unit. The response unit executes the response determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, it was difficult to accurately estimate the emotions of users with intellectual disabilities and respond appropriately accordingly.

[0005] The system according to the embodiment aims to estimate the emotions of a user with an intellectual disability and respond appropriately accordingly. [Means for solving the problem]

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

[0007] The system according to the embodiment can estimate the emotions of a user with an intellectual disability and respond appropriately 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, an aperture, and a 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) A system according to an embodiment of the present invention is a system that estimates the emotion of a user with an intellectual disability and responds according to the emotion. This system acquires emotion estimation information for estimating the emotion of a user with an intellectual disability, and estimates the emotion of the user based on the acquired emotion estimation information. A response is determined according to the estimated emotion, and the response is executed. For example, if the user feels sad, the agent can speak words of comfort to the user. Also, if the user is excited, the agent can provide music to calm the user. In this way, the agent responds according to the emotion of the user, improving the quality of communication with people with intellectual disabilities. Furthermore, the agent acquires information such as the user's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. This allows the user's emotion to be estimated more accurately. In addition, the agent determines a response according to the emotion, taking into account the user's attributes (age, sex, past behavior history, etc.). This system makes communication with people with intellectual disabilities more natural and effective, and enables support for social participation. This allows the quality of communication to be improved by estimating the emotion of a user with an intellectual disability and responding according to the emotion.

[0029] The emotion estimation 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 with an intellectual disability. 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 captures the user's facial expression with a camera and acquires it as emotion estimation information using a facial expression recognition technology. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using a voice analysis technology. Furthermore, the acquisition unit can measure the user's heart rate and blood pressure with a sensor and include it in the emotion estimation information as biometric information. The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the emotion estimation information using, for example, AI and estimates the user's emotion. The AI ​​analyzes the emotion estimation information using, for example, a machine learning algorithm and estimates the user's emotion. The determination unit decides a response according to the user's emotion estimated by the estimation unit. The determination unit determines a response according to the user's emotion using, for example, AI. The AI ​​determines a response according to the user's emotion using, for example, a rule-based algorithm. The response unit executes the response determined by the determination unit. The response unit executes a response such as, for example, speaking comforting words to the user, playing music, or displaying a video. As a result, the emotion estimation system according to the embodiment can improve the quality of communication by estimating the emotion of a user with an intellectual disability and taking a response according to the emotion.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of a user with an intellectual disability. The emotion estimation information includes, but is not limited to, facial expression data, voice data, and biometric information. For example, the acquisition unit captures the user's facial expression with a camera and acquires it as emotion estimation information using a facial expression recognition technology. Specifically, a high-resolution camera is used to capture subtle changes in the user's face, and emotions such as smile, anger, and sadness are identified using a facial expression recognition algorithm using deep learning. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using a voice analysis technology. The voice data is used to analyze features such as the tone, pitch, speed, and volume of the user's voice and detect changes in emotion. Furthermore, the acquisition unit can measure the user's heart rate and blood pressure with a sensor and include them in the emotion estimation information as biometric information. This includes using a wearable device or a smart watch to monitor fluctuations in heart rate and blood pressure in real time and detect stress and excitement. This allows the acquisition unit to collect multifaceted data and comprehensively grasp the user's emotional state. Furthermore, the acquisition unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the estimation unit and decision unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to collect data efficiently and effectively and improve 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. The estimation unit, for example, uses AI to analyze the emotion estimation information and estimate the user's emotion. Specifically, a neural network model using deep learning is used to comprehensively analyze facial expression data, voice data, and biometric information. For facial expression data, a convolutional neural network (CNN) is used to extract facial features and classify emotions. For voice data, a recurrent neural network (RNN) or a long short-term memory (LSTM) model is used to capture temporal changes in voice and estimate emotions. For biometric information, a statistical method or machine learning algorithm is used to analyze the fluctuation patterns of heart rate and blood pressure and estimate stress and relaxation states. This allows the estimation unit to quickly and accurately analyze the collected data and grasp the user's emotional state in real time. 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 situations and time periods and devise future countermeasures. The estimation unit can also use an anomaly detection algorithm to detect unusual emotion patterns and 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 determination unit determines a response according to the user's emotion using, for example, AI. Specifically, the determination unit selects the optimal response to the user's emotion using a rule-based algorithm or reinforcement learning. In the rule-based algorithm, a response to a specific emotion is determined based on a predefined rule set. For example, responses include providing words of comfort when the user is sad, and playing music to calm the user when the user is angry. In the reinforcement learning, the optimal response is learned based on past response results and sequentially improved. This allows the determination unit to provide a flexible and effective response according to the user's emotion. Furthermore, the determination unit can determine a more personalized response by taking into account the individual characteristics and past history of the user. For example, if a particular user likes a particular music, playing that music provides more effective emotional care. In addition, the determination unit can set evaluation criteria for evaluating multiple response options and selecting the most appropriate response. This allows the determination unit to quickly and accurately determine the optimal response to the user's emotion and maximize the effectiveness of the entire system.

[0033] The response unit executes the response determined by the determination unit. The response unit executes responses such as, for example, speaking comforting words to the user, playing music, and displaying images. Specifically, the response unit can use voice synthesis technology to speak appropriate words to the user. Voice synthesis technology generates voices with natural pronunciation and intonation, providing friendly communication to the user. In addition, the music playback function can be used to select and play music according to the user's emotions. For example, if the user wants to relax, calm music is played, and if the user wants to cheer up, lively music is played. Furthermore, the image display function can be used to display images according to the user's emotions. For example, if the user is depressed, encouraging messages and fun images are displayed, and if the user wants to relax, natural scenery and healing images are displayed. This allows the response unit to execute various responses according to the user's emotions and improve the user's emotional state. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of the response content. For example, the response unit records how the user reacted to a specific response, and adjusts the response algorithm based on the data. In addition, the response unit can reliably transmit information using multiple communication means. For example, by using not only voice notifications but also text messages and visual alerts, important information can be delivered reliably, allowing the department to provide a quick and reliable response to the user and improve the quality of emotional care.

[0034] The determination unit determines a dialogue according to the user's emotion as a response according to the user's emotion, and the response unit can perform a dialogue according to the user's emotion. For example, when the user feels sad, the determination unit determines a dialogue to offer comforting words. Also, when the user is excited, the determination unit can determine a dialogue to offer gentle words to calm the user. The response unit executes the dialogue determined by the determination unit. For example, the response unit asks the user, "Are you okay?" in a gentle voice. Also, the response unit can speak to the user, "Please calm down," in a gentle voice. This enables more natural communication by performing a dialogue according to the user's emotion. A part or all of the above-mentioned processing in the determination unit and the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data into the AI, and the AI ​​can determine an appropriate dialogue. The response unit can execute the dialogue determined by the AI.

[0035] The determination unit determines a dialogue in a manner corresponding to the user's emotion as a response corresponding to the user's emotion, and the response unit can perform the dialogue in a manner corresponding to the user's emotion. For example, when the user feels sad, the determination unit determines a dialogue in which the user is spoken to in a gentle tone. Also, when the user is excited, the determination unit can determine a dialogue in which the user is spoken to in a gentle tone. The response unit executes the dialogue in the manner determined by the determination unit. For example, the response unit asks the user, "Are you okay?" in a gentle tone. Also, the response unit can speak to the user, "Please calm down," in a gentle tone. This enables a more appropriate response by performing the dialogue in a manner corresponding to the user's emotion. A part or all of the above-mentioned processing in the determination unit and the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data into the AI, and the AI ​​can determine an appropriate dialogue. The response unit can execute the dialogue in the manner determined by the AI.

[0036] The determination unit can determine the provision of different music or images according to the user's emotions as a response according to the user's emotions. For example, if the user feels sad, the determination unit can determine to provide relaxing music. Also, if the user is excited, the determination unit can determine to provide a calming image. This makes it possible to appropriately adjust the user's emotions by providing music and images according to the user's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data into AI, which can then determine appropriate music and images.

[0037] The determination unit can determine a response according to the user's emotion based on the user's emotion estimated by the estimation unit and the user's attributes. For example, when the user feels sad, the determination unit determines an appropriate response according to the user's age and gender. In addition, when the user is excited, the determination unit can also determine an appropriate response based on the user's past behavior history. This allows for a more individualized response by taking the user's attributes into consideration. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data and attribute data into AI, which can then determine an appropriate response.

[0038] 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 with a camera and acquires it as emotion estimation information using facial expression recognition technology. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using voice analysis technology. Furthermore, the acquisition unit can measure the user's heart rate and blood pressure with a sensor and include it in the emotion estimation information as bioinformation. This allows the user's emotion to be more accurately estimated by acquiring various information. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the user's facial expression data to AI, which can analyze it as emotion estimation information.

[0039] The acquisition unit can analyze the user's past emotion history and select the optimal acquisition method. For example, if the user felt stress in a particular situation in the past, the acquisition unit focuses on acquiring emotion estimation information in that situation. The acquisition unit can also analyze a time period in which the user was relaxed in the past and acquire emotion estimation information during that time period. Furthermore, the acquisition unit can identify an event in which the user was excited in the past and intensively acquire emotion estimation information during that event. This makes it possible to acquire information more effectively by analyzing the past emotion history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's past emotion data into an AI, which can select the optimal acquisition method.

[0040] 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 focuses on acquiring heart rate and motion information and uses it as emotion estimation information. In addition, when the user is in a quiet environment, the acquisition unit can also focus on acquiring voice information and use it as emotion estimation information. Furthermore, when the user is in a busy situation, the acquisition unit can also acquire a minimum amount of emotion estimation information to reduce the burden on the user. This makes it possible to obtain more relevant information by filtering information based on the user's activity status and environment. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's activity status data to AI, and the AI ​​can perform appropriate filtering.

[0041] 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 in a park, the acquisition unit preferentially acquires surrounding audio information. Furthermore, when the user is at home, the acquisition unit can also preferentially acquire indoor environmental information. Furthermore, when the user is at work, the acquisition unit can also preferentially acquire work-related information. In this way, by taking the geographical location information into consideration, more relevant information can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location data of the user to AI, and the AI ​​can preferentially acquire appropriate information.

[0042] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the emotion estimation information. For example, when the user posts a positive message on social media, the acquisition unit acquires the posted content as emotion estimation information. In addition, when the user posts a negative message on social media, the acquisition unit can also acquire the posted content as emotion estimation information. Furthermore, when the user participates in a specific event on social media, the acquisition unit can also acquire information related to the event as emotion estimation information. In this way, information related to the user's emotion can be acquired by analyzing the social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's social media data into an AI, which can acquire appropriate information.

[0043] 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. For example, when the heart rate or blood pressure information is important, the estimation unit performs a detailed estimation. Furthermore, when the facial expression or voice information is important, the estimation unit can also perform a detailed estimation. Furthermore, when the motion or environmental information is important, the estimation unit can also perform a detailed estimation. As a result, by adjusting the level of detail of the estimation based on the importance, more effective emotion estimation is possible. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit inputs the emotion estimation information to an AI, and the AI ​​can perform estimation with an appropriate level of detail.

[0044] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. For example, the estimation unit applies a physiological algorithm to the heart rate and blood pressure information. The estimation unit can also apply a psychological algorithm to the facial expression and voice information. Furthermore, the estimation unit can also apply a behavioral algorithm to the movement and environment information. This enables more accurate emotion estimation by applying an estimation algorithm according to the category. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the emotion estimation information to an AI, which can apply an appropriate algorithm.

[0045] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by preferentially using the latest emotion estimation information. The estimation unit can also perform estimation by referring to past emotion estimation information. Furthermore, the estimation unit can perform estimation by preferentially using emotion estimation information acquired in a specific time period. This enables more effective emotion estimation by determining the priority of estimation based on the acquisition time. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input emotion estimation information to an AI, which can perform estimation with appropriate priority.

[0046] 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 preferentially using emotion estimation information having high relevance. The estimation unit can also perform estimation by using emotion estimation information having a medium relevance next. Furthermore, the estimation unit can also perform estimation by using emotion estimation information having low relevance last. Thereby, by adjusting the order of estimation based on the relevance, more accurate emotion estimation is possible. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input emotion estimation information to AI, and the AI ​​can perform estimation in an appropriate order.

[0047] The determination unit can adjust the level of detail of the response based on the importance of the emotion when determining the response. For example, when the user shows a strong emotion, the determination unit determines a detailed response. In addition, when the user shows a medium emotion, the determination unit can also determine a response with a moderate level of detail. Furthermore, when the user shows a weak emotion, the determination unit can also determine a brief response. This allows a more appropriate response by adjusting the level of detail of the response based on the importance of the emotion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the AI, and the AI ​​can determine the response with an appropriate level of detail.

[0048] The determination unit can apply different response algorithms depending on the emotion category when determining the response. For example, if the user is sad, the determination unit applies a comforting algorithm. In addition, if the user is excited, the determination unit can also apply a calming algorithm. Furthermore, if the user is relaxed, the determination unit can apply an algorithm that provides a pleasant topic. This allows for a more appropriate response by applying a response algorithm according to the emotion category. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data into AI, which can then apply an appropriate algorithm.

[0049] The determination unit can determine the priority of the response based on the time when the emotion occurred when determining the response. The determination unit, for example, preferentially determines the response based on the latest emotion. The determination unit can also determine the next response based on the past emotion. Furthermore, the determination unit can also preferentially determine the response based on the emotion that occurred in a specific time period. This enables a more effective response by determining the priority of the response based on the time when the emotion occurred. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data to AI, and the AI ​​can determine the response with appropriate priority.

[0050] The determination unit can adjust the order of responses based on the relevance of emotions when determining responses. For example, the determination unit preferentially determines responses based on emotions with high relevance. The determination unit can also determine responses next based on emotions with medium relevance. Furthermore, the determination unit can also determine responses last based on emotions with low relevance. This allows for more appropriate responses by adjusting the order of responses based on the relevance of emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data into AI, which can determine responses in an appropriate order.

[0051] The response unit can select the optimal response method by referring to the user's past emotion history when performing the response. For example, the response unit re-executes a response method that was effective when the user felt sad in the past. The response unit can also re-execute a response method that was effective when the user was excited in the past. Furthermore, the response unit can also re-execute a response method that was effective when the user was relaxed in the past. This allows for a more effective response by referring to the past emotion history. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit inputs the user's past emotion data into an AI, which can select an appropriate response method.

[0052] The response unit can customize the response means based on the user's current living situation when performing the response. For example, when the user is at home, the response unit selects a response method that can be performed at home. In addition, when the user is out, the response unit can also select a response method that can be performed away from home. Furthermore, when the user is at work, the response unit can also select a response method that is suitable for the work environment. This allows for a more appropriate response by customizing the response means based on the current living situation. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the user's living situation data into AI, which can select an appropriate response means.

[0053] The response unit can select the optimal response method in consideration of the geographical location information of the user when performing the response. For example, when the user is in a park, the response unit plays natural sounds to relax the user. In addition, when the user is at home, the response unit can also select a response method that can be performed at home. Furthermore, when the user is at work, the response unit can also select a response method that is suitable for the work environment. This makes it possible to respond more appropriately by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the user's geographical location data into an AI, which can select an appropriate response method.

[0054] The response unit can analyze the user's social media activity and suggest a response measure when the response is executed. For example, when the user posts a positive message on social media, the response unit can suggest a response method related to the content of the post. In addition, when the user posts a negative message on social media, the response unit can also suggest a response method related to the content of the post. Furthermore, when the user participates in a specific event on social media, the response unit can also suggest a response method related to the event. In this way, by analyzing social media activity, more appropriate response measures can be suggested. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the user's social media data into AI, which can then suggest appropriate response measures.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The emotion estimation system may further include a learning unit. The learning unit accumulates the user's emotion estimation information and the corresponding results, and can improve the accuracy of the system using a machine learning algorithm. For example, the learning unit can learn the user's facial expression data and the emotion at that time to perform more accurate emotion estimation. The learning unit can also learn the user's voice data and the emotion at that time to improve the accuracy of voice analysis. The learning unit can also learn the user's biometric information and the emotion at that time to improve the accuracy of emotion estimation from the biometric information. This allows the system to more accurately estimate the user's emotion and take appropriate measures.

[0057] The emotion estimation system may further include a customization unit. The customization unit may adjust the settings of the system based on the individual preferences and needs of the user. For example, if the user prefers a particular piece of music, the music may be preferentially provided. Also, if the user prefers a particular wording, the system may be adjusted to use the wording. Furthermore, if the user prefers a particular response during a particular time period, the response may be provided during that time period. In this way, the customization unit may provide a response according to the individual preferences and needs of the user, thereby realizing more personalized communication.

[0058] The emotion estimation system may further include a recording unit. The recording unit may record the user's emotion estimation information and the response results over a long period of time and analyze them later. For example, the recording unit may record the user's emotion changes over a long period of time and analyze the emotion patterns. The recording unit may also record the user's response results and analyze which responses were effective. Furthermore, the user's emotion estimation information may be recorded and used for future research and improvement. In this way, the recording unit may accumulate data on the user's emotions and responses, and may be useful for improving and researching the system.

[0059] The emotion estimation system may further include an environment adjustment unit. The environment adjustment unit may adjust the surrounding environment according to the user's emotional state. For example, if the user is stressed, the lighting may be turned down to create a relaxing environment. If the user is excited, music may be played to calm the user. If the user is relaxed, a scent may be provided to create a more relaxing environment. In this way, the environment adjustment unit may adjust the surrounding environment according to the user's emotional state to provide a more comfortable environment.

[0060] The emotion estimation system may further include a communication support unit. The communication support unit may provide support when the user communicates with other people. For example, if the user is unable to express emotions well, the communication support unit may suggest appropriate words. Also, if the user has difficulty understanding the emotions of other people, the communication support unit may explain the emotions. Furthermore, if the user feels anxious about communication, the communication support unit may suggest ways to relax the user. In this way, the communication support unit may support the user's communication and realize smoother dialogue.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a user with an intellectual disability. The emotion estimation information includes, for example, facial expression data, voice data, and biometric information. The acquisition unit captures the user's facial expression with a camera and acquires it as emotion estimation information using facial expression recognition technology. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using voice analysis technology. Furthermore, the user's heart rate and blood pressure can be measured with a sensor and included in the emotion estimation information as biometric information. Step 2: The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the emotion estimation information using an AI and estimates the user's emotion. The AI ​​analyzes the emotion estimation information using a machine learning algorithm and estimates the user's emotion. Step 3: The determination unit determines a response according to the user's emotion estimated by the estimation unit. The determination unit determines a response according to the user's emotion using an AI. The AI ​​determines a response according to the user's emotion using a rule-based algorithm. Step 4: The response unit executes the response determined by the determination unit, such as offering words of comfort to the user, playing music, displaying a video, or the like.

[0063] (Example 2) A system according to an embodiment of the present invention is a system that estimates the emotion of a user with an intellectual disability and responds according to the emotion. This system acquires emotion estimation information for estimating the emotion of a user with an intellectual disability, and estimates the emotion of the user based on the acquired emotion estimation information. A response is determined according to the estimated emotion, and the response is executed. For example, if the user feels sad, the agent can speak words of comfort to the user. Also, if the user is excited, the agent can provide music to calm the user. In this way, the agent responds according to the emotion of the user, improving the quality of communication with people with intellectual disabilities. Furthermore, the agent acquires information such as the user's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. This allows the user's emotion to be estimated more accurately. In addition, the agent determines a response according to the emotion, taking into account the user's attributes (age, sex, past behavior history, etc.). This system makes communication with people with intellectual disabilities more natural and effective, and enables support for social participation. This allows the quality of communication to be improved by estimating the emotion of a user with an intellectual disability and responding according to the emotion.

[0064] The emotion estimation 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 with an intellectual disability. 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 captures the user's facial expression with a camera and acquires it as emotion estimation information using a facial expression recognition technology. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using a voice analysis technology. Furthermore, the acquisition unit can measure the user's heart rate and blood pressure with a sensor and include it in the emotion estimation information as biometric information. The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the emotion estimation information using, for example, AI and estimates the user's emotion. The AI ​​analyzes the emotion estimation information using, for example, a machine learning algorithm and estimates the user's emotion. The determination unit decides a response according to the user's emotion estimated by the estimation unit. The determination unit determines a response according to the user's emotion using, for example, AI. The AI ​​determines a response according to the user's emotion using, for example, a rule-based algorithm. The response unit executes the response determined by the determination unit. The response unit executes a response such as, for example, speaking comforting words to the user, playing music, or displaying a video. As a result, the emotion estimation system according to the embodiment can improve the quality of communication by estimating the emotion of a user with an intellectual disability and taking a response according to the emotion.

[0065] The acquisition unit acquires emotion estimation information for estimating the emotion of a user with an intellectual disability. The emotion estimation information includes, but is not limited to, facial expression data, voice data, and biometric information. For example, the acquisition unit captures the user's facial expression with a camera and acquires it as emotion estimation information using a facial expression recognition technology. Specifically, a high-resolution camera is used to capture subtle changes in the user's face, and emotions such as smile, anger, and sadness are identified using a facial expression recognition algorithm using deep learning. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using a voice analysis technology. The voice data is used to analyze features such as the tone, pitch, speed, and volume of the user's voice and detect changes in emotion. Furthermore, the acquisition unit can measure the user's heart rate and blood pressure with a sensor and include them in the emotion estimation information as biometric information. This includes using a wearable device or a smart watch to monitor fluctuations in heart rate and blood pressure in real time and detect stress and excitement. This allows the acquisition unit to collect multifaceted data and comprehensively grasp the user's emotional state. Furthermore, the acquisition unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the estimation unit and decision unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to collect data efficiently and effectively and improve the performance of the entire system.

[0066] The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the emotion estimation information and estimate the user's emotion. Specifically, a neural network model using deep learning is used to comprehensively analyze facial expression data, voice data, and biometric information. For facial expression data, a convolutional neural network (CNN) is used to extract facial features and classify emotions. For voice data, a recurrent neural network (RNN) or a long short-term memory (LSTM) model is used to capture temporal changes in voice and estimate emotions. For biometric information, a statistical method or machine learning algorithm is used to analyze the fluctuation patterns of heart rate and blood pressure and estimate stress and relaxation states. This allows the estimation unit to quickly and accurately analyze the collected data and grasp the user's emotional state in real time. 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 situations and time periods and devise future countermeasures. The estimation unit can also use an anomaly detection algorithm to detect unusual emotion patterns and 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.

[0067] The determination unit determines a response according to the user's emotion estimated by the estimation unit. The determination unit determines a response according to the user's emotion using, for example, AI. Specifically, the determination unit selects the optimal response to the user's emotion using a rule-based algorithm or reinforcement learning. In the rule-based algorithm, a response to a specific emotion is determined based on a predefined rule set. For example, responses include providing words of comfort when the user is sad, and playing music to calm the user when the user is angry. In the reinforcement learning, the optimal response is learned based on past response results and sequentially improved. This allows the determination unit to provide a flexible and effective response according to the user's emotion. Furthermore, the determination unit can determine a more personalized response by taking into account the individual characteristics and past history of the user. For example, if a particular user likes a particular music, playing that music provides more effective emotional care. In addition, the determination unit can set evaluation criteria for evaluating multiple response options and selecting the most appropriate response. This allows the determination unit to quickly and accurately determine the optimal response to the user's emotion and maximize the effectiveness of the entire system.

[0068] The response unit executes the response determined by the determination unit. The response unit executes responses such as, for example, speaking comforting words to the user, playing music, and displaying images. Specifically, the response unit can use voice synthesis technology to speak appropriate words to the user. Voice synthesis technology generates voices with natural pronunciation and intonation, providing friendly communication to the user. In addition, the music playback function can be used to select and play music according to the user's emotions. For example, if the user wants to relax, calm music is played, and if the user wants to cheer up, lively music is played. Furthermore, the image display function can be used to display images according to the user's emotions. For example, if the user is depressed, encouraging messages and fun images are displayed, and if the user wants to relax, natural scenery and healing images are displayed. This allows the response unit to execute various responses according to the user's emotions and improve the user's emotional state. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of the response content. For example, the response unit records how the user reacted to a specific response, and adjusts the response algorithm based on the data. In addition, the response unit can reliably transmit information using multiple communication means. For example, by using not only voice notifications but also text messages and visual alerts, important information can be delivered reliably, allowing the department to provide a quick and reliable response to the user and improve the quality of emotional care.

[0069] The determination unit determines a dialogue according to the user's emotion as a response according to the user's emotion, and the response unit can perform a dialogue according to the user's emotion. For example, when the user feels sad, the determination unit determines a dialogue to offer comforting words. Also, when the user is excited, the determination unit can determine a dialogue to offer gentle words to calm the user. The response unit executes the dialogue determined by the determination unit. For example, the response unit asks the user, "Are you okay?" in a gentle voice. Also, the response unit can speak to the user, "Please calm down," in a gentle voice. This enables more natural communication by performing a dialogue according to the user's emotion. A part or all of the above-mentioned processing in the determination unit and the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data into the AI, and the AI ​​can determine an appropriate dialogue. The response unit can execute the dialogue determined by the AI.

[0070] The determination unit determines a dialogue in a manner corresponding to the user's emotion as a response corresponding to the user's emotion, and the response unit can perform the dialogue in a manner corresponding to the user's emotion. For example, when the user feels sad, the determination unit determines a dialogue in which the user is spoken to in a gentle tone. Also, when the user is excited, the determination unit can determine a dialogue in which the user is spoken to in a gentle tone. The response unit executes the dialogue in the manner determined by the determination unit. For example, the response unit asks the user, "Are you okay?" in a gentle tone. Also, the response unit can speak to the user, "Please calm down," in a gentle tone. This enables a more appropriate response by performing the dialogue in a manner corresponding to the user's emotion. A part or all of the above-mentioned processing in the determination unit and the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data into the AI, and the AI ​​can determine an appropriate dialogue. The response unit can execute the dialogue in the manner determined by the AI.

[0071] The determination unit can determine the provision of different music or images according to the user's emotions as a response according to the user's emotions. For example, if the user feels sad, the determination unit can determine to provide relaxing music. Also, if the user is excited, the determination unit can determine to provide a calming image. This makes it possible to appropriately adjust the user's emotions by providing music and images according to the user's emotions. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data into AI, which can then determine appropriate music and images.

[0072] The determination unit can determine a response according to the user's emotion based on the user's emotion estimated by the estimation unit and the user's attributes. For example, when the user feels sad, the determination unit determines an appropriate response according to the user's age and gender. In addition, when the user is excited, the determination unit can also determine an appropriate response based on the user's past behavior history. This allows for a more individualized response by taking the user's attributes into consideration. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data and attribute data into AI, which can then determine an appropriate response.

[0073] 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 with a camera and acquires it as emotion estimation information using facial expression recognition technology. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using voice analysis technology. Furthermore, the acquisition unit can measure the user's heart rate and blood pressure with a sensor and include it in the emotion estimation information as bioinformation. This allows the user's emotion to be more accurately estimated by acquiring various information. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the user's facial expression data to AI, which can analyze it as emotion estimation information.

[0074] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the user. For example, when the user is feeling stressed, the acquisition unit increases the frequency of acquiring the emotion estimation information and tracks changes in emotion in real time. In addition, when the user is relaxed, the acquisition unit can also reduce the frequency of acquiring the emotion estimation information and acquire the minimum necessary information. Furthermore, when the user is excited, the acquisition unit can shorten the timing of acquiring the emotion estimation information so as to be able to respond quickly. This enables more appropriate information collection by adjusting the acquisition timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to an AI, and the AI ​​can determine an appropriate acquisition timing.

[0075] The acquisition unit can analyze the user's past emotion history and select the optimal acquisition method. For example, if the user felt stress in a particular situation in the past, the acquisition unit focuses on acquiring emotion estimation information in that situation. The acquisition unit can also analyze a time period in which the user was relaxed in the past and acquire emotion estimation information during that time period. Furthermore, the acquisition unit can identify an event in which the user was excited in the past and intensively acquire emotion estimation information during that event. This makes it possible to acquire information more effectively by analyzing the past emotion history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's past emotion data into an AI, which can select the optimal acquisition method.

[0076] 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 focuses on acquiring heart rate and motion information and uses it as emotion estimation information. In addition, when the user is in a quiet environment, the acquisition unit can also focus on acquiring voice information and use it as emotion estimation information. Furthermore, when the user is in a busy situation, the acquisition unit can also acquire a minimum amount of emotion estimation information to reduce the burden on the user. This makes it possible to obtain more relevant information by filtering information based on the user's activity status and environment. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's activity status data to AI, and the AI ​​can perform appropriate filtering.

[0077] The acquisition unit can estimate the user's emotion and determine the priority of emotion estimation information to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit preferentially acquires information on heart rate and blood pressure. Furthermore, when the user is relaxed, the acquisition unit can also preferentially acquire information on facial expressions and voice. Furthermore, when the user is excited, the acquisition unit can also preferentially acquire information on movements and voice. In this way, important information can be preferentially acquired by determining the priority of information based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to an AI, which can then determine an appropriate priority.

[0078] 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 in a park, the acquisition unit preferentially acquires surrounding audio information. Furthermore, when the user is at home, the acquisition unit can also preferentially acquire indoor environmental information. Furthermore, when the user is at work, the acquisition unit can also preferentially acquire work-related information. In this way, by taking the geographical location information into consideration, more relevant information can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the geographical location data of the user to AI, and the AI ​​can preferentially acquire appropriate information.

[0079] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the emotion estimation information. For example, when the user posts a positive message on social media, the acquisition unit acquires the posted content as emotion estimation information. In addition, when the user posts a negative message on social media, the acquisition unit can also acquire the posted content as emotion estimation information. Furthermore, when the user participates in a specific event on social media, the acquisition unit can also acquire information related to the event as emotion estimation information. In this way, information related to the user's emotion can be acquired by analyzing the social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's social media data into an AI, which can acquire appropriate information.

[0080] 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, the estimation unit sensitively adjusts the emotion estimation algorithm to detect subtle changes in emotion. In addition, when the user is relaxed, the estimation unit can also gently adjust the emotion estimation algorithm to detect rough changes in emotion. Furthermore, when the user is excited, the estimation unit can quickly adjust the emotion estimation algorithm to detect sudden changes in emotion. This allows for more accurate emotion estimation by adjusting the emotion estimation algorithm. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the user's emotion data to an AI, which can then perform appropriate algorithm adjustment.

[0081] 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. For example, when the heart rate or blood pressure information is important, the estimation unit performs a detailed estimation. Furthermore, when the facial expression or voice information is important, the estimation unit can also perform a detailed estimation. Furthermore, when the motion or environmental information is important, the estimation unit can also perform a detailed estimation. As a result, by adjusting the level of detail of the estimation based on the importance, more effective emotion estimation is possible. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit inputs the emotion estimation information to an AI, and the AI ​​can perform estimation with an appropriate level of detail.

[0082] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. For example, the estimation unit applies a physiological algorithm to the heart rate and blood pressure information. The estimation unit can also apply a psychological algorithm to the facial expression and voice information. Furthermore, the estimation unit can also apply a behavioral algorithm to the movement and environment information. This enables more accurate emotion estimation by applying an estimation algorithm according to the category. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the emotion estimation information to an AI, which can apply an appropriate algorithm.

[0083] The estimation unit can estimate the user's emotion and adjust the accuracy of the estimation based on the estimated user's emotion. For example, when the user is stressed, the estimation unit increases the accuracy of the estimation to detect detailed changes in emotion. Also, when the user is relaxed, the estimation unit can loosen the accuracy of the estimation to detect rough changes in emotion. Furthermore, when the user is excited, the estimation unit can quickly adjust the accuracy of the estimation to detect a sudden change in emotion. This allows for more accurate emotion estimation by adjusting the accuracy of the estimation. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit inputs the user's emotion data into the AI, and the AI ​​can perform estimation with appropriate accuracy.

[0084] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit performs estimation by preferentially using the latest emotion estimation information. The estimation unit can also perform estimation by referring to past emotion estimation information. Furthermore, the estimation unit can perform estimation by preferentially using emotion estimation information acquired in a specific time period. This enables more effective emotion estimation by determining the priority of estimation based on the acquisition time. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input emotion estimation information to an AI, which can perform estimation with appropriate priority.

[0085] 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 preferentially using emotion estimation information having high relevance. The estimation unit can also perform estimation by using emotion estimation information having a medium relevance next. Furthermore, the estimation unit can also perform estimation by using emotion estimation information having low relevance last. Thereby, by adjusting the order of estimation based on the relevance, more accurate emotion estimation is possible. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input emotion estimation information to AI, and the AI ​​can perform estimation in an appropriate order.

[0086] The determination unit can estimate the user's emotion and adjust the manner of expression of the response based on the estimated user's emotion. For example, when the user is sad, the determination unit determines the response of comforting the user with gentle words. In addition, when the user is excited, the determination unit can also determine the response of using gentle words to calm the user. Furthermore, when the user is relaxed, the determination unit can also determine the response of providing a pleasant topic. This allows for a more appropriate response by adjusting the manner of expression of the response. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into an AI, which can then determine an appropriate expression method.

[0087] The determination unit can adjust the level of detail of the response based on the importance of the emotion when determining the response. For example, when the user shows a strong emotion, the determination unit determines a detailed response. In addition, when the user shows a medium emotion, the determination unit can also determine a response with a moderate level of detail. Furthermore, when the user shows a weak emotion, the determination unit can also determine a brief response. This allows a more appropriate response by adjusting the level of detail of the response based on the importance of the emotion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the AI, and the AI ​​can determine the response with an appropriate level of detail.

[0088] The determination unit can apply different response algorithms depending on the emotion category when determining the response. For example, if the user is sad, the determination unit applies a comforting algorithm. In addition, if the user is excited, the determination unit can also apply a calming algorithm. Furthermore, if the user is relaxed, the determination unit can apply an algorithm that provides a pleasant topic. This allows for a more appropriate response by applying a response algorithm according to the emotion category. Some or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data into AI, which can then apply an appropriate algorithm.

[0089] The determination unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. For example, when the user is sad, the determination unit determines a response that provides longer comforting words. In addition, when the user is excited, the determination unit can also determine a response that provides shorter soothing words. Furthermore, when the user is relaxed, the determination unit can also determine a response that provides a pleasant topic of appropriate length. This allows for a more appropriate response by adjusting the length of the response. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into an AI, and the AI ​​can determine a response with an appropriate length.

[0090] The determination unit can determine the priority of the response based on the time when the emotion occurred when determining the response. The determination unit, for example, preferentially determines the response based on the latest emotion. The determination unit can also determine the next response based on the past emotion. Furthermore, the determination unit can also preferentially determine the response based on the emotion that occurred in a specific time period. This enables a more effective response by determining the priority of the response based on the time when the emotion occurred. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data to AI, and the AI ​​can determine the response with appropriate priority.

[0091] The determination unit can adjust the order of responses based on the relevance of emotions when determining responses. For example, the determination unit preferentially determines responses based on emotions with high relevance. The determination unit can also determine responses next based on emotions with medium relevance. Furthermore, the determination unit can also determine responses last based on emotions with low relevance. This allows for more appropriate responses by adjusting the order of responses based on the relevance of emotions. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's emotion data into AI, which can determine responses in an appropriate order.

[0092] The response unit can estimate the user's emotion and adjust the execution method of the response based on the estimated user's emotion. For example, if the user is sad, the response unit can comfort the user in a gentle voice. Also, if the user is excited, the response unit can play gentle music to calm the user. Furthermore, if the user is relaxed, the response unit can provide a pleasant topic. This allows for a more appropriate response by adjusting the execution method of the response. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the user's emotion data into an AI, which can then determine an appropriate execution method.

[0093] The response unit can select the optimal response method by referring to the user's past emotion history when performing the response. For example, the response unit re-executes a response method that was effective when the user felt sad in the past. The response unit can also re-execute a response method that was effective when the user was excited in the past. Furthermore, the response unit can also re-execute a response method that was effective when the user was relaxed in the past. This allows for a more effective response by referring to the past emotion history. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit inputs the user's past emotion data into an AI, which can select an appropriate response method.

[0094] The response unit can customize the response means based on the user's current living situation when performing the response. For example, when the user is at home, the response unit selects a response method that can be performed at home. In addition, when the user is out, the response unit can also select a response method that can be performed away from home. Furthermore, when the user is at work, the response unit can also select a response method that is suitable for the work environment. This allows for a more appropriate response by customizing the response means based on the current living situation. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the user's living situation data into AI, which can select an appropriate response means.

[0095] 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 sad, the response unit performs a comforting response with the highest priority. In addition, when the user is excited, the response unit can also perform a calming response with the highest priority. Furthermore, when the user is relaxed, the response unit can also perform a response that provides a fun topic with the highest priority. This allows for a more appropriate response by determining the priority of the response. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit inputs the user's emotion data into an AI, and the AI ​​can determine the response with an appropriate priority.

[0096] The response unit can select the optimal response method in consideration of the geographical location information of the user when performing the response. For example, when the user is in a park, the response unit plays natural sounds to relax the user. In addition, when the user is at home, the response unit can also select a response method that can be performed at home. Furthermore, when the user is at work, the response unit can also select a response method that is suitable for the work environment. This makes it possible to respond more appropriately by taking the geographical location information into consideration. Part or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the user's geographical location data into an AI, which can select an appropriate response method.

[0097] The response unit can analyze the user's social media activity and suggest a response measure when the response is executed. For example, when the user posts a positive message on social media, the response unit can suggest a response method related to the content of the post. In addition, when the user posts a negative message on social media, the response unit can also suggest a response method related to the content of the post. Furthermore, when the user participates in a specific event on social media, the response unit can also suggest a response method related to the event. In this way, by analyzing social media activity, more appropriate response measures can be suggested. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input the user's social media data into AI, which can then suggest appropriate response measures.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The emotion estimation system may further include a feedback unit. The feedback unit may collect the user's reactions to the received treatment and improve the system's response based on the reaction. For example, if the user feels relaxed in response to the provided music, the feedback unit may record the information and provide the same music in a similar situation in the future. In addition, if the user reacts positively to the agent's words, the words may be utilized in future responses. Furthermore, if the user reacts negatively to a particular treatment, the system may be adjusted to avoid that treatment. This allows the system's response to be continuously improved based on the user's reaction, thereby realizing more effective communication.

[0100] The emotion estimation system may further include a learning unit. The learning unit accumulates the user's emotion estimation information and the corresponding results, and can improve the accuracy of the system using a machine learning algorithm. For example, the learning unit can learn the user's facial expression data and the emotion at that time to perform more accurate emotion estimation. The learning unit can also learn the user's voice data and the emotion at that time to improve the accuracy of voice analysis. The learning unit can also learn the user's biometric information and the emotion at that time to improve the accuracy of emotion estimation from the biometric information. This allows the system to more accurately estimate the user's emotion and take appropriate measures.

[0101] The emotion estimation system may further include a prediction unit. The prediction unit may predict future emotions based on the user's past emotion data and the current situation. For example, if the user is prone to feeling stress in a particular situation, the prediction unit may predict stress when that situation approaches and provide a response that allows the user to relax in advance. If the user is relaxed during a particular time period, a response that allows the user to relax during that time period may be provided. Furthermore, if the user is predicted to be excited at a particular event, a response that allows the user to calm down before the event may be provided. This allows the prediction unit to predict the user's emotions in advance and provide an appropriate response.

[0102] The emotion estimation system may further include a customization unit. The customization unit may adjust the settings of the system based on the individual preferences and needs of the user. For example, if the user prefers a particular piece of music, the music may be preferentially provided. Also, if the user prefers a particular wording, the system may be adjusted to use the wording. Furthermore, if the user prefers a particular response during a particular time period, the response may be provided during that time period. In this way, the customization unit may provide a response according to the individual preferences and needs of the user, thereby realizing more personalized communication.

[0103] The emotion estimation system may further include a notification unit. The notification unit may send a notification to a family member or a caregiver depending on the user's emotional state. For example, if the user is feeling strong stress, a notification may be sent to the family member to request support. If the user is relaxed, a notification may be sent to the family member to reassure them. If the user is excited, a notification may be sent to the caregiver to prompt them to take an appropriate action. In this way, the notification unit may convey the user's emotional state to the family member or caregiver, and provide more effective support.

[0104] The emotion estimation system may further include a recording unit. The recording unit may record the user's emotion estimation information and the response results over a long period of time and analyze them later. For example, the recording unit may record the user's emotion changes over a long period of time and analyze the emotion patterns. The recording unit may also record the user's response results and analyze which responses were effective. Furthermore, the user's emotion estimation information may be recorded and used for future research and improvement. In this way, the recording unit may accumulate data on the user's emotions and responses, and may be useful for improving and researching the system.

[0105] The emotion estimation system may further include an alert unit. The alert unit may send an alert to a system administrator when the user's emotional state changes suddenly. For example, when the user suddenly feels strong stress, the alert unit may send an alert to the system administrator to prompt a quick response. In addition, when the user suddenly becomes excited, the alert unit may send an alert to the system administrator to instruct the system administrator to take an appropriate response. In addition, when the user suddenly becomes relaxed, the alert unit may send an alert to the system administrator to check the situation. In this way, the alert unit may quickly respond to a sudden change in the user's emotion and provide appropriate support.

[0106] The emotion estimation system may further include an environment adjustment unit. The environment adjustment unit may adjust the surrounding environment according to the user's emotional state. For example, if the user is stressed, the lighting may be turned down to create a relaxing environment. If the user is excited, music may be played to calm the user. If the user is relaxed, a scent may be provided to create a more relaxing environment. In this way, the environment adjustment unit may adjust the surrounding environment according to the user's emotional state to provide a more comfortable environment.

[0107] The emotion estimation system may further include a schedule management unit. The schedule management unit may adjust the schedule based on the emotional state of the user. For example, if the user is feeling stressed, time for relaxation may be added to the schedule. Also, if the user is excited, a calming activity may be added to the schedule. Furthermore, if the user is relaxed, an activity for maintaining that state may be added to the schedule. In this way, the schedule management unit may adjust the schedule according to the emotional state of the user and provide more appropriate activities.

[0108] The emotion estimation system may further include a communication support unit. The communication support unit may provide support when the user communicates with other people. For example, if the user is unable to express emotions well, the communication support unit may suggest appropriate words. Also, if the user has difficulty understanding the emotions of other people, the communication support unit may explain the emotions. Furthermore, if the user feels anxious about communication, the communication support unit may suggest ways to relax the user. In this way, the communication support unit may support the user's communication and realize smoother dialogue.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a user with an intellectual disability. The emotion estimation information includes, for example, facial expression data, voice data, and biometric information. The acquisition unit captures the user's facial expression with a camera and acquires it as emotion estimation information using facial expression recognition technology. The acquisition unit can also record the user's voice and acquire it as emotion estimation information using voice analysis technology. Furthermore, the user's heart rate and blood pressure can be measured with a sensor and included in the emotion estimation information as biometric information. Step 2: The estimation unit estimates the user's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit analyzes the emotion estimation information using an AI and estimates the user's emotion. The AI ​​analyzes the emotion estimation information using a machine learning algorithm and estimates the user's emotion. Step 3: The determination unit determines a response according to the user's emotion estimated by the estimation unit. The determination unit determines a response according to the user's emotion using an AI. The AI ​​determines a response according to the user's emotion using a rule-based algorithm. Step 4: The response unit executes the response determined by the determination unit, such as offering words of comfort to the user, playing music, displaying a video, or the like.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] For example, the acquisition unit acquires the user's facial expression and voice using the camera 42 and microphone 38B of the smart device 14, and the acquired facial expression and voice are analyzed 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 acquired emotion estimation information. 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 estimated emotion. The response unit is realized, for example, by the control unit 46A of the smart device 14, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] For example, the acquisition unit acquires the user's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214, and the acquired facial expression and voice are analyzed 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 acquired emotion estimation information. 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 estimated emotion. The response unit is realized, for example, by the control unit 46A of the smart glasses 214, and executes the determined response. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] For example, the acquisition unit acquires the user's facial expression and voice using the camera 42 and microphone 238 of the headset type terminal 314, and the acquired facial expression and voice are analyzed 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 acquired emotion estimation information. 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 estimated emotion. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] For example, the acquisition unit acquires the user's facial expression and voice using the camera 42 and microphone 238 of the robot 414, and the acquired facial expression and voice are analyzed 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 acquired emotion estimation information. 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 estimated emotion. The response unit is realized, for example, by the control unit 46A of the robot 414, and executes the determined response. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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 has positive feelings such as "I want more" or "I want to know more."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a user with an intellectual disability; an estimation unit that estimates a feeling of the user based on the feeling estimation information acquired by the acquisition unit; 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 response determined by the determination unit. A system characterized by: (Appendix 2) The determination unit is determining a dialogue corresponding to the user's emotion as a response corresponding 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 emotion of the user as a response corresponding to the emotion of the user; 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 determination unit is Providing different music or video depending on the user's emotion is determined as a response to the user's emotion. 2. The system of claim 1, (Appendix 5) The determination unit is Based on the emotion of the user estimated by the estimation unit and the attribute of the user, Deciding a response according to the user's emotion 2. The system of claim 1, (Appendix 6) 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 the emotion estimation information. 2. The system of claim 1, (Appendix 7) The acquisition unit is Estimate the user's emotions, Adjusting the timing of acquiring emotion estimation information based on the estimated emotion of the user 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze the user's past emotion history, Select the best acquisition method 2. The system of claim 1, (Appendix 9) The acquisition unit is When acquiring emotion estimation information, Filtering based on the user's current activity and environment 2. The system of claim 1, (Appendix 10) The acquisition unit is Estimate the user's emotions, Determine the priority of emotion estimation information to be acquired based on the estimated user emotion 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring emotion estimation information, Prioritize the retrieval of relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring emotion estimation information, Analyze users' social media activities and obtain relevant information 2. The system of claim 1, (Appendix 13) The estimation unit is Estimate the user's emotions, Adjusting emotion estimation algorithms based on estimated user emotions 2. The system of claim 1, (Appendix 14) The estimation unit is When estimating, Adjusting the level of detail of emotion estimation based on the importance of information for emotion estimation 2. The system of claim 1, (Appendix 15) The estimation unit is When estimating, Apply different estimation algorithms depending on the category of emotion estimation information 2. The system of claim 1, (Appendix 16) The estimation unit is Estimate the user's emotions, Adjust the accuracy of the estimation based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The estimation unit is When estimating, Determine the priority of estimation based on the time when emotion estimation information was acquired 2. The system of claim 1, (Appendix 18) The estimation unit is When estimating, Adjusting the order of estimation based on the relevance of emotion estimation information 2. The system of claim 1, (Appendix 19) The determination unit is Estimate the user's emotions, Adjust the way you express your responses based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The determination unit is When deciding on a response, Adjust response detail based on emotional importance 2. The system of claim 1, (Appendix 21) The determination unit is When deciding on a response, Apply different response algorithms depending on emotion categories 2. The system of claim 1, (Appendix 22) The determination unit is Estimate the user's emotions, Adjust the length of responses based on estimated user sentiment 2. The system of claim 1, (Appendix 23) The determination unit is When deciding on a response, Prioritize responses based on when emotions occur 2. The system of claim 1, (Appendix 24) The determination unit is When deciding on a response, Adjusting response order based on emotional relevance 2. The system of claim 1, (Appendix 25) The corresponding portion is Estimate the user's emotions, Adjust how responses are executed based on estimated user sentiment 2. The system of claim 1, (Appendix 26) The corresponding portion is When the response is executed, Select the optimal response method by referring to the user's past emotion history 2. The system of claim 1, (Appendix 27) The corresponding portion is When the response is executed, Customize responses based on the user's current life situation 2. The system of claim 1, (Appendix 28) The corresponding portion is Estimate the user's emotions, Decide the priority of responses based on estimated user sentiment 2. The system of claim 1, (Appendix 29) The corresponding portion is When the response is executed, Consider the user's geographic location to determine the best course of action 2. The system of claim 1, (Appendix 30) The corresponding portion is When the response is executed, Analyze users' social media activity and suggest action options 2. The system of claim 1, [Explanation of symbols]

[0183] 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 with an intellectual disability; an estimation unit that estimates a feeling of the user based on the feeling estimation information acquired by the acquisition unit; 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 response determined by the determination unit. A system characterized by:

2. The determination unit is determining a dialogue corresponding to the user's emotion as a response corresponding 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 .

3. The determination unit is determining a dialogue in a manner corresponding to the emotion of the user as a response corresponding to the emotion of the user; 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 determination unit is Providing different music or video depending on the user's emotion is determined as a response to the user's emotion.

2. The system of claim 1 .

5. The determination unit is Based on the emotion of the user estimated by the estimation unit and the attribute of the user, Deciding a response according to the user's emotion 2. The system of claim 1 .

6. 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 the emotion estimation information.

2. The system of claim 1 .

7. The acquisition unit is When acquiring the emotion estimation information, filtering is performed based on the current activity status and environment of the user.

2. The system of claim 1 .

Images