system

Abstract

The system according to this embodiment aims to accurately understand the user's emotions and provide empathetic responses and appropriate advice. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a response unit, and a collaboration unit. The data collection unit collects multimodal data such as the user's voice, facial expressions, and posture. The analysis unit analyzes the data collected by the data collection unit to understand the user's true emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. The collaboration unit collaborates with an electronic payment application.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately understand the user's emotions and provide a sympathetic response.

[0005] The system according to the embodiment aims to accurately understand the user's emotions and provide a sympathetic response and appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a response unit, and a collaboration unit. The data collection unit collects multimodal data such as the user's voice, facial expressions, and posture. The analysis unit analyzes the data collected by the data collection unit to understand the user's true emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. The collaboration unit collaborates with an electronic payment application. [Effects of the Invention]

[0007] The system according to this embodiment can accurately understand the user's emotions and provide empathetic responses and appropriate advice. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​counselor system according to an embodiment of the present invention is a system that analyzes multimodal data such as voice, facial expressions, and posture to achieve a deeper understanding of emotions and empathy. This AI counselor system collects multimodal data such as the user's voice, facial expressions, and posture, and the AI ​​analyzes it to understand the user's true emotions. Furthermore, the AI ​​provides empathetic responses and appropriate advice tailored to the user's personality and values. This mechanism enables early detection of stress and bridging to specialists as needed. It also promotes daily use by linking with electronic payment apps. As a result, users can receive a seamless service that is available 24 hours a day, 365 days a year, anywhere. For example, the AI ​​counselor system collects multimodal data such as the user's voice, facial expressions, and posture. In this process, detailed data such as what the user says, facial expressions, and posture is collected. For example, the tone of what the user says, changes in facial expressions, and changes in posture are collected. This allows the AI ​​to grasp the user's emotional state. Next, the AI ​​analyzes the collected data. The AI ​​comprehensively analyzes the collected data such as voice, facial expressions, and posture to understand the user's true emotions. For example, the AI ​​can analyze the tone of voice, facial expressions, and posture of the user to identify the stress and anxiety they are experiencing. Furthermore, the AI ​​provides empathetic responses and appropriate advice tailored to the user's personality and values. For instance, it can offer empathetic words and appropriate advice regarding the user's stress and anxiety, thereby providing a sense of security. This mechanism enables early detection of stress and facilitates referral to professionals when necessary. For example, if the user's stress or anxiety is severe, the AI ​​can recommend consultation with a professional, allowing the user to receive appropriate care early on. The system also integrates with electronic payment apps to promote everyday use. For example, users can consult with an AI counselor through the app and pay for counseling services via the app. This makes counseling services easily accessible to users.In this way, an AI counselor that analyzes multimodal data such as voice, facial expressions, and posture to achieve a deeper understanding of emotions and empathy can support users' mental health and create a society where everyone can live authentically. This allows the AI ​​counselor system to deeply understand users' emotions and support their mental health by providing empathetic responses and appropriate advice.

[0029] The AI ​​counselor system according to the embodiment comprises a collection unit, an analysis unit, a response unit, and a coordination unit. The collection unit collects multimodal data such as the user's voice, facial expressions, and posture. The collection unit collects detailed data such as the tone of what the user is saying, changes in facial expressions, and changes in posture. The collection unit collects voice data with a microphone, facial expression data with a camera, and posture data with a sensor. The collection unit analyzes the tone of what the user is saying and detects changes in emotion. The collection unit analyzes changes in the user's facial expressions and detects changes in emotion. The collection unit analyzes changes in the user's posture and detects changes in emotion. The analysis unit analyzes the data collected by the collection unit and understands the user's true emotions. The analysis unit comprehensively analyzes the collected data such as voice, facial expressions, and posture. The analysis unit analyzes the tone of the voice data, changes in facial expressions, and changes in posture to identify the stress and anxiety the user is feeling. The analysis unit analyzes the tone of the voice data and understands the user's emotions. The analysis unit, for example, analyzes changes in facial expression data to understand the user's emotions. The analysis unit, for example, analyzes changes in posture data to understand the user's emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. The response unit, for example, provides empathetic responses tailored to the user's personality and values. The response unit, for example, offers empathetic words to the user regarding the stress and anxiety they are feeling. The response unit, for example, provides appropriate advice to the user regarding the stress and anxiety they are feeling. The response unit, for example, provides empathetic responses based on the user's personality and values. The response unit, for example, provides appropriate advice based on the user's personality and values. The integration unit integrates with an electronic payment app. The integration unit, for example, enables the user to consult with an AI counselor. The integration unit, for example, enables the user to pay for counseling services through an electronic payment app. The integration unit, for example, facilitates message exchanges between the user and the AI ​​counselor. The integration unit, for example, facilitates payment of counseling services through an electronic payment app.As a result, the AI ​​counselor system according to this embodiment can deeply understand the user's emotions and support the user's mental health by providing empathetic responses and appropriate advice.

[0030] The data collection unit collects multimodal data such as the user's voice, facial expressions, and posture. Specifically, the unit collects detailed data such as the tone of the user's speech, changes in facial expressions, and changes in posture. Voice data is collected using a high-sensitivity microphone, and features such as the tone, pitch, speed, and volume of the user's voice are analyzed. This allows for the detection of changes in the user's emotions and stress levels. Facial expression data is collected using a high-resolution camera, capturing subtle facial movements and changes in expression. For example, changes in facial expressions such as smiles, frown lines, and eye movements are analyzed to understand the user's emotional state. Posture data is collected using sensors to detect changes in the user's body movements and posture. These include accelerometers and gyroscopes, and changes in emotion are detected by analyzing the user's body tilt and movement patterns. This data is collected in real time and transmitted to a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to understand the user's true emotions. Specifically, it comprehensively analyzes collected data such as voice, facial expressions, and posture. For voice data analysis, speech recognition and natural language processing technologies are used to analyze the content and tone of the user's speech and identify changes in emotion. For example, when the user's voice tone becomes higher or their speaking speed increases, they are likely to be feeling stress or anxiety. For facial expression data analysis, image recognition technology is used to analyze subtle facial movements and changes in expression to understand the user's emotions. For example, changes in facial expressions such as frown lines and drooping corners of the mouth are analyzed to identify the emotions the user is feeling. For posture data analysis, machine learning algorithms are used to analyze the user's body movements and changes in posture to detect changes in emotion. For example, when a user leans forward or sways their body, they are likely to be feeling anxiety or tension. The analysis unit comprehensively analyzes this data and can grasp the user's emotional state in real time. Furthermore, the analysis unit can also utilize past data and statistical information to analyze long-term emotional fluctuations and trends. This allows for a deeper understanding of the user's emotional state and enables appropriate responses.

[0032] The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. Specifically, it provides empathetic responses tailored to the user's personality and values. For example, it offers empathetic words to address the stress and anxiety the user is experiencing. The response unit uses natural language generation technology to generate words that resonate with the user's emotions, providing a sense of security. It also provides appropriate advice based on the user's personality and values. For example, if the user is feeling stressed, it provides specific methods for relaxation and advice on how to reduce stress. The response unit can continuously improve its responses based on the user's past consultations and feedback. This allows it to provide more appropriate and effective responses to the user. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of its responses. For example, it collects feedback on how the user felt about the advice provided and uses this to improve the responses. This allows the response unit to provide more appropriate and effective responses to the user and support their mental health.

[0033] The integration unit will connect with electronic payment apps. Specifically, it will enable users to consult with AI counselors. The integration unit will use APIs to facilitate message exchange between users and AI counselors. This will allow users to easily consult with AI counselors. Furthermore, the integration unit will enable users to pay for counseling services through electronic payment apps. For example, after using a counseling service, users can pay the fee through the electronic payment app. This will make it easy for users to use counseling services. In addition, the integration unit can connect with other apps and services. For example, if a user is using a health management app, that data can be imported into the AI ​​counselor system to provide more detailed counseling. This will enable the integration unit to provide more comprehensive support to users.

[0034] The data collection unit can collect detailed data such as the tone of voice, changes in facial expressions, and changes in posture of the user. For example, the data collection unit can collect the tone of voice of the user. For example, the data collection unit can collect changes in the user's facial expressions. For example, the data collection unit can collect changes in the user's posture. By collecting detailed data on the user, a more accurate understanding of emotions becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's voice data into a generating AI and have the generating AI perform analysis of the voice data.

[0035] The analysis unit comprehensively analyzes collected data such as voice, facial expressions, and posture to understand the user's true emotions. For example, the analysis unit comprehensively analyzes the collected voice data. For example, the analysis unit comprehensively analyzes the collected facial expression data. For example, the analysis unit comprehensively analyzes the collected posture data. This allows for an accurate understanding of the user's true emotions through comprehensive data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected voice data into a generating AI and have the generating AI perform the analysis of the voice data.

[0036] The response unit can provide empathetic responses and appropriate advice tailored to the user's personality and values. For example, the response unit provides empathetic responses based on the user's personality and values. For example, the response unit provides appropriate advice based on the user's personality and values. This enhances the user's sense of security through responses and advice tailored to the user's personality and values. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input a response based on the user's personality and values ​​into a generating AI and have the generating AI perform the response generation.

[0037] The integration unit can integrate with electronic payment apps to facilitate daily use. For example, the integration unit can enable users to consult with an AI counselor. For example, the integration unit can enable users to pay for counseling services through an electronic payment app. This allows users to easily access counseling services through integration with electronic payment apps. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can have a message generation AI execute the exchange of messages between the user and the AI ​​counselor.

[0038] The response unit can perform early detection of stress and, if necessary, refer the user to a specialist. For example, if the user's stress or anxiety is severe, the response unit will recommend consulting a specialist. For example, if the user's stress or anxiety is mild, the response unit will provide appropriate advice. This allows the user to receive appropriate care early through early detection of stress and referral to a specialist. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's stress level into a generating AI and have the generating AI perform early detection of stress.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, if the user has provided a lot of voice data in the past, the data collection unit will prioritize collecting voice data. For example, if the user has provided a lot of facial expression data in the past, the data collection unit will focus on collecting changes in facial expressions. For example, if the user has provided a lot of posture data in the past, the data collection unit will collect changes in posture in detail. By analyzing the past data collection history, the optimal collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current environment and circumstances during data collection. For example, if the user is in a quiet environment, the data collection unit will prioritize collecting audio data. If the user is moving, the data collection unit will focus on collecting posture and gesture data. If the user is in a dark place, the data collection unit will refrain from collecting facial expression data and focus on audio data. This allows for more appropriate data collection by filtering data based on the user's current environment and circumstances. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current environment data into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at home, the data collection unit will prioritize the collection of data related to relaxation. If the user is at work, the data collection unit will prioritize the collection of data related to stress and tension. If the user is in a public place, the data collection unit will consider surrounding sounds and environmental noises when collecting data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media expressing stress, the data collection unit can collect data related to that emotion. For example, if a user posts on social media expressing relaxation, the data collection unit can collect data related to that emotion. For example, if a user posts on social media expressing anxiety, the data collection unit can collect data related to that emotion. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a speech analysis algorithm to speech data. For example, the analysis unit applies a facial expression analysis algorithm to facial expression data. For example, the analysis unit applies a posture analysis algorithm to posture data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can adjust the order of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may perform the analysis while referring to past data. For example, the analysis unit may focus on analyzing data collected during a specific period. This allows for prioritizing the analysis of the most recent data by adjusting the order of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The analysis unit can adjust its analysis method based on the relevance of the data during analysis. For example, the analysis unit applies a detailed analysis method to highly relevant data. For example, it applies a simplified analysis method to less relevant data. For example, it applies an analysis method with an appropriate level of detail to data of moderate relevance. By adjusting the analysis method based on the relevance of the data, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis method.

[0047] The response unit can customize the content of its response based on the user's personality and values. For example, if the user is introverted, the response unit will provide a quiet and reserved response. If the user is extroverted, the response unit will provide an active and cheerful response. If the response unit is based on the user's values, the response unit will provide an empathetic response. By customizing the content of the response based on the user's personality and values, a more appropriate response becomes possible. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user personality and value data into a generating AI and have the generating AI perform the customization of the response content.

[0048] The response unit can provide the optimal response by referring to the user's past response history when responding. For example, the response unit may respond by referring to response styles that the user has preferred in the past. For example, the response unit may avoid response styles that the user has found unpleasant in the past. For example, the response unit may select the optimal response content from the user's past response history. This makes it possible to provide a more appropriate response by referring to the user's past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit may input the user's past response history data into a generating AI and have the generating AI perform the task of providing the optimal response.

[0049] The response unit can adjust the content of its response based on the user's current situation. For example, if the user is working, the response unit will provide a short, to-the-point response. If the user is relaxed, the response unit will provide a response that includes detailed explanations. If the user is in a hurry, the response unit will provide a quick and concise response. By adjusting the content of the response according to the user's current situation, a more appropriate response becomes possible. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's current situation data into a generating AI and have the generating AI adjust the content of the response.

[0050] The response unit can analyze the user's social media activity and provide a relevant response when responding. For example, if the user posts on social media expressing stress, the response unit will provide a response related to that emotion. For example, if the user posts on social media expressing relaxation, the response unit will provide a response related to that emotion. For example, if the user posts on social media expressing anxiety, the response unit will provide a response related to that emotion. In this way, by analyzing the user's social media activity, a relevant response can be provided. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing a relevant response.

[0051] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit will prioritize integration with apps that the user has frequently used in the past. For example, the integration unit will integrate by referring to the integration method that the user has preferred in the past. For example, the integration unit will select the optimal integration method from the user's past integration history. In this way, the optimal integration method can be selected by referring to the user's past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the user's past integration history data into a generating AI and have the generating AI perform the selection of the optimal integration method.

[0052] The integration unit can customize the means of integration based on the user's current usage status during integration. For example, the integration unit seamlessly integrates with the application the user is currently using. For example, the integration unit provides the optimal means of integration according to the user's current situation. For example, the integration unit customizes the means of integration considering the user's current usage status. This makes it possible to perform more appropriate integration by customizing the means of integration based on the user's current usage status. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's current usage status data into a generating AI and have the generating AI perform the customization of the means of integration.

[0053] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is at home, the integration unit will prioritize integration with apps available at home. If the user is at work, the integration unit will prioritize integration with apps available at work. If the user is out, the integration unit will prioritize integration with apps available at the user's location. This allows the integration unit to select the optimal integration method by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal integration method.

[0054] The integration unit can analyze the user's social media activity during integration and provide relevant integration methods. For example, if the user posts on social media expressing stress, the integration unit can provide integration with apps related to that emotion. For example, if the user posts on social media expressing relaxation, the integration unit can provide integration with apps related to that emotion. For example, if the user posts on social media expressing anxiety, the integration unit can provide integration with apps related to that emotion. In this way, by analyzing the user's social media activity, relevant integration methods can be provided. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant integration methods.

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

[0056] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, if the user has provided a lot of voice data in the past, the data collection unit will prioritize collecting voice data. For example, if the user has provided a lot of facial expression data in the past, the data collection unit will focus on collecting changes in facial expressions. For example, if the user has provided a lot of posture data in the past, the data collection unit will collect changes in posture in detail. By analyzing the past data collection history, the optimal collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0058] The response unit can customize the content of its response based on the user's personality and values. For example, if the user is introverted, the response unit will provide a quiet and reserved response. If the user is extroverted, the response unit will provide an active and cheerful response. If the response unit is based on the user's values, the response unit will provide an empathetic response. By customizing the content of the response based on the user's personality and values, a more appropriate response becomes possible. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user personality and value data into a generating AI and have the generating AI perform the customization of the response content.

[0059] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit will prioritize integration with apps that the user has frequently used in the past. For example, the integration unit will integrate by referring to the integration method that the user has preferred in the past. For example, the integration unit will select the optimal integration method from the user's past integration history. In this way, the optimal integration method can be selected by referring to the user's past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the user's past integration history data into a generating AI and have the generating AI perform the selection of the optimal integration method.

[0060] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is at home, the integration unit will prioritize integration with apps available at home. If the user is at work, the integration unit will prioritize integration with apps available at work. If the user is out, the integration unit will prioritize integration with apps available at the user's location. This allows the integration unit to select the optimal integration method by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal integration method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects multimodal data such as the user's voice, facial expressions, and posture. For example, voice data is collected by a microphone, facial expression data by a camera, and posture data by a sensor. The data collection unit collects detailed data such as the tone of what the user is saying, changes in facial expressions, and changes in posture, and detects changes in emotion. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's true emotions. For example, it comprehensively analyzes the collected data such as voice, facial expressions, and posture, analyzing the tone of the voice data, changes in facial expressions, and changes in posture to identify the stress and anxiety the user is feeling. Step 3: The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. For example, it provides empathetic responses tailored to the user's personality and values, offers empathetic words regarding the stress and anxiety the user is feeling, and provides appropriate advice. Step 4: The integration unit will connect with the electronic payment app. For example, it will enable users to consult with an AI counselor and pay for the counseling service through the electronic payment app.

[0063] (Example of form 2)The AI ​​counselor system according to an embodiment of the present invention is a system that analyzes multimodal data such as voice, facial expressions, and posture to achieve a deeper understanding of emotions and empathy. This AI counselor system collects multimodal data such as the user's voice, facial expressions, and posture, and the AI ​​analyzes it to understand the user's true emotions. Furthermore, the AI ​​provides empathetic responses and appropriate advice tailored to the user's personality and values. This mechanism enables early detection of stress and bridging to specialists as needed. It also promotes daily use by linking with electronic payment apps. As a result, users can receive a seamless service that is available 24 hours a day, 365 days a year, anywhere. For example, the AI ​​counselor system collects multimodal data such as the user's voice, facial expressions, and posture. In this process, detailed data such as what the user says, facial expressions, and posture is collected. For example, the tone of what the user says, changes in facial expressions, and changes in posture are collected. This allows the AI ​​to grasp the user's emotional state. Next, the AI ​​analyzes the collected data. The AI ​​comprehensively analyzes the collected data such as voice, facial expressions, and posture to understand the user's true emotions. For example, the AI ​​can analyze the tone of voice, facial expressions, and posture of the user to identify the stress and anxiety they are experiencing. Furthermore, the AI ​​provides empathetic responses and appropriate advice tailored to the user's personality and values. For instance, it can offer empathetic words and appropriate advice regarding the user's stress and anxiety, thereby providing a sense of security. This mechanism enables early detection of stress and facilitates referral to professionals when necessary. For example, if the user's stress or anxiety is severe, the AI ​​can recommend consultation with a professional, allowing the user to receive appropriate care early on. The system also integrates with electronic payment apps to promote everyday use. For example, users can consult with an AI counselor and pay for counseling services through the electronic payment app, making it easy for users to access counseling services.In this way, an AI counselor that analyzes multimodal data such as voice, facial expressions, and posture to achieve a deeper understanding of emotions and empathy can support users' mental health and create a society where everyone can live authentically. This allows the AI ​​counselor system to deeply understand users' emotions and support their mental health by providing empathetic responses and appropriate advice.

[0064] The AI ​​counselor system according to the embodiment comprises a collection unit, an analysis unit, a response unit, and a coordination unit. The collection unit collects multimodal data such as the user's voice, facial expressions, and posture. The collection unit collects detailed data such as the tone of what the user is saying, changes in facial expressions, and changes in posture. The collection unit collects voice data with a microphone, facial expression data with a camera, and posture data with a sensor. The collection unit analyzes the tone of what the user is saying and detects changes in emotion. The collection unit analyzes changes in the user's facial expressions and detects changes in emotion. The collection unit analyzes changes in the user's posture and detects changes in emotion. The analysis unit analyzes the data collected by the collection unit and understands the user's true emotions. The analysis unit comprehensively analyzes the collected data such as voice, facial expressions, and posture. The analysis unit analyzes the tone of the voice data, changes in facial expressions, and changes in posture to identify the stress and anxiety the user is feeling. The analysis unit analyzes the tone of the voice data and understands the user's emotions. The analysis unit, for example, analyzes changes in facial expression data to understand the user's emotions. The analysis unit, for example, analyzes changes in posture data to understand the user's emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. The response unit, for example, provides empathetic responses tailored to the user's personality and values. The response unit, for example, offers empathetic words to the user regarding the stress and anxiety they are feeling. The response unit, for example, provides appropriate advice to the user regarding the stress and anxiety they are feeling. The response unit, for example, provides empathetic responses based on the user's personality and values. The response unit, for example, provides appropriate advice based on the user's personality and values. The integration unit integrates with an electronic payment app. The integration unit, for example, enables the user to consult with an AI counselor. The integration unit, for example, enables the user to pay for counseling services through an electronic payment app. The integration unit, for example, facilitates message exchange between the user and the AI ​​counselor. The integration unit, for example, handles payment for counseling services through an electronic payment app.As a result, the AI ​​counselor system according to this embodiment can deeply understand the user's emotions and support the user's mental health by providing empathetic responses and appropriate advice.

[0065] The data collection unit collects multimodal data such as the user's voice, facial expressions, and posture. Specifically, the unit collects detailed data such as the tone of the user's speech, changes in facial expressions, and changes in posture. Voice data is collected using a high-sensitivity microphone, and features such as the tone, pitch, speed, and volume of the user's voice are analyzed. This allows for the detection of changes in the user's emotions and stress levels. Facial expression data is collected using a high-resolution camera, capturing subtle facial movements and changes in expression. For example, changes in facial expressions such as smiles, frown lines, and eye movements are analyzed to understand the user's emotional state. Posture data is collected using sensors to detect changes in the user's body movements and posture. These include accelerometers and gyroscopes, and changes in emotion are detected by analyzing the user's body tilt and movement patterns. This data is collected in real time and transmitted to a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The analysis unit analyzes data collected by the data collection unit to understand the user's true emotions. Specifically, it comprehensively analyzes collected data such as voice, facial expressions, and posture. For voice data analysis, speech recognition and natural language processing technologies are used to analyze the content and tone of the user's speech and identify changes in emotion. For example, when the user's voice tone becomes higher or their speaking speed increases, they are likely to be feeling stress or anxiety. For facial expression data analysis, image recognition technology is used to analyze subtle facial movements and changes in expression to understand the user's emotions. For example, changes in facial expressions such as frown lines and drooping corners of the mouth are analyzed to identify the emotions the user is feeling. For posture data analysis, machine learning algorithms are used to analyze the user's body movements and changes in posture to detect changes in emotion. For example, when a user leans forward or sways their body, they are likely to be feeling anxiety or tension. The analysis unit comprehensively analyzes this data and can grasp the user's emotional state in real time. Furthermore, the analysis unit can also utilize past data and statistical information to analyze long-term emotional fluctuations and trends. This allows for a deeper understanding of the user's emotional state and enables appropriate responses.

[0067] The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. Specifically, it provides empathetic responses tailored to the user's personality and values. For example, it offers empathetic words to address the stress and anxiety the user is experiencing. The response unit uses natural language generation technology to generate words that resonate with the user's emotions, providing a sense of security. It also provides appropriate advice based on the user's personality and values. For example, if the user is feeling stressed, it provides specific methods for relaxation and advice on how to reduce stress. The response unit can continuously improve its responses based on the user's past consultations and feedback. This allows it to provide more appropriate and effective responses to the user. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and effectiveness of its responses. For example, it collects feedback on how the user felt about the advice provided and uses this to improve the responses. This allows the response unit to provide more appropriate and effective responses to the user and support their mental health.

[0068] The integration unit will connect with electronic payment apps. Specifically, it will enable users to consult with AI counselors. The integration unit will use APIs to facilitate message exchange between users and AI counselors. This will allow users to easily consult with AI counselors. Furthermore, the integration unit will enable users to pay for counseling services through electronic payment apps. For example, after using a counseling service, users can pay the fee through the electronic payment app. This will make it easy for users to use counseling services. In addition, the integration unit can connect with other apps and services. For example, if a user is using a health management app, that data can be imported into the AI ​​counselor system to provide more detailed counseling. This will enable the integration unit to provide more comprehensive support to users.

[0069] The data collection unit can collect detailed data such as the tone of voice, changes in facial expressions, and changes in posture of the user. For example, the data collection unit can collect the tone of voice of the user. For example, the data collection unit can collect changes in the user's facial expressions. For example, the data collection unit can collect changes in the user's posture. By collecting detailed data on the user, a more accurate understanding of emotions becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's voice data into a generating AI and have the generating AI perform analysis of the voice data.

[0070] The analysis unit comprehensively analyzes collected data such as voice, facial expressions, and posture to understand the user's true emotions. For example, the analysis unit comprehensively analyzes the collected voice data. For example, the analysis unit comprehensively analyzes the collected facial expression data. For example, the analysis unit comprehensively analyzes the collected posture data. This allows for an accurate understanding of the user's true emotions through comprehensive data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected voice data into a generating AI and have the generating AI perform the analysis of the voice data.

[0071] The response unit can provide empathetic responses and appropriate advice tailored to the user's personality and values. For example, the response unit provides empathetic responses based on the user's personality and values. For example, the response unit provides appropriate advice based on the user's personality and values. This enhances the user's sense of security through responses and advice tailored to the user's personality and values. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input a response based on the user's personality and values ​​into a generating AI and have the generating AI perform the response generation.

[0072] The integration unit can integrate with electronic payment apps to facilitate daily use. For example, the integration unit can enable users to consult with an AI counselor. For example, the integration unit can enable users to pay for counseling services through an electronic payment app. This allows users to easily access counseling services through integration with electronic payment apps. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can have a message generation AI execute the exchange of messages between the user and the AI ​​counselor.

[0073] The response unit can perform early detection of stress and, if necessary, refer the user to a specialist. For example, if the user's stress or anxiety is severe, the response unit will recommend consulting a specialist. For example, if the user's stress or anxiety is mild, the response unit will provide appropriate advice. This allows the user to receive appropriate care early through early detection of stress and referral to a specialist. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's stress level into a generating AI and have the generating AI perform early detection of stress.

[0074] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will focus on collecting tone data and subtle changes in facial expressions. For example, if the user is relaxed, the data collection unit will prioritize collecting data on changes in posture and gestures. For example, if the user is anxious, the data collection unit will collect detailed information on intonation and facial tension. By adjusting the type of data according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, if the user has provided a lot of voice data in the past, the data collection unit will prioritize collecting voice data. For example, if the user has provided a lot of facial expression data in the past, the data collection unit will focus on collecting changes in facial expressions. For example, if the user has provided a lot of posture data in the past, the data collection unit will collect changes in posture in detail. By analyzing the past data collection history, the optimal collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can filter data based on the user's current environment and circumstances during data collection. For example, if the user is in a quiet environment, the data collection unit will prioritize collecting audio data. If the user is moving, the data collection unit will focus on collecting posture and gesture data. If the user is in a dark place, the data collection unit will refrain from collecting facial expression data and focus on audio data. This allows for more appropriate data collection by filtering data based on the user's current environment and circumstances. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current environment data into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting tone data and subtle changes in facial expressions. For example, if the user is relaxed, the data collection unit will prioritize collecting data on changes in posture and gestures. For example, if the user is anxious, the data collection unit will prioritize collecting intonation in the voice and the degree of tension in facial expressions. By prioritizing data according to the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at home, the data collection unit will prioritize the collection of data related to relaxation. If the user is at work, the data collection unit will prioritize the collection of data related to stress and tension. If the user is in a public place, the data collection unit will consider surrounding sounds and environmental noises when collecting data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media expressing stress, the data collection unit can collect data related to that emotion. For example, if a user posts on social media expressing relaxation, the data collection unit can collect data related to that emotion. For example, if a user posts on social media expressing anxiety, the data collection unit can collect data related to that emotion. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit applies an algorithm that focuses on analyzing data related to stress. For example, if the user is relaxed, the analysis unit applies an algorithm that focuses on analyzing data related to relaxation. For example, if the user is anxious, the analysis unit applies an algorithm that focuses on analyzing data related to anxiety. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a speech analysis algorithm to speech data. For example, the analysis unit applies a facial expression analysis algorithm to facial expression data. For example, the analysis unit applies a posture analysis algorithm to posture data. By applying different analysis algorithms depending on the data category, more appropriate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of stress-related data. For example, if the user is relaxed, the analysis unit will prioritize the analysis of relaxation-related data. For example, if the user is anxious, the analysis unit will prioritize the analysis of anxiety-related data. By determining the priority of analysis according to the user's emotions, more important data can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may perform the analysis while referring to past data. For example, the analysis unit may focus on analyzing data collected during a specific period. This allows for prioritizing the analysis of the most recent data by adjusting the order of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The analysis unit can adjust its analysis method based on the relevance of the data during analysis. For example, the analysis unit applies a detailed analysis method to highly relevant data. For example, it applies a simplified analysis method to less relevant data. For example, it applies an analysis method with an appropriate level of detail to data of moderate relevance. By adjusting the analysis method based on the relevance of the data, a more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis method.

[0086] The response unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is stressed, the response unit will respond in a calm tone. For example, if the user is relaxed, the response unit will respond in a cheerful tone. For example, if the user is anxious, the response unit will respond in a reassuring tone. By adjusting the way the response is expressed according to the user's emotions, a more empathetic response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into the generative AI and have the generative AI adjust the way the response is expressed.

[0087] The response unit can customize the content of its response based on the user's personality and values. For example, if the user is introverted, the response unit will provide a quiet and reserved response. If the user is extroverted, the response unit will provide an active and cheerful response. If the response unit is based on the user's values, the response unit will provide an empathetic response. By customizing the content of the response based on the user's personality and values, a more appropriate response becomes possible. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user personality and value data into a generating AI and have the generating AI perform the customization of the response content.

[0088] The response unit can provide the optimal response by referring to the user's past response history when responding. For example, the response unit may respond by referring to response styles that the user has preferred in the past. For example, the response unit may avoid response styles that the user has found unpleasant in the past. For example, the response unit may select the optimal response content from the user's past response history. This makes it possible to provide a more appropriate response by referring to the user's past response history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit may input the user's past response history data into a generating AI and have the generating AI perform the task of providing the optimal response.

[0089] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is stressed, the response unit will prioritize responses related to stress. For example, if the user is relaxed, the response unit will prioritize responses related to relaxation. For example, if the user is anxious, the response unit will prioritize responses related to anxiety. This allows for the priority of more important responses to be provided by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI determine the priority of responses.

[0090] The response unit can adjust the content of its response based on the user's current situation. For example, if the user is working, the response unit will provide a short, to-the-point response. If the user is relaxed, the response unit will provide a response that includes detailed explanations. If the user is in a hurry, the response unit will provide a quick and concise response. By adjusting the content of the response according to the user's current situation, a more appropriate response becomes possible. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's current situation data into a generating AI and have the generating AI adjust the content of the response.

[0091] The response unit can analyze the user's social media activity and provide a relevant response when responding. For example, if the user posts on social media expressing stress, the response unit will provide a response related to that emotion. For example, if the user posts on social media expressing relaxation, the response unit will provide a response related to that emotion. For example, if the user posts on social media expressing anxiety, the response unit will provide a response related to that emotion. In this way, by analyzing the user's social media activity, a relevant response can be provided. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing a relevant response.

[0092] The integration unit can estimate the user's emotions and select an app to integrate with based on the estimated emotions. For example, if the user is feeling stressed, the integration unit will integrate with a relaxation app. If the user is relaxed, the integration unit will integrate with an entertainment app. If the user is feeling anxious, the integration unit will integrate with a mental health app. This allows for integration with more appropriate apps by selecting apps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI select an app to integrate with.

[0093] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit will prioritize integration with apps that the user has frequently used in the past. For example, the integration unit will integrate by referring to the integration method that the user has preferred in the past. For example, the integration unit will select the optimal integration method from the user's past integration history. In this way, the optimal integration method can be selected by referring to the user's past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the user's past integration history data into a generating AI and have the generating AI perform the selection of the optimal integration method.

[0094] The integration unit can customize the means of integration based on the user's current usage status during integration. For example, the integration unit seamlessly integrates with the application the user is currently using. For example, the integration unit provides the optimal means of integration according to the user's current situation. For example, the integration unit customizes the means of integration considering the user's current usage status. This makes it possible to perform more appropriate integration by customizing the means of integration based on the user's current usage status. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's current usage status data into a generating AI and have the generating AI perform the customization of the means of integration.

[0095] The integration unit can estimate the user's emotions and determine the priority of integrations based on the estimated user emotions. For example, if the user is feeling stressed, the integration unit will prioritize integrations with apps that help reduce stress. For example, if the user is relaxed, the integration unit will prioritize integrations with apps that promote relaxation. For example, if the user is feeling anxious, the integration unit will prioritize integrations with apps that help reduce anxiety. This allows for prioritizing more important integrations by determining the priority of integrations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI determine the priority of integrations.

[0096] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is at home, the integration unit will prioritize integration with apps available at home. If the user is at work, the integration unit will prioritize integration with apps available at work. If the user is out, the integration unit will prioritize integration with apps available at the user's location. This allows the integration unit to select the optimal integration method by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal integration method.

[0097] The integration unit can analyze the user's social media activity during integration and provide relevant integration methods. For example, if the user posts on social media expressing stress, the integration unit can provide integration with apps related to that emotion. For example, if the user posts on social media expressing relaxation, the integration unit can provide integration with apps related to that emotion. For example, if the user posts on social media expressing anxiety, the integration unit can provide integration with apps related to that emotion. In this way, by analyzing the user's social media activity, relevant integration methods can be provided. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant integration methods.

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

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of stress-related data. For example, if the user is relaxed, the analysis unit will prioritize the analysis of relaxation-related data. For example, if the user is anxious, the analysis unit will prioritize the analysis of anxiety-related data. By determining the priority of analysis according to the user's emotions, more important data can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0100] The response unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is stressed, the response unit will respond in a calm tone. For example, if the user is relaxed, the response unit will respond in a cheerful tone. For example, if the user is anxious, the response unit will respond in a reassuring tone. By adjusting the way the response is expressed according to the user's emotions, a more empathetic response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into the generative AI and have the generative AI adjust the way the response is expressed.

[0101] The integration unit can estimate the user's emotions and select an app to integrate with based on the estimated emotions. For example, if the user is feeling stressed, the integration unit will integrate with a relaxation app. If the user is relaxed, the integration unit will integrate with an entertainment app. If the user is feeling anxious, the integration unit will integrate with a mental health app. This allows for integration with more appropriate apps by selecting apps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI select an app to integrate with.

[0102] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will focus on collecting tone data and subtle changes in facial expressions. For example, if the user is relaxed, the data collection unit will prioritize collecting data on changes in posture and gestures. For example, if the user is anxious, the data collection unit will collect detailed information on intonation and facial tension. By adjusting the type of data according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit applies an algorithm that focuses on analyzing data related to stress. For example, if the user is relaxed, the analysis unit applies an algorithm that focuses on analyzing data related to relaxation. For example, if the user is anxious, the analysis unit applies an algorithm that focuses on analyzing data related to anxiety. By adjusting the analysis algorithm according to the user's emotions, more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0104] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, if the user has provided a lot of voice data in the past, the data collection unit will prioritize collecting voice data. For example, if the user has provided a lot of facial expression data in the past, the data collection unit will focus on collecting changes in facial expressions. For example, if the user has provided a lot of posture data in the past, the data collection unit will collect changes in posture in detail. By analyzing the past data collection history, the optimal collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit performs an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0106] The response unit can customize the content of its response based on the user's personality and values. For example, if the user is introverted, the response unit will provide a quiet and reserved response. If the user is extroverted, the response unit will provide an active and cheerful response. If the response unit is based on the user's values, the response unit will provide an empathetic response. By customizing the content of the response based on the user's personality and values, a more appropriate response becomes possible. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input user personality and value data into a generating AI and have the generating AI perform the customization of the response content.

[0107] The integration unit can select the optimal integration method by referring to the user's past integration history during integration. For example, the integration unit will prioritize integration with apps that the user has frequently used in the past. For example, the integration unit will integrate by referring to the integration method that the user has preferred in the past. For example, the integration unit will select the optimal integration method from the user's past integration history. In this way, the optimal integration method can be selected by referring to the user's past integration history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input the user's past integration history data into a generating AI and have the generating AI perform the selection of the optimal integration method.

[0108] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is at home, the integration unit will prioritize integration with apps available at home. If the user is at work, the integration unit will prioritize integration with apps available at work. If the user is out, the integration unit will prioritize integration with apps available at the user's location. This allows the integration unit to select the optimal integration method by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal integration method.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects multimodal data such as the user's voice, facial expressions, and posture. For example, voice data is collected by a microphone, facial expression data by a camera, and posture data by a sensor. The data collection unit collects detailed data such as the tone of what the user is saying, changes in facial expressions, and changes in posture, and detects changes in emotion. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's true emotions. For example, it comprehensively analyzes the collected data such as voice, facial expressions, and posture, analyzing the tone of the voice data, changes in facial expressions, and changes in posture to identify the stress and anxiety the user is feeling. Step 3: The response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the analysis unit. For example, it provides empathetic responses tailored to the user's personality and values, offers empathetic words regarding the stress and anxiety the user is feeling, and provides appropriate advice. Step 4: The integration unit will connect with the electronic payment app. For example, it will enable users to consult with an AI counselor and pay for the counseling service through the electronic payment app.

[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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, response unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects multimodal data such as the user's voice, facial expressions, and posture using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the user's true emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results using the specific processing unit 290 of the data processing unit 12. The collaboration unit, for example, collaborates with an electronic payment application using the control unit 46A of the smart device 14, enabling the user to consult with an AI counselor and pay for counseling services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, response unit, and coordination unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects multimodal data such as the user's voice, facial expressions, and posture using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the user's true emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results using the specific processing unit 290 of the data processing unit 12. The coordination unit, for example, coordinates with an electronic payment application using the control unit 46A of the smart glasses 214, enabling the user to consult with an AI counselor and pay for counseling services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, response unit, and coordination unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects multimodal data such as the user's voice, facial expressions, and posture using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the user's true emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results using the specific processing unit 290 of the data processing unit 12. The coordination unit, for example, coordinates with an electronic payment application using the control unit 46A of the headset terminal 314, enabling the user to consult with an AI counselor and pay for counseling services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, response unit, and coordination unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects multimodal data such as the user's voice, facial expressions, and posture using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 to understand the user's true emotions. The response unit provides empathetic responses and appropriate advice based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The coordination unit, for example, coordinates with an electronic payment application using the control unit 46A of the robot 414, enabling the user to consult with an AI counselor and pay for counseling services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it 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 user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects multimodal data such as the user's voice, facial expressions, and posture, An analysis unit analyzes the data collected by the aforementioned collection unit to understand the user's true emotions, A response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the aforementioned analysis unit, It includes a unit that connects with electronic payment apps. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects detailed data such as the tone of voice, changes in facial expressions, and changes in posture of the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By comprehensively analyzing collected data such as voice, facial expressions, and posture, we can understand the user's true emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The response unit is Provides empathetic responses and appropriate advice tailored to the user's personality and values. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, We will integrate with electronic payment apps to promote everyday use. The system described in Appendix 1, characterized by the features described herein. (Note 6) The response unit is Early detection of stress and referral to specialists as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current environment and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis method is adjusted based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The response unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The response unit is When responding, customize the content of the response based on the user's personality and values. The system described in Appendix 1, characterized by the features described herein. (Note 21) The response unit is When responding, the system provides the most appropriate response by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The response unit is It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The response unit is When responding, adjust the content of the response based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The response unit is When responding, the system analyzes the user's social media activity and provides relevant responses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, It estimates the user's emotions and selects apps to collaborate with based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, During integration, the system selects the optimal integration method by referring to the user's past integration history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, During integration, the integration method is customized based on the user's current usage status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When integrating, the system selects the optimal integration method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, During integration, the system analyzes the user's social media activity and provides relevant integration methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects multimodal data such as the user's voice, facial expressions, and posture, An analysis unit analyzes the data collected by the aforementioned collection unit to understand the user's true emotions, A response unit provides empathetic responses and appropriate advice based on the analysis results obtained by the aforementioned analysis unit, It includes a unit that connects with electronic payment apps. A system characterized by the following features.

2. The aforementioned collection unit is The system collects detailed data such as the tone of voice, changes in facial expressions, and changes in posture of the user. The system according to feature 1.

3. The aforementioned analysis unit, By comprehensively analyzing collected data such as voice, facial expressions, and posture, we can understand the user's true emotions. The system according to feature 1.

4. The response unit is Provides empathetic responses and appropriate advice tailored to the user's personality and values. The system according to feature 1.

5. The aforementioned linkage unit is, We will integrate with electronic payment apps to promote everyday use. The system according to feature 1.

6. The response unit is Early detection of stress and referral to specialists as needed. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

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