system

Abstract

The system according to this embodiment aims to detect signs of customer stress and anxiety and propose appropriate countermeasures. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives data entered by the customer. The analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety. The proposal unit proposes improvement measures based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, it has not been sufficiently done to detect signs of customer stress and anxiety at an early stage and propose appropriate improvement measures, and there is room for improvement.

[0005] The system according to the embodiment aims to detect signs of customer stress and anxiety and propose appropriate improvement measures.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives data input by a customer. The analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety. The proposal unit proposes an improvement measure based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect signs of customer stress and anxiety and suggest appropriate countermeasures. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 1 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 psychological support system according to an embodiment of the present invention is a system that provides psychological support and self-improvement assistance to customers who are experiencing stress or anxiety. This system identifies subtle differences contained in images, videos, and audio, visualizes the mental state and stress level, and proposes solutions for improvement. For example, the customer inputs images, videos, or audio. Next, the system analyzes this data and detects signs of stress and anxiety. For example, the system identifies subtle changes in facial expressions or differences in voice tone. This makes it possible to visualize the customer's mental state and stress level. Next, the system proposes solutions based on the visualized mental state and stress level. For example, it provides relaxation music, meditation guidance, and stress management advice. This allows the customer to understand their own mental state and stress level and take appropriate measures. This system is designed to provide psychological support and self-improvement assistance to customers who are experiencing stress or anxiety, and contributes to improving the customer's mental health. Thus, the psychological support system can visualize the customer's mental state and stress level and propose appropriate solutions for improvement.

[0029] The psychological support system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives data entered by the customer. The data entered by the customer includes, but is not limited to, text data, image data, and audio data. The reception unit can, for example, receive data entered by the customer using a smartphone or personal computer. The reception unit can also receive data entered directly by the customer. The analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety. The analysis unit comprises, for example, an image analysis unit for analyzing image data, a video analysis unit for analyzing video data, and an audio analysis unit for analyzing audio data. The image analysis unit, for example, analyzes subtle changes in the customer's facial expressions and detects signs of stress or anxiety. The video analysis unit, for example, analyzes changes in the customer's movements and posture and detects signs of stress or anxiety. The audio analysis unit, for example, analyzes changes in the tone and speed of the customer's voice and detects signs of stress or anxiety. The proposal unit proposes improvement measures based on the analysis results obtained by the analysis unit. The proposal unit includes, for example, a music provision unit that provides music for relaxation, a meditation guide unit that provides guidance for meditation, and an advice provision unit that provides advice for stress management. The music provision unit provides, for example, music with a high relaxation effect according to the customer's stress level. The meditation guide unit provides, for example, guidance for meditation according to the customer's mental state. The advice provision unit provides, for example, specific advice for the customer's stress management. As a result, the psychological support system according to the embodiment can detect the customer's stress and anxiety and propose appropriate solutions.

[0030] The analysis unit may include an image analysis unit that analyzes image data. The image analysis unit analyzes image data such as JPEG, PNG, and GIF. The image analysis unit can, for example, analyze subtle changes in a customer's facial expression to detect signs of stress or anxiety. For example, the image analysis unit can analyze the frequency of a customer's smiles and eye movements to detect signs of stress or anxiety. The image analysis unit can also analyze a customer's complexion and skin condition to detect signs of stress or anxiety. Furthermore, the image analysis unit can analyze changes in a customer's posture and movements to detect signs of stress or anxiety. This allows for more accurate detection of signs of stress and anxiety in a customer by analyzing image data. Some or all of the above-described processes in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input customer image data into a generating AI and have the generating AI perform the detection of signs of stress and anxiety.

[0031] The analysis unit may include a video analysis unit that analyzes video data. The video analysis unit analyzes video data such as MP4, AVI, and MOV. The video analysis unit can, for example, analyze changes in the customer's movements and posture to detect signs of stress and anxiety. For example, the video analysis unit can analyze the customer's walking rhythm and hand movements to detect signs of stress and anxiety. The video analysis unit can also analyze changes in the customer's facial expressions and eye movements to detect signs of stress and anxiety. Furthermore, the video analysis unit can analyze changes in the customer's body movements and posture to detect signs of stress and anxiety. This allows for more accurate detection of signs of stress and anxiety in the customer by analyzing video data. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without AI. For example, the video analysis unit can input the customer's video data into a generating AI and have the generating AI perform the detection of signs of stress and anxiety.

[0032] The analysis unit may include a voice analysis unit that analyzes audio data. The voice analysis unit analyzes audio data such as WAV, MP3, and AAC. The voice analysis unit can, for example, analyze changes in the tone and speed of the customer's voice to detect signs of stress or anxiety. For example, the voice analysis unit can analyze changes in the pitch and rhythm of the customer's voice to detect signs of stress or anxiety. The voice analysis unit can also analyze changes in the volume and tempo of the customer's voice to detect signs of stress or anxiety. Furthermore, the voice analysis unit can analyze changes in the quality and volume of the customer's voice to detect signs of stress or anxiety. This allows for more accurate detection of signs of stress and anxiety in the customer by analyzing the audio data. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the customer's audio data into a generating AI and have the generating AI perform the detection of signs of stress and anxiety.

[0033] The proposed unit may include a music provision unit that provides music for relaxation. The music provision unit provides music for relaxation, such as classical music, nature sounds, and healing music. The music provision unit provides music with a high relaxation effect according to the customer's stress level. For example, if the customer's stress level is high, the music provision unit can provide classical music with a high relaxation effect. If the customer's stress level is moderate, the music provision unit can also provide nature sounds or healing music. Furthermore, if the customer's stress level is low, the music provision unit can provide upbeat music. In this way, the customer's stress can be reduced by providing music for relaxation. Some or all of the above processing in the music provision unit may be performed using AI, for example, or without AI. For example, the music provision unit can input the customer's stress level into a generating AI and have the generating AI select music with a high relaxation effect.

[0034] The proposed unit may include a meditation guide unit that provides meditation guidance. The meditation guide unit provides meditation guidance such as audio guides, video guides, and text guides. The meditation guide unit provides meditation guidance according to the customer's mental state, for example. For example, if the customer's stress level is high, the meditation guide unit can provide a meditation guide with a high relaxation effect. Also, if the customer's stress level is moderate, the meditation guide unit can provide a meditation guide to improve concentration. Furthermore, if the customer's stress level is low, the meditation guide unit can provide a meditation guide for self-improvement. In this way, by providing meditation guidance, the mental health of the customer is improved. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input the customer's mental state into a generating AI and have the generating AI select an appropriate meditation guide.

[0035] The proposal unit may include an advice-providing unit that provides advice for stress management. The advice-providing unit provides advice for stress management, such as suggestions for breathing exercises, time management, and exercise. The advice-providing unit provides specific advice according to the customer's stress level. For example, if the customer's stress level is high, the advice-providing unit can suggest breathing exercises that have a high relaxation effect. Also, if the customer's stress level is moderate, the advice-providing unit can suggest methods for time management. Furthermore, if the customer's stress level is low, the advice-providing unit can suggest exercise. In this way, the customer's stress can be reduced by providing advice for stress management. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the customer's stress level into a generating AI and have the generating AI select appropriate advice.

[0036] The reception department can analyze a customer's past data submission history and select an appropriate reception method. For example, the reception department can prioritize suggesting data submission methods (images, videos, audio, etc.) that the customer has frequently used in the past. The reception department can also analyze the time periods when customers have previously submitted data and suggest the optimal reception time. Furthermore, the reception department can select the optimal reception method for specific days of the week or time periods based on the customer's past data submission history. In this way, the optimal reception method can be selected by analyzing the customer's past data submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's past data submission history into a generating AI and have the generating AI select the optimal reception method.

[0037] The reception unit can filter data upon receipt based on the customer's current lifestyle and areas of interest. For example, if a customer inputs their current lifestyle, the AI ​​will filter the data based on that information. The reception unit can also accept only highly relevant data based on the customer's areas of interest. Furthermore, the reception unit can prioritize data according to the customer's lifestyle and areas of interest, prioritizing the acceptance of important data. This allows for the acceptance of more relevant data by filtering data based on the customer's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the customer's lifestyle and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0038] The reception desk can prioritize receiving data that is highly relevant, taking into account the customer's geographical location. For example, if the customer is in a specific region, the reception desk will prioritize receiving data related to that region. Furthermore, based on the customer's geographical location, the reception desk can also prioritize receiving data related to region-specific stress factors. Additionally, if the customer is traveling, the reception desk can prioritize receiving data related to their travel destination. This allows for the priority of receiving highly relevant data by considering the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into a generating AI and have the generating AI select highly relevant data.

[0039] The reception department can analyze the customer's social media activity and receive relevant data upon receiving the data. For example, the reception department can analyze the customer's social media posts and prioritize receiving data related to stress and anxiety. The reception department can also receive relevant data based on the customer's interests on social media. Furthermore, the reception department can consider the frequency of the customer's social media activity and receive data at an appropriate time. This allows for the priority reception of relevant data by analyzing the customer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the customer's social media activity data into a generating AI and have the generating AI select relevant data.

[0040] 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 can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can set analysis priorities according to the importance of the data, prioritizing the analysis of important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. It can also apply a video analysis algorithm to video data. Furthermore, it can apply an audio analysis algorithm to audio data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category. 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 apply the appropriate analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes 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 order.

[0044] The proposal unit can adjust the level of detail in its proposals based on the importance of the improvement measures. For example, it can provide detailed proposals for high-importance improvement measures and simplified proposals for low-importance measures. Furthermore, it can prioritize proposals according to their importance, prioritizing the most important measures. This allows for efficient proposals by adjusting the level of detail based on the importance of the improvement measures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the improvement measures into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0045] The suggestion unit can apply different suggestion algorithms depending on the category of the improvement measure when making a suggestion. For example, the suggestion unit can apply a music suggestion algorithm for music for relaxation. It can also apply a meditation suggestion algorithm for meditation guidance. Furthermore, it can apply an advice suggestion algorithm for advice on stress management. By applying the appropriate suggestion algorithm according to the category of the improvement measure, the accuracy of the suggestion is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the improvement measure into a generating AI and have the generating AI apply the appropriate suggestion algorithm.

[0046] The proposal department can determine the priority of proposals based on the submission timing of the improvement measures. For example, the proposal department will prioritize the most recent improvement measures. It can also postpone the proposal of older improvement measures. Furthermore, the proposal department can adjust the proposal schedule based on the submission timing. This allows for efficient proposals by prioritizing proposals based on the submission timing of the improvement measures. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the improvement measures into a generating AI and have the generating AI determine the priority of the proposals.

[0047] The proposal unit can adjust the order of proposals based on the relevance of the improvement measures. For example, the proposal unit can prioritize proposing highly relevant improvement measures. It can also postpone proposing less relevant improvement measures. Furthermore, the proposal unit can adjust the proposal schedule based on the relevance of the improvement measures. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the improvement measures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the improvement measures into a generating AI and have the generating AI adjust the order of proposals.

[0048] The music delivery unit can provide appropriate music by referring to the customer's past music history when providing music. For example, the music delivery unit can refer to the customer's past music playback history and provide music that matches the customer's preferences. The music delivery unit can also prioritize providing music that the customer has previously given a high rating to. Furthermore, the music delivery unit can analyze the customer's past music history and provide music that is best suited to the customer's current emotional state. In this way, the optimal music can be provided by referring to the customer's past music history. Some or all of the above processing in the music delivery unit may be performed using AI, for example, or not using AI. For example, the music delivery unit can input the customer's past music history into a generating AI and have the generating AI select appropriate music.

[0049] The music service can customize the music selection based on the customer's current lifestyle when providing music. For example, if the customer is working, the music service can provide music that enhances concentration. It can also provide music with a high relaxation effect if the customer wants to relax. Furthermore, if the customer is exercising, it can provide music that boosts energy. This allows for the provision of more appropriate music by customizing it based on the customer's lifestyle. Some or all of the above processing in the music service may be performed using AI, for example, or without AI. For example, the music service can input customer lifestyle data into a generating AI and have the generating AI perform the music selection.

[0050] The music delivery unit can provide appropriate music by considering the customer's geographical location information when providing music. For example, if the customer is in a specific region, the music delivery unit can provide music related to that region. The music delivery unit can also provide region-specific music based on the customer's geographical location information. Furthermore, if the customer is traveling, the music delivery unit can provide music related to their travel destination. In this way, the optimal music can be provided by considering the customer's geographical location information. Some or all of the above processing in the music delivery unit may be performed using AI, for example, or without AI. For example, the music delivery unit can input the customer's geographical location information into a generating AI and have the generating AI select appropriate music.

[0051] The music provision department can analyze a customer's social media activity and provide relevant music when providing music. For example, the music provision department can analyze a customer's social media posts and provide music related to the customer's interests. The music provision department can also consider the frequency of the customer's social media activity and provide music at an appropriate time. Furthermore, the music provision department can prioritize providing music that the customer has highly rated based on their social media ratings. In this way, relevant music can be provided by analyzing the customer's social media activity. Some or all of the above processing in the music provision department may be performed using AI, for example, or not using AI. For example, the music provision department can input customer social media activity data into a generating AI and have the generating AI select relevant music.

[0052] The meditation guide unit can provide the most suitable guide by referring to the customer's past meditation history when providing a meditation guide. For example, the meditation guide unit can refer to the frequency and type of meditation the customer has performed in the past and provide a meditation guide that suits the customer's preferences. The meditation guide unit can also prioritize providing meditation guides that the customer has previously given high ratings to. Furthermore, the meditation guide unit can analyze the customer's past meditation history and provide a meditation guide that is best suited to the customer's current emotional state. In this way, the optimal meditation guide can be provided by referring to the customer's past meditation history. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or not using AI. For example, the meditation guide unit can input the customer's past meditation history into a generating AI and have the generating AI select an appropriate meditation guide.

[0053] The meditation guide unit can customize the content of the meditation guide based on the customer's current lifestyle when providing it. For example, if the customer is at work, the meditation guide unit can provide a meditation guide to improve concentration. It can also provide a meditation guide with a high relaxation effect if the customer wants to relax. Furthermore, if the customer has just exercised, the meditation guide unit can provide a meditation guide with a high recovery effect. This allows for the provision of more appropriate meditation guides by customizing them based on the customer's lifestyle. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input customer lifestyle data into a generating AI and have the generating AI customize the content of the meditation guide.

[0054] The meditation guide unit can provide appropriate guidance by considering the customer's geographical location when providing meditation guidance. For example, if the customer is in a specific region, the meditation guide unit can provide a meditation guide related to that region. The meditation guide unit can also provide region-specific meditation guides based on the customer's geographical location. Furthermore, if the customer is traveling, the meditation guide unit can provide a meditation guide related to their travel destination. In this way, the optimal meditation guide can be provided by considering the customer's geographical location. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input the customer's geographical location information into a generating AI and have the generating AI select an appropriate meditation guide.

[0055] The meditation guide department can analyze a customer's social media activity and provide relevant guides when offering meditation guides. For example, it can analyze a customer's social media posts and provide meditation guides related to the customer's interests. It can also consider the customer's frequency of social media activity and provide meditation guides at an appropriate time. Furthermore, based on the customer's social media ratings, the meditation guide department can prioritize providing meditation guides that the customer has highly rated. In this way, relevant meditation guides can be provided by analyzing the customer's social media activity. Some or all of the above processing in the meditation guide department may be performed using AI, for example, or not. For example, the meditation guide department can input customer social media activity data into a generating AI and have the generating AI select relevant meditation guides.

[0056] The advice-providing unit can provide optimal advice by referring to the customer's past behavioral history when providing advice. For example, the advice-providing unit can refer to the frequency and type of actions the customer has taken in the past and provide advice that matches the customer's preferences. The advice-providing unit can also prioritize providing advice that the customer has previously given a high rating to. Furthermore, the advice-providing unit can analyze the customer's past behavioral history and provide advice that is best suited to the customer's current emotional state. In this way, optimal advice can be provided by referring to the customer's past behavioral history. Some or all of the above processes in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the customer's past behavioral history into a generating AI and have the generating AI select appropriate advice.

[0057] The advice-providing unit can customize the content of advice based on the customer's current living situation when providing advice. For example, if the customer is at work, the advice-providing unit can provide advice to improve concentration. It can also provide advice with a high relaxation effect if the customer wants to relax. Furthermore, if the customer has just exercised, the advice-providing unit can provide advice with a high recovery effect. This allows for the provision of more appropriate advice by customizing it based on the customer's living situation. Some or all of the above-described processes in the advice-providing unit may be performed using AI, for example, or not. For example, the advice-providing unit can input customer living situation data into a generating AI and have the generating AI customize the content of the advice.

[0058] The advice-providing unit can provide appropriate advice by considering the customer's geographical location when providing advice. For example, if the customer is in a specific region, the advice-providing unit can provide advice relevant to that region. The advice-providing unit can also provide region-specific advice based on the customer's geographical location. Furthermore, if the customer is traveling, the advice-providing unit can provide advice relevant to their travel destination. In this way, by considering the customer's geographical location, the optimal advice can be provided. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the customer's geographical location into a generating AI and have the generating AI select appropriate advice.

[0059] The advice-providing unit can analyze the customer's social media activity and provide relevant advice when providing advice. For example, the advice-providing unit can analyze the customer's social media posts and provide advice related to the customer's interests. The advice-providing unit can also consider the customer's frequency of social media activity and provide advice at an appropriate time. Furthermore, based on the customer's social media ratings, the advice-providing unit can prioritize providing advice that the customer has given a high rating to. In this way, relevant advice can be provided by analyzing the customer's social media activity. Some or all of the above processes in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the customer's social media activity data into a generating AI and have the generating AI select relevant advice.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The psychological support system can also be equipped with a biofeedback unit. The biofeedback unit acquires the customer's physiological data (e.g., heart rate, skin electrical activity, breathing patterns, etc.) and transmits this data to the analysis unit. The analysis unit can use this physiological data to more accurately detect signs of stress and anxiety in the customer. For example, by analyzing fluctuations in heart rate and changes in skin electrical activity, the customer's stress level can be assessed. Similarly, by analyzing changes in breathing patterns, the customer's state of relaxation can be assessed. This allows the psychological support system to use the customer's physiological data to more accurately assess their mental state and propose improvement measures.

[0062] The psychological support system can also be equipped with a behavioral tracking unit. The behavioral tracking unit acquires data on the customer's daily behavior (e.g., steps taken, exercise level, sleep patterns, etc.) and transmits this data to an analysis unit. The analysis unit can use this behavioral data to detect signs of stress and anxiety in the customer. For example, by analyzing a decrease in exercise level or disruptions in sleep patterns, the customer's stress level can be assessed. Furthermore, by analyzing fluctuations in step count, the customer's activity level can be assessed. This allows the psychological support system to use the customer's daily behavioral data to provide a more accurate assessment of their mental state and suggest improvement measures.

[0063] The psychological support system can also be equipped with an environmental sensor unit. The environmental sensor unit acquires environmental data around the customer (e.g., temperature, humidity, noise level, etc.) and transmits this data to an analysis unit. The analysis unit can use this environmental data to detect signs of stress and anxiety in the customer. For example, by analyzing increases in noise levels or temperature fluctuations, the customer's stress level can be evaluated. Similarly, by analyzing changes in humidity, the customer's comfort level can be evaluated. This allows the psychological support system to use the customer's surrounding environmental data to provide a more accurate assessment of their mental state and suggest improvement measures.

[0064] The psychological support system can also be equipped with a social interaction unit. This unit analyzes the customer's social media activity and message exchanges and transmits this data to the analysis unit. The analysis unit can use this social data to detect signs of stress and anxiety in the customer. For example, by analyzing changes in the frequency and content of social media posts, the customer's mental state can be assessed. Furthermore, by analyzing the content and tone of message exchanges, the customer's emotional state can be assessed. This allows the psychological support system to use the customer's social interaction data to provide a more accurate assessment of their mental state and propose improvement measures.

[0065] The psychological support system can also be equipped with a personalized content delivery unit. This unit provides individually customized content based on the customer's past usage history and preferences. For example, it can provide highly relaxing content based on videos the customer has previously enjoyed watching or music the customer has listened to. It can also customize meditation guides and stress management advice according to the customer's preferences. Furthermore, it can analyze the customer's past usage history and deliver content at the optimal time. This allows the psychological support system to provide more effective mental support by delivering content tailored to the customer's individual needs.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives data entered by the customer. This data may include, for example, text data, image data, and audio data. The reception desk can accept data entered by the customer using a smartphone or computer. It can also accept data entered by the customer directly. Step 2: The analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety. The analysis unit comprises an image analysis unit that analyzes image data, a video analysis unit that analyzes video data, and a voice analysis unit that analyzes audio data. The image analysis unit analyzes subtle changes in the customer's facial expressions and detects signs of stress or anxiety. The video analysis unit analyzes changes in the customer's movements and posture and detects signs of stress or anxiety. The voice analysis unit analyzes changes in the tone and speed of the customer's voice and detects signs of stress or anxiety. Step 3: The proposal unit proposes improvement measures based on the analysis results obtained by the analysis unit. The proposal unit comprises a music provision unit that provides music for relaxation, a meditation guide unit that provides guidance for meditation, and an advice provision unit that provides advice for stress management. The music provision unit provides music with a high relaxation effect according to the customer's stress level. The meditation guide unit provides guidance for meditation according to the customer's mental state. The advice provision unit provides specific advice for the customer's stress management.

[0068] (Example of form 2) The psychological support system according to an embodiment of the present invention is a system that provides psychological support and self-improvement assistance to customers who are experiencing stress or anxiety. This system identifies subtle differences contained in images, videos, and audio, visualizes the mental state and stress level, and proposes solutions for improvement. For example, the customer inputs images, videos, or audio. Next, the system analyzes this data and detects signs of stress and anxiety. For example, the system identifies subtle changes in facial expressions or differences in voice tone. This makes it possible to visualize the customer's mental state and stress level. Next, the system proposes solutions based on the visualized mental state and stress level. For example, it provides relaxation music, meditation guidance, and stress management advice. This allows the customer to understand their own mental state and stress level and take appropriate measures. This system is designed to provide psychological support and self-improvement assistance to customers who are experiencing stress or anxiety, and contributes to improving the customer's mental health. Thus, the psychological support system can visualize the customer's mental state and stress level and propose appropriate solutions for improvement.

[0069] The psychological support system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives data entered by the customer. The data entered by the customer includes, but is not limited to, text data, image data, and audio data. The reception unit can, for example, receive data entered by the customer using a smartphone or personal computer. The reception unit can also receive data entered directly by the customer. The analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety. The analysis unit comprises, for example, an image analysis unit for analyzing image data, a video analysis unit for analyzing video data, and an audio analysis unit for analyzing audio data. The image analysis unit, for example, analyzes subtle changes in the customer's facial expressions and detects signs of stress or anxiety. The video analysis unit, for example, analyzes changes in the customer's movements and posture and detects signs of stress or anxiety. The audio analysis unit, for example, analyzes changes in the tone and speed of the customer's voice and detects signs of stress or anxiety. The proposal unit proposes improvement measures based on the analysis results obtained by the analysis unit. The proposal unit includes, for example, a music provision unit that provides music for relaxation, a meditation guide unit that provides guidance for meditation, and an advice provision unit that provides advice for stress management. The music provision unit provides, for example, music with a high relaxation effect according to the customer's stress level. The meditation guide unit provides, for example, guidance for meditation according to the customer's mental state. The advice provision unit provides, for example, specific advice for the customer's stress management. As a result, the psychological support system according to the embodiment can detect the customer's stress and anxiety and propose appropriate solutions.

[0070] The analysis unit may include an image analysis unit that analyzes image data. The image analysis unit analyzes image data such as JPEG, PNG, and GIF. The image analysis unit can, for example, analyze subtle changes in a customer's facial expression to detect signs of stress or anxiety. For example, the image analysis unit can analyze the frequency of a customer's smiles and eye movements to detect signs of stress or anxiety. The image analysis unit can also analyze a customer's complexion and skin condition to detect signs of stress or anxiety. Furthermore, the image analysis unit can analyze changes in a customer's posture and movements to detect signs of stress or anxiety. This allows for more accurate detection of signs of stress and anxiety in a customer by analyzing image data. Some or all of the above-described processes in the image analysis unit may be performed using AI, for example, or without AI. For example, the image analysis unit can input customer image data into a generating AI and have the generating AI perform the detection of signs of stress and anxiety.

[0071] The analysis unit may include a video analysis unit that analyzes video data. The video analysis unit analyzes video data such as MP4, AVI, and MOV. The video analysis unit can, for example, analyze changes in the customer's movements and posture to detect signs of stress and anxiety. For example, the video analysis unit can analyze the customer's walking rhythm and hand movements to detect signs of stress and anxiety. The video analysis unit can also analyze changes in the customer's facial expressions and eye movements to detect signs of stress and anxiety. Furthermore, the video analysis unit can analyze changes in the customer's body movements and posture to detect signs of stress and anxiety. This allows for more accurate detection of signs of stress and anxiety in the customer by analyzing video data. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without AI. For example, the video analysis unit can input the customer's video data into a generating AI and have the generating AI perform the detection of signs of stress and anxiety.

[0072] The analysis unit may include a voice analysis unit that analyzes audio data. The voice analysis unit analyzes audio data such as WAV, MP3, and AAC. The voice analysis unit can, for example, analyze changes in the tone and speed of the customer's voice to detect signs of stress or anxiety. For example, the voice analysis unit can analyze changes in the pitch and rhythm of the customer's voice to detect signs of stress or anxiety. The voice analysis unit can also analyze changes in the volume and tempo of the customer's voice to detect signs of stress or anxiety. Furthermore, the voice analysis unit can analyze changes in the quality and volume of the customer's voice to detect signs of stress or anxiety. This allows for more accurate detection of signs of stress and anxiety in the customer by analyzing the audio data. Some or all of the above processing in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the customer's audio data into a generating AI and have the generating AI perform the detection of signs of stress and anxiety.

[0073] The proposed unit may include a music provision unit that provides music for relaxation. The music provision unit provides music for relaxation, such as classical music, nature sounds, and healing music. The music provision unit provides music with a high relaxation effect according to the customer's stress level. For example, if the customer's stress level is high, the music provision unit can provide classical music with a high relaxation effect. If the customer's stress level is moderate, the music provision unit can also provide nature sounds or healing music. Furthermore, if the customer's stress level is low, the music provision unit can provide upbeat music. In this way, the customer's stress can be reduced by providing music for relaxation. Some or all of the above processing in the music provision unit may be performed using AI, for example, or without AI. For example, the music provision unit can input the customer's stress level into a generating AI and have the generating AI select music with a high relaxation effect.

[0074] The proposed unit may include a meditation guide unit that provides meditation guidance. The meditation guide unit provides meditation guidance such as audio guides, video guides, and text guides. The meditation guide unit provides meditation guidance according to the customer's mental state, for example. For example, if the customer's stress level is high, the meditation guide unit can provide a meditation guide with a high relaxation effect. Also, if the customer's stress level is moderate, the meditation guide unit can provide a meditation guide to improve concentration. Furthermore, if the customer's stress level is low, the meditation guide unit can provide a meditation guide for self-improvement. In this way, by providing meditation guidance, the mental health of the customer is improved. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input the customer's mental state into a generating AI and have the generating AI select an appropriate meditation guide.

[0075] The proposal unit may include an advice-providing unit that provides advice for stress management. The advice-providing unit provides advice for stress management, such as suggestions for breathing exercises, time management, and exercise. The advice-providing unit provides specific advice according to the customer's stress level. For example, if the customer's stress level is high, the advice-providing unit can suggest breathing exercises that have a high relaxation effect. Also, if the customer's stress level is moderate, the advice-providing unit can suggest methods for time management. Furthermore, if the customer's stress level is low, the advice-providing unit can suggest exercise. In this way, the customer's stress can be reduced by providing advice for stress management. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the customer's stress level into a generating AI and have the generating AI select appropriate advice.

[0076] The reception desk can estimate the customer's emotions and adjust the timing of data reception based on the estimated emotions. For example, if the customer is stressed, the AI ​​can immediately receive the data and begin analysis quickly. If the customer is relaxed, the AI ​​can slightly delay data reception, allowing the customer to provide the data in a calm state. Furthermore, if the customer is busy, the AI ​​can time data reception to an appropriate moment, reducing the customer's burden. By adjusting the timing of data reception according to the customer's emotions, data can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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 reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI and have the generative AI adjust the timing of data reception.

[0077] The reception department can analyze a customer's past data submission history and select an appropriate reception method. For example, the reception department can prioritize suggesting data submission methods (images, videos, audio, etc.) that the customer has frequently used in the past. The reception department can also analyze the time periods when customers have previously submitted data and suggest the optimal reception time. Furthermore, the reception department can select the optimal reception method for specific days of the week or time periods based on the customer's past data submission history. In this way, the optimal reception method can be selected by analyzing the customer's past data submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's past data submission history into a generating AI and have the generating AI select the optimal reception method.

[0078] The reception unit can filter data upon receipt based on the customer's current lifestyle and areas of interest. For example, if a customer inputs their current lifestyle, the AI ​​will filter the data based on that information. The reception unit can also accept only highly relevant data based on the customer's areas of interest. Furthermore, the reception unit can prioritize data according to the customer's lifestyle and areas of interest, prioritizing the acceptance of important data. This allows for the acceptance of more relevant data by filtering data based on the customer's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the customer's lifestyle and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0079] The reception desk can estimate the customer's emotions and prioritize the data to be received based on the estimated emotions. For example, if the customer is stressed, the AI ​​will prioritize receiving stress-related data. Similarly, if the customer is relaxed, the AI ​​can prioritize receiving relaxation-related data. Furthermore, if the customer is anxious, the AI ​​can prioritize receiving anxiety-relieving data. This allows for the prioritization of more appropriate data based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI and have the generative AI determine the data prioritization.

[0080] The reception desk can prioritize receiving data that is highly relevant, taking into account the customer's geographical location. For example, if the customer is in a specific region, the reception desk will prioritize receiving data related to that region. Furthermore, based on the customer's geographical location, the reception desk can also prioritize receiving data related to region-specific stress factors. Additionally, if the customer is traveling, the reception desk can prioritize receiving data related to their travel destination. This allows for the priority of receiving highly relevant data by considering the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into a generating AI and have the generating AI select highly relevant data.

[0081] The reception department can analyze the customer's social media activity and receive relevant data upon receiving the data. For example, the reception department can analyze the customer's social media posts and prioritize receiving data related to stress and anxiety. The reception department can also receive relevant data based on the customer's interests on social media. Furthermore, the reception department can consider the frequency of the customer's social media activity and receive data at an appropriate time. This allows for the priority reception of relevant data by analyzing the customer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the customer's social media activity data into a generating AI and have the generating AI select relevant data.

[0082] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is stressed, the analysis unit can provide the analysis results in a simple and easy-to-understand format. If the customer is relaxed, the analysis unit can also provide detailed analysis results to promote a deeper understanding. Furthermore, if the customer is anxious, the analysis unit can provide the analysis results in a reassuring way. In this way, by adjusting the presentation of the analysis based on the customer's emotions, more appropriate analysis results can be provided. 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 customer emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0083] 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 can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can set analysis priorities according to the importance of the data, prioritizing the analysis of important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an image analysis algorithm to image data. It can also apply a video analysis algorithm to video data. Furthermore, it can apply an audio analysis algorithm to audio data. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category. 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 apply the appropriate analysis algorithm.

[0085] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is stressed, the analysis unit can provide a short, concise analysis. If the customer is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the customer is anxious, the analysis unit can provide a reassuring analysis. By adjusting the length of the analysis based on the customer's emotions, more appropriate analysis results can be provided. 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 not using AI. For example, the analysis unit can input customer emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes 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 order.

[0088] The proposal unit can estimate the customer's emotions and adjust the way it presents its proposals based on those emotions. For example, if the customer is stressed, the proposal unit will present simple and easy-to-understand proposals. If the customer is relaxed, the proposal unit can present detailed proposals to promote deeper understanding. Furthermore, if the customer is anxious, the proposal unit can present proposals in a reassuring manner. By adjusting the way proposals are presented based on the customer's emotions, more appropriate proposals can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input customer emotion data into a generative AI and have the generative AI adjust the way proposals are presented.

[0089] The proposal unit can adjust the level of detail in its proposals based on the importance of the improvement measures. For example, it can provide detailed proposals for high-importance improvement measures and simplified proposals for low-importance measures. Furthermore, it can prioritize proposals according to their importance, prioritizing the most important measures. This allows for efficient proposals by adjusting the level of detail based on the importance of the improvement measures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the improvement measures into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0090] The suggestion unit can apply different suggestion algorithms depending on the category of the improvement measure when making a suggestion. For example, the suggestion unit can apply a music suggestion algorithm for music for relaxation. It can also apply a meditation suggestion algorithm for meditation guidance. Furthermore, it can apply an advice suggestion algorithm for advice on stress management. By applying the appropriate suggestion algorithm according to the category of the improvement measure, the accuracy of the suggestion is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category of the improvement measure into a generating AI and have the generating AI apply the appropriate suggestion algorithm.

[0091] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on those emotions. For example, if the customer is stressed, the suggestion unit can provide a short, concise suggestion. If the customer is relaxed, the suggestion unit can provide a more detailed suggestion. Furthermore, if the customer is anxious, the suggestion unit can provide a reassuring suggestion. By adjusting the length of the suggestion based on the customer's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input customer emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0092] The proposal department can determine the priority of proposals based on the submission timing of the improvement measures. For example, the proposal department will prioritize the most recent improvement measures. It can also postpone the proposal of older improvement measures. Furthermore, the proposal department can adjust the proposal schedule based on the submission timing. This allows for efficient proposals by prioritizing proposals based on the submission timing of the improvement measures. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of the improvement measures into a generating AI and have the generating AI determine the priority of the proposals.

[0093] The proposal unit can adjust the order of proposals based on the relevance of the improvement measures. For example, the proposal unit can prioritize proposing highly relevant improvement measures. It can also postpone proposing less relevant improvement measures. Furthermore, the proposal unit can adjust the proposal schedule based on the relevance of the improvement measures. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the improvement measures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the improvement measures into a generating AI and have the generating AI adjust the order of proposals.

[0094] The music provision department can estimate the customer's emotions and select a music genre to provide based on those emotions. For example, if a customer is feeling stressed, the music provision department can provide classical music with a high relaxation effect. If the customer is relaxed, the music provision department can also provide upbeat music. Furthermore, if the customer is feeling anxious, the music provision department can provide music that provides a sense of security. This allows for the provision of more appropriate music by selecting a music genre based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the music provision department may be performed using AI, or not. For example, the music provision department can input customer emotion data into a generative AI and have the generative AI select a music genre.

[0095] The music delivery unit can provide appropriate music by referring to the customer's past music history when providing music. For example, the music delivery unit can refer to the customer's past music playback history and provide music that matches the customer's preferences. The music delivery unit can also prioritize providing music that the customer has previously given a high rating to. Furthermore, the music delivery unit can analyze the customer's past music history and provide music that is best suited to the customer's current emotional state. In this way, the optimal music can be provided by referring to the customer's past music history. Some or all of the above processing in the music delivery unit may be performed using AI, for example, or not using AI. For example, the music delivery unit can input the customer's past music history into a generating AI and have the generating AI select appropriate music.

[0096] The music service can customize the music selection based on the customer's current lifestyle when providing music. For example, if the customer is working, the music service can provide music that enhances concentration. It can also provide music with a high relaxation effect if the customer wants to relax. Furthermore, if the customer is exercising, it can provide music that boosts energy. This allows for the provision of more appropriate music by customizing it based on the customer's lifestyle. Some or all of the above processing in the music service may be performed using AI, for example, or without AI. For example, the music service can input customer lifestyle data into a generating AI and have the generating AI perform the music selection.

[0097] The music provider can estimate the customer's emotions and adjust the playback order of music based on those emotions. For example, if the customer is stressed, the music provider can play relaxing music first. If the customer is relaxed, it can also play upbeat music first. Furthermore, if the customer is anxious, it can play reassuring music first. By adjusting the playback order of music based on the customer's emotions, more appropriate music can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the music provider may be performed using AI or not. For example, the music provider can input customer emotion data into a generative AI and have the generative AI adjust the playback order of music.

[0098] The music delivery unit can provide appropriate music by considering the customer's geographical location information when providing music. For example, if the customer is in a specific region, the music delivery unit can provide music related to that region. The music delivery unit can also provide region-specific music based on the customer's geographical location information. Furthermore, if the customer is traveling, the music delivery unit can provide music related to their travel destination. In this way, the optimal music can be provided by considering the customer's geographical location information. Some or all of the above processing in the music delivery unit may be performed using AI, for example, or without AI. For example, the music delivery unit can input the customer's geographical location information into a generating AI and have the generating AI select appropriate music.

[0099] The music provision department can analyze a customer's social media activity and provide relevant music when providing music. For example, the music provision department can analyze a customer's social media posts and provide music related to the customer's interests. The music provision department can also consider the frequency of the customer's social media activity and provide music at an appropriate time. Furthermore, the music provision department can prioritize providing music that the customer has highly rated based on their social media ratings. In this way, relevant music can be provided by analyzing the customer's social media activity. Some or all of the above processing in the music provision department may be performed using AI, for example, or not using AI. For example, the music provision department can input customer social media activity data into a generating AI and have the generating AI select relevant music.

[0100] The meditation guide unit can estimate the customer's emotions and adjust the content of the meditation guide based on those emotions. For example, if the customer is feeling stressed, the meditation guide unit can provide a simple and easy-to-understand meditation guide. If the customer is relaxed, the meditation guide unit can also provide a detailed meditation guide. Furthermore, if the customer is feeling anxious, the meditation guide unit can provide a reassuring meditation guide. By adjusting the content of the meditation guide based on the customer's emotions, a more appropriate meditation guide can be provided. 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 meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input customer emotion data into a generative AI and have the generative AI adjust the content of the meditation guide.

[0101] The meditation guide unit can provide the most suitable guide by referring to the customer's past meditation history when providing a meditation guide. For example, the meditation guide unit can refer to the frequency and type of meditation the customer has performed in the past and provide a meditation guide that suits the customer's preferences. The meditation guide unit can also prioritize providing meditation guides that the customer has previously given high ratings to. Furthermore, the meditation guide unit can analyze the customer's past meditation history and provide a meditation guide that is best suited to the customer's current emotional state. In this way, the optimal meditation guide can be provided by referring to the customer's past meditation history. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or not using AI. For example, the meditation guide unit can input the customer's past meditation history into a generating AI and have the generating AI select an appropriate meditation guide.

[0102] The meditation guide unit can customize the content of the meditation guide based on the customer's current lifestyle when providing it. For example, if the customer is at work, the meditation guide unit can provide a meditation guide to improve concentration. It can also provide a meditation guide with a high relaxation effect if the customer wants to relax. Furthermore, if the customer has just exercised, the meditation guide unit can provide a meditation guide with a high recovery effect. This allows for the provision of more appropriate meditation guides by customizing them based on the customer's lifestyle. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input customer lifestyle data into a generating AI and have the generating AI customize the content of the meditation guide.

[0103] The meditation guide unit can estimate the customer's emotions and adjust the order of the meditation guides based on the estimated emotions. For example, if the customer is feeling stressed, the meditation guide unit can first provide a meditation guide with a high relaxation effect. Similarly, if the customer is relaxed, the meditation guide unit can first provide a meditation guide that enhances concentration. Furthermore, if the customer is feeling anxious, the meditation guide unit can first provide a meditation guide that provides a sense of security. This allows for the provision of more appropriate meditation guides by adjusting the order based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meditation guide unit may be performed using AI, or not. For example, the meditation guide unit can input customer emotion data into a generative AI and have the generative AI adjust the order of the meditation guides.

[0104] The meditation guide unit can provide appropriate guidance by considering the customer's geographical location when providing meditation guidance. For example, if the customer is in a specific region, the meditation guide unit can provide a meditation guide related to that region. The meditation guide unit can also provide region-specific meditation guides based on the customer's geographical location. Furthermore, if the customer is traveling, the meditation guide unit can provide a meditation guide related to their travel destination. In this way, the optimal meditation guide can be provided by considering the customer's geographical location. Some or all of the above processing in the meditation guide unit may be performed using AI, for example, or without AI. For example, the meditation guide unit can input the customer's geographical location information into a generating AI and have the generating AI select an appropriate meditation guide.

[0105] The meditation guide department can analyze a customer's social media activity and provide relevant guides when offering meditation guides. For example, it can analyze a customer's social media posts and provide meditation guides related to the customer's interests. It can also consider the customer's frequency of social media activity and provide meditation guides at an appropriate time. Furthermore, based on the customer's social media ratings, the meditation guide department can prioritize providing meditation guides that the customer has highly rated. In this way, relevant meditation guides can be provided by analyzing the customer's social media activity. Some or all of the above processing in the meditation guide department may be performed using AI, for example, or not. For example, the meditation guide department can input customer social media activity data into a generating AI and have the generating AI select relevant meditation guides.

[0106] The advice-providing unit can estimate the customer's emotions and adjust the content of the advice based on the estimated emotions. For example, if the customer is stressed, the advice-providing unit can provide simple and easy-to-understand advice. If the customer is relaxed, the advice-providing unit can also provide detailed advice. Furthermore, if the customer is anxious, the advice-providing unit can provide reassuring advice. In this way, by adjusting the content of the advice based on the customer's emotions, more appropriate advice can be provided. 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 advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input customer emotion data into a generative AI and have the generative AI adjust the content of the advice.

[0107] The advice-providing unit can provide optimal advice by referring to the customer's past behavioral history when providing advice. For example, the advice-providing unit can refer to the frequency and type of actions the customer has taken in the past and provide advice that matches the customer's preferences. The advice-providing unit can also prioritize providing advice that the customer has previously given a high rating to. Furthermore, the advice-providing unit can analyze the customer's past behavioral history and provide advice that is best suited to the customer's current emotional state. In this way, optimal advice can be provided by referring to the customer's past behavioral history. Some or all of the above processes in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the customer's past behavioral history into a generating AI and have the generating AI select appropriate advice.

[0108] The advice-providing unit can customize the content of advice based on the customer's current living situation when providing advice. For example, if the customer is at work, the advice-providing unit can provide advice to improve concentration. It can also provide advice with a high relaxation effect if the customer wants to relax. Furthermore, if the customer has just exercised, the advice-providing unit can provide advice with a high recovery effect. This allows for the provision of more appropriate advice by customizing it based on the customer's living situation. Some or all of the above-described processes in the advice-providing unit may be performed using AI, for example, or not. For example, the advice-providing unit can input customer living situation data into a generating AI and have the generating AI customize the content of the advice.

[0109] The advice-providing unit can estimate the customer's emotions and prioritize advice based on those emotions. For example, if the customer is stressed, the advice-providing unit will prioritize advice related to stress relief. Similarly, if the customer is relaxed, the advice-providing unit can prioritize advice related to relaxation. Furthermore, if the customer is anxious, the advice-providing unit can prioritize advice related to anxiety relief. This allows for the provision of more appropriate advice by prioritizing it based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice-providing unit may be performed using AI or not. For example, the advice-providing unit can input customer emotion data into a generative AI and have the generative AI determine the priority of advice.

[0110] The advice-providing unit can provide appropriate advice by considering the customer's geographical location when providing advice. For example, if the customer is in a specific region, the advice-providing unit can provide advice relevant to that region. The advice-providing unit can also provide region-specific advice based on the customer's geographical location. Furthermore, if the customer is traveling, the advice-providing unit can provide advice relevant to their travel destination. In this way, by considering the customer's geographical location, the optimal advice can be provided. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the customer's geographical location into a generating AI and have the generating AI select appropriate advice.

[0111] The advice-providing unit can analyze the customer's social media activity and provide relevant advice when providing advice. For example, the advice-providing unit can analyze the customer's social media posts and provide advice related to the customer's interests. The advice-providing unit can also consider the customer's frequency of social media activity and provide advice at an appropriate time. Furthermore, based on the customer's social media ratings, the advice-providing unit can prioritize providing advice that the customer has given a high rating to. In this way, relevant advice can be provided by analyzing the customer's social media activity. Some or all of the above processes in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the customer's social media activity data into a generating AI and have the generating AI select relevant advice. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives data entered by the customer using a smartphone or personal computer. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and includes an image analysis unit, a video analysis unit, and an audio analysis unit. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and includes a music provision unit, a meditation guide unit, and an advice provision unit. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives data entered by the customer using the smart glasses. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and includes an image analysis unit, a video analysis unit, and a voice analysis unit. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and includes a music provision unit, a meditation guide unit, and an advice provision unit. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives data entered by the customer using the headset terminal. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and includes an image analysis unit, a video analysis unit, and a voice analysis unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and includes a music provision unit, a meditation guide unit, and an advice provision unit. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives data entered by the customer using the robot. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and includes an image analysis unit, a video analysis unit, and a voice analysis unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and includes a music provision unit, a meditation guide unit, and an advice provision unit.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The psychological support system can also be equipped with a biofeedback unit. The biofeedback unit acquires the customer's physiological data (e.g., heart rate, skin electrical activity, breathing patterns, etc.) and transmits this data to the analysis unit. The analysis unit can use this physiological data to more accurately detect signs of stress and anxiety in the customer. For example, by analyzing fluctuations in heart rate and changes in skin electrical activity, the customer's stress level can be assessed. Similarly, by analyzing changes in breathing patterns, the customer's state of relaxation can be assessed. This allows the psychological support system to use the customer's physiological data to more accurately assess their mental state and propose improvement measures.

[0114] The psychological support system can also be equipped with a behavioral tracking unit. The behavioral tracking unit acquires data on the customer's daily behavior (e.g., steps taken, exercise level, sleep patterns, etc.) and transmits this data to an analysis unit. The analysis unit can use this behavioral data to detect signs of stress and anxiety in the customer. For example, by analyzing a decrease in exercise level or disruptions in sleep patterns, the customer's stress level can be assessed. Furthermore, by analyzing fluctuations in step count, the customer's activity level can be assessed. This allows the psychological support system to use the customer's daily behavioral data to provide a more accurate assessment of their mental state and suggest improvement measures.

[0115] The psychological support system can also be equipped with an environmental sensor unit. The environmental sensor unit acquires environmental data around the customer (e.g., temperature, humidity, noise level, etc.) and transmits this data to an analysis unit. The analysis unit can use this environmental data to detect signs of stress and anxiety in the customer. For example, by analyzing increases in noise levels or temperature fluctuations, the customer's stress level can be evaluated. Similarly, by analyzing changes in humidity, the customer's comfort level can be evaluated. This allows the psychological support system to use the customer's surrounding environmental data to provide a more accurate assessment of their mental state and suggest improvement measures.

[0116] The psychological support system can also be equipped with a social interaction unit. This unit analyzes the customer's social media activity and message exchanges and transmits this data to the analysis unit. The analysis unit can use this social data to detect signs of stress and anxiety in the customer. For example, by analyzing changes in the frequency and content of social media posts, the customer's mental state can be assessed. Furthermore, by analyzing the content and tone of message exchanges, the customer's emotional state can be assessed. This allows the psychological support system to use the customer's social interaction data to provide a more accurate assessment of their mental state and propose improvement measures.

[0117] The psychological support system can also be equipped with a personalized content delivery unit. This unit provides individually customized content based on the customer's past usage history and preferences. For example, it can provide highly relaxing content based on videos the customer has previously enjoyed watching or music the customer has listened to. It can also customize meditation guides and stress management advice according to the customer's preferences. Furthermore, it can analyze the customer's past usage history and deliver content at the optimal time. This allows the psychological support system to provide more effective mental support by delivering content tailored to the customer's individual needs.

[0118] The psychological support system can also be equipped with an emotion estimation unit. This unit analyzes customer input data (e.g., images, videos, audio, etc.) to estimate the customer's emotions. The analysis unit then uses the estimated emotion data to more accurately detect signs of stress and anxiety in the customer. For example, by analyzing the customer's facial expressions and tone of voice, the system can assess the customer's emotional state. Furthermore, it can propose appropriate solutions based on the customer's emotion data. This allows the psychological support system to use customer emotion data to provide more accurate assessments of their mental state and propose solutions.

[0119] The psychological support system can also be equipped with an emotional feedback unit. This unit provides real-time feedback on the client's emotional state, making it easier for the client to understand their own emotions. For example, if a client is feeling stressed, the system immediately provides this information, enabling the client to take steps to reduce stress. If the client is relaxed, the system can also provide advice to maintain that state. Furthermore, if the client is feeling anxious, the system can provide reassuring feedback. This allows the psychological support system to provide more effective mental support by providing real-time feedback on the client's emotional state.

[0120] The psychological support system can also be equipped with an emotion prediction unit. This unit predicts the customer's future emotional state based on their past data. The analysis unit can then use the predicted emotional data to detect signs of stress and anxiety in the customer in advance. For example, by analyzing the customer's past stress patterns, it can predict future stress peaks and take preventative measures. It can also predict future emotional fluctuations based on the customer's past emotional data and suggest improvement measures at the appropriate time. This allows the psychological support system to provide more effective mental support by predicting the customer's future emotional state.

[0121] The psychological support system can also include an emotional history unit. This unit stores the customer's past emotional data, which the analysis unit then uses to analyze the customer's emotional patterns. For example, by analyzing the customer's past emotional fluctuations, it's possible to identify triggers for the customer's stress and anxiety. Furthermore, the system can assess long-term mental health trends based on the customer's emotional history. It can also use the customer's emotional history to propose individually customized improvement measures. This allows the psychological support system to use the customer's past emotional data to provide more accurate assessments of their mental state and propose improvement measures.

[0122] The psychological support system can also include an emotion-sharing section. This section allows customers to share their emotional state with other users. For example, if a customer is feeling stressed, they can share this information with other users and receive empathy and support. Similarly, if a customer is relaxed, they can share this state with others and receive positive influence. Furthermore, if a customer is feeling anxious, they can receive encouragement and advice from other users. This allows the psychological support system to provide more effective mental support by enabling customers to share their emotional state with others.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives data entered by the customer. This data may include, for example, text data, image data, and audio data. The reception desk can accept data entered by the customer using a smartphone or computer. It can also accept data entered by the customer directly. Step 2: The analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety. The analysis unit comprises an image analysis unit that analyzes image data, a video analysis unit that analyzes video data, and a voice analysis unit that analyzes audio data. The image analysis unit analyzes subtle changes in the customer's facial expressions and detects signs of stress or anxiety. The video analysis unit analyzes changes in the customer's movements and posture and detects signs of stress or anxiety. The voice analysis unit analyzes changes in the tone and speed of the customer's voice and detects signs of stress or anxiety. Step 3: The proposal unit proposes improvement measures based on the analysis results obtained by the analysis unit. The proposal unit comprises a music provision unit that provides music for relaxation, a meditation guide unit that provides guidance for meditation, and an advice provision unit that provides advice for stress management. The music provision unit provides music with a high relaxation effect according to the customer's stress level. The meditation guide unit provides guidance for meditation according to the customer's mental state. The advice provision unit provides specific advice for the customer's stress management.

[0125] 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.

[0126] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0127] 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.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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. 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.

[0143] 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.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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. 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.

[0159] 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.

[0160] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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. 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.

[0176] 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.

[0177] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 reception area that receives data entered by customers, An analysis unit analyzes the data received by the reception unit and detects signs of stress or anxiety, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes improvement measures, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, It includes an image analysis unit for analyzing image data. The system according to feature 1.

3. The aforementioned analysis unit, It includes a video analysis unit that analyzes video data. The system according to feature 1.

4. The aforementioned analysis unit, It is equipped with a voice analysis unit that analyzes voice data. The system according to feature 1.

5. The aforementioned proposal section is, It features a music service area that provides music for relaxation. The system according to feature 1.

6. The aforementioned proposal section is, It includes a meditation guide section that provides guidance on meditation. The system according to feature 1.

7. The aforementioned proposal section is, Equipped with an advice department that provides advice for stress management. The system according to feature 1.

8. The aforementioned reception unit is We estimate customer emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the customer's past data submission history to select the appropriate submission method. The system according to feature 1.

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