system

Abstract

The system according to the embodiment aims to provide optimal cognitive behavioral therapy based on the user's specific cognitive patterns and behaviors. [Solution] A system according to an embodiment includes a voice analysis unit, a cognitive behavioral therapy providing unit, and a gamification unit. The voice analysis unit analyzes a user's voice. The cognitive behavioral therapy providing unit provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit. The gamification unit manages the progress provided by the cognitive behavioral therapy providing unit and provides a reward system.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques provide cognitive behavioral therapy in a uniform manner, leaving room for improvement in providing optimal therapy based on a user's specific cognitive patterns and behaviors.

[0005] The system according to the embodiment aims to provide optimal cognitive behavioral therapy based on the user's specific cognitive patterns and behaviors. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice analysis unit, a cognitive behavioral therapy providing unit, and a gamification unit. The voice analysis unit analyzes a user's voice. The cognitive behavioral therapy providing unit provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit. The gamification unit manages the progress provided by the cognitive behavioral therapy providing unit and provides a reward system. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal cognitive behavioral therapy based on the user's specific cognitive patterns and behaviors. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A counseling system according to an embodiment of the present invention analyzes a user's voice, provides cognitive behavioral therapy, manages progress, and provides a reward system. The counseling system analyzes the user's voice, provides cognitive behavioral therapy, manages progress, and provides a reward system, thereby facilitating continued use by the user. For example, when a user expresses their complaints or worries through a voice dialogue, an AI analyzes the content and selects an appropriate cognitive behavioral therapy technique. For example, if a user says, "My work isn't going well," the AI ​​analyzes the statement and identifies cognitive distortions. The AI ​​then provides specific advice and questions to correct the distortions. For example, the AI ​​may ask questions such as, "What specifically do you feel isn't going well?" to help the user organize their thoughts. Furthermore, the counseling system incorporates gamification elements to encourage users to continue using the service. For example, users are awarded points upon achieving certain goals, which can be used to purchase virtual items. A dashboard is also provided to visualize progress, allowing users to check their own progress. This allows the counseling system to provide cognitive behavioral therapy optimized based on each user's specific cognitive patterns and behaviors. For example, if a user has had a particular cognitive distortion in the past, the AI ​​will select the optimal approach based on that information. This allows the user to receive more effective counseling. This allows the counseling system to provide cognitive behavioral therapy in a natural dialogue while listening to the user's complaints and worries.

[0029] A counseling system according to an embodiment includes a voice analysis unit, a cognitive behavioral therapy providing unit, and a gamification unit. The voice analysis unit analyzes a user's voice. The user's voice may include, for example, complaints or worries, but is not limited to such examples. The voice analysis unit converts the user's speech into text data using, for example, voice recognition technology. The voice analysis unit can also estimate the user's emotions using emotion analysis technology. For example, the voice analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The voice analysis unit can also analyze the content of the user's speech and extract specific keywords. For example, the voice analysis unit extracts a keyword such as "My work is not going well" from the user's speech and identifies cognitive distortions. The cognitive behavioral therapy providing unit provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit. Cognitive behavioral therapy is performed, for example, by providing specific advice and questions, but is not limited to such examples. For example, the cognitive behavioral therapy providing unit poses a question to the user such as, "What specifically do you feel is not going well?" The cognitive behavioral therapy providing unit may also provide specific advice to correct the user's cognitive distortions. For example, the cognitive behavioral therapy providing unit may provide the user with advice such as, "What can I do to change that way of thinking?" The cognitive behavioral therapy providing unit may also select an optimal approach based on the user's specific cognitive patterns or behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit may select an optimal approach based on that information. The gamification unit manages the progress provided by the cognitive behavioral therapy providing unit and provides a reward system. The progress may be managed, for example, by recording the goals and progress achieved by the user, but is not limited to such examples. For example, the gamification unit may award points to the user when the user achieves a certain goal. The gamification unit may also enable the user to purchase virtual items using the points. For example, the gamification unit may provide an interface for the user to purchase virtual items using the points.Furthermore, the gamification unit may provide a dashboard for visualizing progress. For example, the gamification unit may display progress as graphs or charts so that the user can check their progress. This allows the counseling system according to the embodiment to analyze the user's voice, provide cognitive behavioral therapy, manage progress, and provide a reward system. This makes it easier for the user to continue using the system.

[0030] The voice analysis unit can identify the content of a user's complaints or problems. The voice analysis unit, for example, converts the user's speech into text data using voice recognition technology. For example, the voice analysis unit extracts specific keywords from the user's speech and identifies the content of the complaints or problems. The voice analysis unit can also estimate the user's emotions using emotion analysis technology. For example, the voice analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The voice analysis unit can also analyze the content of the user's speech and extract specific keywords. For example, the voice analysis unit extracts keywords such as "My work isn't going well" from the user's speech and identifies the content of the complaints or problems. This allows the user's complaints or worries to be identified, thereby providing appropriate cognitive behavioral therapy. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the voice analysis unit can input the user's speech into a generation AI and have the generation AI analyze the content of the speech.

[0031] The cognitive behavioral therapy providing unit can provide specific advice or questions based on the identified content. For example, the cognitive behavioral therapy providing unit asks the user a question such as, "What specifically do you feel is not going well?" For example, the cognitive behavioral therapy providing unit provides the user with advice such as, "What can I do to change that way of thinking?" The cognitive behavioral therapy providing unit can also provide specific advice for correcting the user's cognitive distortions. For example, the cognitive behavioral therapy providing unit provides the user with advice such as, "What can I do to change that way of thinking?" The cognitive behavioral therapy providing unit can also select an optimal approach based on the user's specific cognitive patterns or behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects an optimal approach based on that information. In this way, the cognitive distortions of the user can be corrected by providing specific advice or questions based on the identified content. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed, for example, using AI or without AI. For example, the cognitive behavioral therapy provider can input the user's statements into the generation AI and have the generation AI generate specific advice and questions.

[0032] The gamification unit can award points to a user when the user achieves a specific goal and allow the user to use the points to purchase virtual items. The gamification unit, for example, awards points to a user when the user achieves a certain goal. For example, the gamification unit provides an interface for the user to purchase virtual items using the points. The gamification unit can also enable the user to purchase virtual items using the points. For example, the gamification unit provides an interface for the user to purchase virtual items using the points. This allows the user to maintain their motivation by awarding points to a user when the user achieves a certain goal and allowing the user to use the points to purchase virtual items. Some or all of the above-described processing in the gamification unit may be performed using, for example, AI or without AI. For example, the gamification unit can input the user's progress to a generation AI and cause the generation AI to award points and purchase virtual items.

[0033] The gamification unit can provide a dashboard for visualizing the progress. For example, the gamification unit displays the progress as a graph or chart so that the user can check their own progress. For example, the gamification unit displays the progress as a graph or chart so that the user can check their own progress. The gamification unit can also provide a dashboard for visualizing the progress. For example, the gamification unit displays the progress as a graph or chart so that the user can check their own progress. By providing a dashboard for visualizing the progress, the user can check their own progress. Some or all of the above-described processing in the gamification unit may be performed using AI, or may be performed without using AI, for example. For example, the gamification unit can input the user's progress to a generation AI and cause the generation AI to generate a dashboard.

[0034] The cognitive behavioral therapy providing unit can select an appropriate approach based on the user's specific cognitive patterns and behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects the optimal approach based on that information. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects the optimal approach based on that information. The cognitive behavioral therapy providing unit can also select the optimal approach based on the user's specific cognitive patterns and behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects the optimal approach based on that information. This allows for more effective counseling by selecting the optimal approach based on the user's specific cognitive patterns and behaviors. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, or without, AI. For example, the cognitive behavioral therapy providing unit can input the user's cognitive patterns and behavioral data into a generation AI and have the generation AI select the optimal approach.

[0035] During voice analysis, the voice analysis unit can optimize the analysis algorithm by referring to the user's past speech history. For example, the voice analysis unit uses AI to adjust the analysis algorithm based on phrases frequently used by the user in the past. For example, the voice analysis unit improves the analysis accuracy for a specific topic based on the user's past speech history. The voice analysis unit can also analyze the user's past speech patterns and use AI to select the optimal analysis algorithm. For example, the voice analysis unit optimizes the analysis algorithm by referring to the user's past speech history. By referencing the user's past speech history, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above-described processing in the voice analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the voice analysis unit can input the user's past speech history data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0036] During voice analysis, the voice analysis unit can improve the accuracy of analysis based on the user's speaking speed and tone. For example, if the user speaks quickly, the voice analysis unit uses AI to increase the analysis speed to perform accurate analysis. For example, if the user speaks slowly, the voice analysis unit uses AI to adjust the analysis speed and perform a more detailed analysis. Furthermore, if the user's tone changes, the AI ​​can capture the change and improve the analysis accuracy. For example, the voice analysis unit improves the analysis accuracy by taking into account the user's speaking speed and tone. In this way, the analysis accuracy can be improved by taking into account the user's speaking speed and tone. Some or all of the above-described processing in the voice analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the voice analysis unit can input the user's speaking speed and tone data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0037] The audio analysis unit can filter the user's background sound during audio analysis to improve the accuracy of the analysis. For example, when the user is in a noisy environment, the audio analysis unit uses AI to filter the background sound and analyze the audio. For example, when the user is in a quiet environment, the audio analysis unit uses AI to minimize the background sound during analysis. The audio analysis unit can also filter the user's surrounding sounds in real time to improve the accuracy of the analysis. For example, the audio analysis unit filters the user's background sound to improve the accuracy of the analysis. In this way, filtering the user's background sound can improve the accuracy of the analysis. Some or all of the above-described processing in the audio analysis unit may be performed using AI, or may be performed without using AI. For example, the audio analysis unit can input the user's background sound data to a generation AI and have the generation AI filter the background sound.

[0038] The speech analysis unit can perform speech analysis while taking into account the user's geographical and cultural background. For example, if the user lives in a specific region, the speech analysis unit performs analysis while taking into account the dialect and expressions of that region. For example, the speech analysis unit takes into account the user's cultural background to accurately analyze utterances related to a specific culture. The speech analysis unit can also reflect regional expressions in its analysis based on the user's geographical background. For example, the speech analysis unit performs analysis while taking into account the user's geographical and cultural background. This enables more accurate analysis by taking into account the user's geographical and cultural background. Some or all of the above-described processing in the speech analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech analysis unit can input the user's geographical and cultural background data into a generation AI and have the generation AI perform the analysis.

[0039] The voice analysis unit can analyze the user's social media activities and complement related information during voice analysis. The voice analysis unit, for example, analyzes the user's social media posts and complements information related to the content of the comments. For example, the voice analysis unit analyzes the content of the comments taking into account the user's friendships on social media. The voice analysis unit can also complement the content of the comments based on the user's social media activity history. For example, the voice analysis unit analyzes the user's social media activities and complements related information. In this way, by analyzing the user's social media activities, it is possible to complement related information and improve analysis accuracy. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's social media activity data into a generation AI and have the generation AI complement related information.

[0040] During voice analysis, the voice analysis unit can customize the analysis method by reflecting the user's past feedback. The voice analysis unit adjusts the analysis method based on, for example, feedback provided by the user in the past. For example, the voice analysis unit reflects the user's past feedback to improve analysis accuracy. The voice analysis unit can also customize the analysis algorithm based on the user's feedback. For example, the voice analysis unit customizes the analysis method by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the analysis method can be customized and analysis accuracy can be improved. Some or all of the above-described processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0041] When providing cognitive behavioral therapy, the cognitive behavioral therapy providing unit can select an optimal approach by referring to the user's past counseling history. The cognitive behavioral therapy providing unit selects an optimal approach based on, for example, the user's past counseling history. For example, the cognitive behavioral therapy providing unit may provide the user with an approach that was effective in the past again. The cognitive behavioral therapy providing unit can also analyze the user's past counseling history and propose a new approach. For example, the cognitive behavioral therapy providing unit selects an optimal approach by referring to the user's past counseling history. In this way, by referring to the user's past counseling history, an optimal approach can be selected and the effectiveness of counseling can be improved. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit may input the user's past counseling history data into a generation AI and cause the generation AI to select an optimal approach.

[0042] The cognitive behavioral therapy providing unit can provide advice based on the user's current living situation and stress level when providing cognitive behavioral therapy. For example, if the user has a high stress level, the cognitive behavioral therapy providing unit uses AI to suggest relaxation methods. For example, the cognitive behavioral therapy providing unit provides specific advice taking into account the user's current living situation. The cognitive behavioral therapy providing unit can also select an appropriate approach based on the user's stress level. For example, the cognitive behavioral therapy providing unit provides advice taking into account the user's current living situation and stress level. This makes it possible to provide more appropriate advice by taking into account the user's current living situation and stress level. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using AI, for example, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's living situation and stress level data into the generation AI and have the generation AI provide advice.

[0043] The cognitive behavioral therapy providing unit can improve the approach by reflecting user feedback when providing cognitive behavioral therapy. The cognitive behavioral therapy providing unit improves the approach based on, for example, feedback provided by the user. For example, the cognitive behavioral therapy providing unit reflects user feedback and adjusts the content of advice. The cognitive behavioral therapy providing unit can also propose a new approach based on user feedback. For example, the cognitive behavioral therapy providing unit improves the approach by reflecting user feedback. In this way, by reflecting user feedback, the approach can be improved and the effectiveness of counseling can be enhanced. Some or all of the above-mentioned processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input user feedback data into a generation AI and cause the generation AI to improve the approach.

[0044] The cognitive behavioral therapy providing unit can provide advice taking into account the geographical and cultural background of the user when providing cognitive behavioral therapy. For example, the cognitive behavioral therapy providing unit provides region-specific advice taking into account the geographical background of the user. For example, the cognitive behavioral therapy providing unit provides culturally appropriate advice taking into account the cultural background of the user. The cognitive behavioral therapy providing unit can also provide advice for region-specific problems based on the geographical background of the user. For example, the cognitive behavioral therapy providing unit provides advice taking into account the geographical and cultural background of the user. This makes it possible to provide more appropriate advice by taking into account the geographical and cultural background of the user. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, or without, AI, for example. For example, the cognitive behavioral therapy providing unit can input the user's geographical and cultural background data into a generation AI and cause the generation AI to provide advice.

[0045] The cognitive behavioral therapy providing unit can analyze the user's social media activities and provide relevant advice when providing cognitive behavioral therapy. The cognitive behavioral therapy providing unit, for example, analyzes the user's social media posts and provides relevant advice. For example, the cognitive behavioral therapy providing unit provides advice taking into account the user's social media friendships. The cognitive behavioral therapy providing unit can also provide advice based on the user's social media activity history. For example, the cognitive behavioral therapy providing unit analyzes the user's social media activities and provides relevant advice. In this way, by analyzing the user's social media activities, relevant advice can be provided and the effectiveness of counseling can be improved. Some or all of the above-mentioned processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant advice.

[0046] The cognitive behavioral therapy providing unit can customize the approach by reflecting the user's past feedback when providing cognitive behavioral therapy. The cognitive behavioral therapy providing unit customizes the approach based on, for example, feedback provided by the user in the past. For example, the cognitive behavioral therapy providing unit reflects the user's past feedback and adjusts the content of advice. The cognitive behavioral therapy providing unit can also propose a new approach based on the user's feedback. For example, the cognitive behavioral therapy providing unit customizes the approach by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the approach can be customized and the effectiveness of counseling can be improved. Some or all of the above-mentioned processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the approach.

[0047] The gamification unit can optimize the reward system by referring to the user's past achievement history when managing progress. The gamification unit, for example, optimizes the reward system based on the user's past achievement history. For example, the gamification unit provides new rewards based on goals the user has achieved in the past. The gamification unit can also analyze the user's past achievement history and propose an optimal reward system. For example, the gamification unit optimizes the reward system by referring to the user's past achievement history. In this way, by referring to the user's past achievement history, the reward system can be optimized and the user's motivation can be maintained. Some or all of the above-described processing in the gamification unit may be performed using, for example, AI, or may be performed without using AI. For example, the gamification unit can input the user's past achievement history data into a generation AI and cause the generation AI to optimize the reward system.

[0048] The gamification unit can provide rewards based on the user's current motivation level during progress management. For example, the gamification unit provides additional rewards when the user's motivation level is high. For example, the gamification unit provides rewards along with encouraging messages when the user's motivation level is low. The gamification unit can also provide optimal rewards based on the user's motivation level. For example, the gamification unit provides rewards based on the user's current motivation level. This allows the user's current motivation level to be taken into consideration, thereby providing appropriate rewards and maintaining motivation. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's motivation level data into a generation AI and cause the generation AI to provide rewards.

[0049] The gamification unit can improve the reward system by reflecting user feedback during progress management. The gamification unit improves the reward system, for example, based on feedback provided by the user. For example, the gamification unit reflects user feedback and adds new rewards. The gamification unit can also adjust the balance of the reward system based on user feedback. For example, the gamification unit improves the reward system by reflecting user feedback. In this way, by reflecting user feedback, the reward system can be improved and the user's motivation can be maintained. Some or all of the above-mentioned processing in the gamification unit may be performed using, for example, AI, or may be performed without using AI. For example, the gamification unit can input user feedback data into a generation AI and cause the generation AI to improve the reward system.

[0050] The gamification unit can provide rewards by taking into account the user's geographical and cultural backgrounds when managing the progress. For example, the gamification unit can provide region-specific rewards by taking into account the user's geographical background. For example, the gamification unit can provide culturally appropriate rewards by taking into account the user's cultural background. The gamification unit can also provide region-specific rewards based on the user's geographical background. For example, the gamification unit can provide rewards by taking into account the user's geographical and cultural backgrounds. This allows the user to be provided with appropriate rewards and maintain motivation by taking into account the user's geographical and cultural backgrounds. Some or all of the above-described processing in the gamification unit can be performed using, or without, AI. For example, the gamification unit can input the user's geographical and cultural background data into a generation AI and cause the generation AI to provide rewards.

[0051] The gamification unit can analyze the user's social media activities and provide related rewards during progress management. For example, the gamification unit can analyze the user's social media posts and provide related rewards. For example, the gamification unit can provide rewards taking into account the user's social media friendships. The gamification unit can also provide rewards based on the user's social media activity history. For example, the gamification unit can analyze the user's social media activities and provide related rewards. In this way, by analyzing the user's social media activities, it is possible to provide related rewards and maintain motivation. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related rewards.

[0052] The gamification unit can customize the reward system by reflecting the user's past feedback when managing the progress. The gamification unit customizes the reward system based on, for example, feedback provided by the user in the past. For example, the gamification unit adds new rewards by reflecting the user's past feedback. The gamification unit can also adjust the balance of the reward system based on the user's feedback. For example, the gamification unit customizes the reward system by reflecting the user's past feedback. In this way, the reward system can be customized by reflecting the user's past feedback, thereby maintaining motivation. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the reward system.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The counseling system may further include a behavioral data acquisition unit that collects user behavioral data. The behavioral data acquisition unit may, for example, collect the user's smartphone usage patterns and location information and analyze the user's daily behavioral patterns. For example, if the user tends to feel stressed when in a particular place, the cognitive behavioral therapy provision unit may provide advice on avoiding that place. Also, if the user feels stressed during a particular time of day, the cognitive behavioral therapy provision unit may suggest ways to relax during that time. Furthermore, the behavioral data acquisition unit may monitor the user's behavioral patterns over the long term and identify stress triggers. This allows the counseling system to provide a more effective approach based on the user's behavioral data.

[0055] The counseling system may further include a dietary data acquisition unit that collects dietary data of the user. The dietary data acquisition unit may, for example, record the content and time of meals eaten by the user and analyze nutritional balance. For example, if the user feels stressed after eating a particular meal, the cognitive behavioral therapy provision unit may provide advice to avoid that meal. Also, if the user's consumption of a nutritionally balanced meal reduces stress, the cognitive behavioral therapy provision unit may recommend that meal. Furthermore, the dietary data acquisition unit may monitor the user's eating patterns over the long term and analyze the relationship between diet and stress. This allows the counseling system to provide a more personalized approach based on the user's dietary data.

[0056] The counseling system may further include a sleep data acquisition unit that collects the user's sleep data. The sleep data acquisition unit may, for example, record the user's sleep time and sleep quality and analyze the sleep pattern. For example, if the user is not getting enough sleep, the cognitive behavioral therapy provision unit may provide advice on improving the quality of sleep. Also, if the user is getting deep sleep during a specific time period, the cognitive behavioral therapy provision unit may suggest a relaxation method tailored to that time period. Furthermore, the sleep data acquisition unit may monitor the user's sleep pattern over the long term and analyze the correlation with stress. This allows the counseling system to provide a more effective approach based on the user's sleep data.

[0057] The counseling system may further include an exercise data acquisition unit that collects the user's exercise data. The exercise data acquisition unit may, for example, record the amount and type of exercise the user does and analyze the exercise patterns. For example, if the user feels stressed due to lack of exercise, the cognitive behavioral therapy provision unit may recommend appropriate exercise. Also, if the user finds that performing a specific exercise reduces stress, the cognitive behavioral therapy provision unit may provide advice for continuing that exercise. Furthermore, the exercise data acquisition unit may monitor the user's exercise patterns over the long term and analyze the relationship between exercise and stress. This allows the counseling system to provide a more personalized approach based on the user's exercise data.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The voice analysis unit analyzes the user's voice. The user's voice may include, for example, complaints or worries, but is not limited to such examples. The voice analysis unit converts the user's speech into text data using, for example, voice recognition technology. The voice analysis unit can also estimate the user's emotions using emotion analysis technology. For example, the voice analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The voice analysis unit can also analyze the content of the user's speech and extract specific keywords. For example, the voice analysis unit extracts keywords such as "My work is not going well" from the user's speech and identifies cognitive distortions. Step 2: The cognitive behavioral therapy providing unit provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit. Cognitive behavioral therapy is performed, for example, by providing specific advice and questions, but is not limited to such examples. For example, the cognitive behavioral therapy providing unit asks the user a question such as, "What specifically do you feel is not going well?" The cognitive behavioral therapy providing unit can also provide specific advice to correct the user's cognitive distortions. For example, the cognitive behavioral therapy providing unit provides the user with advice such as, "What can I do to change that way of thinking?" Furthermore, the cognitive behavioral therapy providing unit can select an optimal approach based on the user's specific cognitive patterns and behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects an optimal approach based on that information. Step 3: The gamification unit manages the progress provided by the cognitive behavioral therapy provider and provides a reward system. Progress is managed, for example, by recording the goals and progress achieved by the user, but is not limited to this example. For example, the gamification unit awards points when the user achieves a certain goal. The gamification unit may also enable the user to purchase virtual items using the points. For example, the gamification unit may provide an interface for the user to purchase virtual items using the points. Furthermore, the gamification unit may provide a dashboard for visualizing the progress. For example, the gamification unit may display the progress as a graph or chart so that the user can check their progress.

[0060] (Example 2) A counseling system according to an embodiment of the present invention analyzes a user's voice, provides cognitive behavioral therapy, manages progress, and provides a reward system. The counseling system analyzes the user's voice, provides cognitive behavioral therapy, manages progress, and provides a reward system, thereby facilitating continued use by the user. For example, when a user expresses their complaints or worries through a voice dialogue, an AI analyzes the content and selects an appropriate cognitive behavioral therapy technique. For example, if a user says, "My work isn't going well," the AI ​​analyzes the statement and identifies cognitive distortions. The AI ​​then provides specific advice and questions to correct the distortions. For example, the AI ​​may ask questions such as, "What specifically do you feel isn't going well?" to help the user organize their thoughts. Furthermore, the counseling system incorporates gamification elements to encourage users to continue using the service. For example, users are awarded points upon achieving certain goals, which can be used to purchase virtual items. A dashboard is also provided to visualize progress, allowing users to check their own progress. This allows the counseling system to provide cognitive behavioral therapy optimized based on each user's specific cognitive patterns and behaviors. For example, if a user has had a particular cognitive distortion in the past, the AI ​​will select the optimal approach based on that information. This allows the user to receive more effective counseling. This allows the counseling system to provide cognitive behavioral therapy in a natural dialogue while listening to the user's complaints and worries.

[0061] A counseling system according to an embodiment includes a voice analysis unit, a cognitive behavioral therapy providing unit, and a gamification unit. The voice analysis unit analyzes a user's voice. The user's voice may include, for example, complaints or worries, but is not limited to such examples. The voice analysis unit converts the user's speech into text data using, for example, voice recognition technology. The voice analysis unit can also estimate the user's emotions using emotion analysis technology. For example, the voice analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The voice analysis unit can also analyze the content of the user's speech and extract specific keywords. For example, the voice analysis unit extracts a keyword such as "My work is not going well" from the user's speech and identifies cognitive distortions. The cognitive behavioral therapy providing unit provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit. Cognitive behavioral therapy is performed, for example, by providing specific advice and questions, but is not limited to such examples. For example, the cognitive behavioral therapy providing unit poses a question to the user such as, "What specifically do you feel is not going well?" The cognitive behavioral therapy providing unit may also provide specific advice to correct the user's cognitive distortions. For example, the cognitive behavioral therapy providing unit may provide the user with advice such as, "What can I do to change that way of thinking?" The cognitive behavioral therapy providing unit may also select an optimal approach based on the user's specific cognitive patterns or behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit may select an optimal approach based on that information. The gamification unit manages the progress provided by the cognitive behavioral therapy providing unit and provides a reward system. The progress may be managed, for example, by recording the goals and progress achieved by the user, but is not limited to such examples. For example, the gamification unit may award points to the user when the user achieves a certain goal. The gamification unit may also enable the user to purchase virtual items using the points. For example, the gamification unit may provide an interface for the user to purchase virtual items using the points.Furthermore, the gamification unit may provide a dashboard for visualizing progress. For example, the gamification unit may display progress as graphs or charts so that the user can check their progress. This allows the counseling system according to the embodiment to analyze the user's voice, provide cognitive behavioral therapy, manage progress, and provide a reward system. This makes it easier for the user to continue using the system.

[0062] The voice analysis unit can identify the content of a user's complaints or problems. The voice analysis unit, for example, converts the user's speech into text data using voice recognition technology. For example, the voice analysis unit extracts specific keywords from the user's speech and identifies the content of the complaints or problems. The voice analysis unit can also estimate the user's emotions using emotion analysis technology. For example, the voice analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The voice analysis unit can also analyze the content of the user's speech and extract specific keywords. For example, the voice analysis unit extracts keywords such as "My work isn't going well" from the user's speech and identifies the content of the complaints or problems. This allows the user's complaints or worries to be identified, thereby providing appropriate cognitive behavioral therapy. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the voice analysis unit can input the user's speech into a generation AI and have the generation AI analyze the content of the speech.

[0063] The cognitive behavioral therapy providing unit can provide specific advice or questions based on the identified content. For example, the cognitive behavioral therapy providing unit asks the user a question such as, "What specifically do you feel is not going well?" For example, the cognitive behavioral therapy providing unit provides the user with advice such as, "What can I do to change that way of thinking?" The cognitive behavioral therapy providing unit can also provide specific advice for correcting the user's cognitive distortions. For example, the cognitive behavioral therapy providing unit provides the user with advice such as, "What can I do to change that way of thinking?" The cognitive behavioral therapy providing unit can also select an optimal approach based on the user's specific cognitive patterns or behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects an optimal approach based on that information. In this way, the cognitive distortions of the user can be corrected by providing specific advice or questions based on the identified content. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed, for example, using AI or without AI. For example, the cognitive behavioral therapy provider can input the user's statements into the generation AI and have the generation AI generate specific advice and questions.

[0064] The gamification unit can award points to a user when the user achieves a specific goal and allow the user to use the points to purchase virtual items. The gamification unit, for example, awards points to a user when the user achieves a certain goal. For example, the gamification unit provides an interface for the user to purchase virtual items using the points. The gamification unit can also enable the user to purchase virtual items using the points. For example, the gamification unit provides an interface for the user to purchase virtual items using the points. This allows the user to maintain their motivation by awarding points to a user when the user achieves a certain goal and allowing the user to use the points to purchase virtual items. Some or all of the above-described processing in the gamification unit may be performed using, for example, AI or without AI. For example, the gamification unit can input the user's progress to a generation AI and cause the generation AI to award points and purchase virtual items.

[0065] The gamification unit can provide a dashboard for visualizing the progress. For example, the gamification unit displays the progress as a graph or chart so that the user can check their own progress. For example, the gamification unit displays the progress as a graph or chart so that the user can check their own progress. The gamification unit can also provide a dashboard for visualizing the progress. For example, the gamification unit displays the progress as a graph or chart so that the user can check their own progress. By providing a dashboard for visualizing the progress, the user can check their own progress. Some or all of the above-described processing in the gamification unit may be performed using AI, or may be performed without using AI, for example. For example, the gamification unit can input the user's progress to a generation AI and cause the generation AI to generate a dashboard.

[0066] The cognitive behavioral therapy providing unit can select an appropriate approach based on the user's specific cognitive patterns and behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects the optimal approach based on that information. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects the optimal approach based on that information. The cognitive behavioral therapy providing unit can also select the optimal approach based on the user's specific cognitive patterns and behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects the optimal approach based on that information. This allows for more effective counseling by selecting the optimal approach based on the user's specific cognitive patterns and behaviors. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, or without, AI. For example, the cognitive behavioral therapy providing unit can input the user's cognitive patterns and behavioral data into a generation AI and have the generation AI select the optimal approach.

[0067] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​in the voice analysis unit increases the accuracy of the voice analysis to capture subtle changes in emotions. For example, if the user is relaxed, the AI ​​in the voice analysis unit returns the accuracy of the voice analysis to normal and maintains natural conversation. Furthermore, if the user is excited, the AI ​​in the voice analysis unit can adjust the accuracy of the voice analysis to accurately capture emotional fluctuations. For example, the voice analysis unit estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. This enables more accurate analysis by adjusting the accuracy of the voice analysis based on the user's emotions. Some or all of the above-described processing in the voice analysis unit may be performed using, or without, AI. For example, the voice analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the accuracy of the voice analysis.

[0068] During voice analysis, the voice analysis unit can optimize the analysis algorithm by referring to the user's past speech history. For example, the voice analysis unit uses AI to adjust the analysis algorithm based on phrases frequently used by the user in the past. For example, the voice analysis unit improves the analysis accuracy for a specific topic based on the user's past speech history. The voice analysis unit can also analyze the user's past speech patterns and use AI to select the optimal analysis algorithm. For example, the voice analysis unit optimizes the analysis algorithm by referring to the user's past speech history. By referencing the user's past speech history, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above-described processing in the voice analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the voice analysis unit can input the user's past speech history data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0069] During voice analysis, the voice analysis unit can improve the accuracy of analysis based on the user's speaking speed and tone. For example, if the user speaks quickly, the voice analysis unit uses AI to increase the analysis speed to perform accurate analysis. For example, if the user speaks slowly, the voice analysis unit uses AI to adjust the analysis speed and perform a more detailed analysis. Furthermore, if the user's tone changes, the AI ​​can capture the change and improve the analysis accuracy. For example, the voice analysis unit improves the analysis accuracy by taking into account the user's speaking speed and tone. In this way, the analysis accuracy can be improved by taking into account the user's speaking speed and tone. Some or all of the above-described processing in the voice analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the voice analysis unit can input the user's speaking speed and tone data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0070] The audio analysis unit can filter the user's background sound during audio analysis to improve the accuracy of the analysis. For example, when the user is in a noisy environment, the audio analysis unit uses AI to filter the background sound and analyze the audio. For example, when the user is in a quiet environment, the audio analysis unit uses AI to minimize the background sound during analysis. The audio analysis unit can also filter the user's surrounding sounds in real time to improve the accuracy of the analysis. For example, the audio analysis unit filters the user's background sound to improve the accuracy of the analysis. In this way, filtering the user's background sound can improve the accuracy of the analysis. Some or all of the above-described processing in the audio analysis unit may be performed using AI, or may be performed without using AI. For example, the audio analysis unit can input the user's background sound data to a generation AI and have the generation AI filter the background sound.

[0071] The voice analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is sad, the voice analysis unit causes the AI ​​to prioritize analysis results related to the emotion. For example, if the user is angry, the voice analysis unit causes the AI ​​to prioritize analysis results related to that emotion. Also, if the user is happy, the voice analysis unit can cause the AI ​​to prioritize analysis results related to that emotion. For example, the voice analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated emotions. This allows important information to be provided preferentially by prioritizing analysis results based on the user's emotions. Some or all of the above-described processing in the voice analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the voice analysis unit can input the user's emotion data into a generation AI and have the generation AI prioritize the analysis results.

[0072] The speech analysis unit can perform speech analysis while taking into account the user's geographical and cultural background. For example, if the user lives in a specific region, the speech analysis unit performs analysis while taking into account the dialect and expressions of that region. For example, the speech analysis unit takes into account the user's cultural background to accurately analyze utterances related to a specific culture. The speech analysis unit can also reflect regional expressions in its analysis based on the user's geographical background. For example, the speech analysis unit performs analysis while taking into account the user's geographical and cultural background. This enables more accurate analysis by taking into account the user's geographical and cultural background. Some or all of the above-described processing in the speech analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech analysis unit can input the user's geographical and cultural background data into a generation AI and have the generation AI perform the analysis.

[0073] The voice analysis unit can analyze the user's social media activities and complement related information during voice analysis. The voice analysis unit, for example, analyzes the user's social media posts and complements information related to the content of the comments. For example, the voice analysis unit analyzes the content of the comments taking into account the user's friendships on social media. The voice analysis unit can also complement the content of the comments based on the user's social media activity history. For example, the voice analysis unit analyzes the user's social media activities and complements related information. In this way, by analyzing the user's social media activities, it is possible to complement related information and improve analysis accuracy. Some or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's social media activity data into a generation AI and have the generation AI complement related information.

[0074] During voice analysis, the voice analysis unit can customize the analysis method by reflecting the user's past feedback. The voice analysis unit adjusts the analysis method based on, for example, feedback provided by the user in the past. For example, the voice analysis unit reflects the user's past feedback to improve analysis accuracy. The voice analysis unit can also customize the analysis algorithm based on the user's feedback. For example, the voice analysis unit customizes the analysis method by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the analysis method can be customized and analysis accuracy can be improved. Some or all of the above-described processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0075] The cognitive behavioral therapy providing unit can estimate the user's emotions and adjust the content of advice and questions based on the estimated user's emotions. For example, if the user is sad, the AI ​​in the cognitive behavioral therapy providing unit provides advice in a gentle tone. For example, if the user is angry, the AI ​​in the cognitive behavioral therapy providing unit asks questions in a calm tone. The cognitive behavioral therapy providing unit can also provide positive advice if the user is happy. For example, the cognitive behavioral therapy providing unit estimates the user's emotions and adjusts the content of advice and questions based on the estimated emotions. This allows for more effective counseling by adjusting the content of advice and questions based on the user's emotions. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using AI, for example, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's emotional data into the generation AI and have the generation AI adjust the content of advice and questions.

[0076] When providing cognitive behavioral therapy, the cognitive behavioral therapy providing unit can select an optimal approach by referring to the user's past counseling history. The cognitive behavioral therapy providing unit selects an optimal approach based on, for example, the user's past counseling history. For example, the cognitive behavioral therapy providing unit may provide the user with an approach that was effective in the past again. The cognitive behavioral therapy providing unit can also analyze the user's past counseling history and propose a new approach. For example, the cognitive behavioral therapy providing unit selects an optimal approach by referring to the user's past counseling history. In this way, by referring to the user's past counseling history, an optimal approach can be selected and the effectiveness of counseling can be improved. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit may input the user's past counseling history data into a generation AI and cause the generation AI to select an optimal approach.

[0077] The cognitive behavioral therapy providing unit can provide advice based on the user's current living situation and stress level when providing cognitive behavioral therapy. For example, if the user has a high stress level, the cognitive behavioral therapy providing unit uses AI to suggest relaxation methods. For example, the cognitive behavioral therapy providing unit provides specific advice taking into account the user's current living situation. The cognitive behavioral therapy providing unit can also select an appropriate approach based on the user's stress level. For example, the cognitive behavioral therapy providing unit provides advice taking into account the user's current living situation and stress level. This makes it possible to provide more appropriate advice by taking into account the user's current living situation and stress level. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using AI, for example, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's living situation and stress level data into the generation AI and have the generation AI provide advice.

[0078] The cognitive behavioral therapy providing unit can improve the approach by reflecting user feedback when providing cognitive behavioral therapy. The cognitive behavioral therapy providing unit improves the approach based on, for example, feedback provided by the user. For example, the cognitive behavioral therapy providing unit reflects user feedback and adjusts the content of advice. The cognitive behavioral therapy providing unit can also propose a new approach based on user feedback. For example, the cognitive behavioral therapy providing unit improves the approach by reflecting user feedback. In this way, by reflecting user feedback, the approach can be improved and the effectiveness of counseling can be enhanced. Some or all of the above-mentioned processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input user feedback data into a generation AI and cause the generation AI to improve the approach.

[0079] The cognitive behavioral therapy providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is sad, the cognitive behavioral therapy providing unit can prioritize providing advice related to the emotion. For example, if the user is angry, the cognitive behavioral therapy providing unit can prioritize providing advice to stay calm. Furthermore, if the user is happy, the cognitive behavioral therapy providing unit can prioritize providing positive advice. For example, the cognitive behavioral therapy providing unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. In this way, by prioritizing advice based on the user's emotions, important advice can be provided preferentially. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's emotion data to a generation AI and cause the generation AI to determine the priority of advice.

[0080] The cognitive behavioral therapy providing unit can provide advice taking into account the geographical and cultural background of the user when providing cognitive behavioral therapy. For example, the cognitive behavioral therapy providing unit provides region-specific advice taking into account the geographical background of the user. For example, the cognitive behavioral therapy providing unit provides culturally appropriate advice taking into account the cultural background of the user. The cognitive behavioral therapy providing unit can also provide advice for region-specific problems based on the geographical background of the user. For example, the cognitive behavioral therapy providing unit provides advice taking into account the geographical and cultural background of the user. This makes it possible to provide more appropriate advice by taking into account the geographical and cultural background of the user. Some or all of the above-described processing in the cognitive behavioral therapy providing unit may be performed using, or without, AI, for example. For example, the cognitive behavioral therapy providing unit can input the user's geographical and cultural background data into a generation AI and cause the generation AI to provide advice.

[0081] The cognitive behavioral therapy providing unit can analyze the user's social media activities and provide relevant advice when providing cognitive behavioral therapy. The cognitive behavioral therapy providing unit, for example, analyzes the user's social media posts and provides relevant advice. For example, the cognitive behavioral therapy providing unit provides advice taking into account the user's social media friendships. The cognitive behavioral therapy providing unit can also provide advice based on the user's social media activity history. For example, the cognitive behavioral therapy providing unit analyzes the user's social media activities and provides relevant advice. In this way, by analyzing the user's social media activities, relevant advice can be provided and the effectiveness of counseling can be improved. Some or all of the above-mentioned processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant advice.

[0082] The cognitive behavioral therapy providing unit can customize the approach by reflecting the user's past feedback when providing cognitive behavioral therapy. The cognitive behavioral therapy providing unit customizes the approach based on, for example, feedback provided by the user in the past. For example, the cognitive behavioral therapy providing unit reflects the user's past feedback and adjusts the content of advice. The cognitive behavioral therapy providing unit can also propose a new approach based on the user's feedback. For example, the cognitive behavioral therapy providing unit customizes the approach by reflecting the user's past feedback. In this way, by reflecting the user's past feedback, the approach can be customized and the effectiveness of counseling can be improved. Some or all of the above-mentioned processing in the cognitive behavioral therapy providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the cognitive behavioral therapy providing unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the approach.

[0083] The gamification unit can estimate a user's emotions and adjust the content of the reward system based on the estimated user emotions. For example, if the user is sad, the gamification unit adjusts the reward system to provide an encouraging message. For example, if the user is happy, the gamification unit adjusts the reward system to provide an additional reward. Furthermore, if the user is feeling stressed, the gamification unit can adjust the reward system to provide a relaxing reward. For example, the gamification unit estimates a user's emotions and adjusts the content of the reward system based on the estimated emotions. This allows the user's motivation to be maintained by adjusting the content of the reward system based on the user's emotions. Some or all of the above-described processing in the gamification unit may be performed using AI, for example, or may be performed without using AI. For example, the gamification unit can input user emotion data into a generation AI and cause the generation AI to adjust the content of the reward system.

[0084] The gamification unit can optimize the reward system by referring to the user's past achievement history when managing progress. The gamification unit, for example, optimizes the reward system based on the user's past achievement history. For example, the gamification unit provides new rewards based on goals the user has achieved in the past. The gamification unit can also analyze the user's past achievement history and propose an optimal reward system. For example, the gamification unit optimizes the reward system by referring to the user's past achievement history. In this way, by referring to the user's past achievement history, the reward system can be optimized and the user's motivation can be maintained. Some or all of the above-described processing in the gamification unit may be performed using, for example, AI, or may be performed without using AI. For example, the gamification unit can input the user's past achievement history data into a generation AI and cause the generation AI to optimize the reward system.

[0085] The gamification unit can provide rewards based on the user's current motivation level during progress management. For example, the gamification unit provides additional rewards when the user's motivation level is high. For example, the gamification unit provides rewards along with encouraging messages when the user's motivation level is low. The gamification unit can also provide optimal rewards based on the user's motivation level. For example, the gamification unit provides rewards based on the user's current motivation level. This allows the user's current motivation level to be taken into consideration, thereby providing appropriate rewards and maintaining motivation. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's motivation level data into a generation AI and cause the generation AI to provide rewards.

[0086] The gamification unit can improve the reward system by reflecting user feedback during progress management. The gamification unit improves the reward system, for example, based on feedback provided by the user. For example, the gamification unit reflects user feedback and adds new rewards. The gamification unit can also adjust the balance of the reward system based on user feedback. For example, the gamification unit improves the reward system by reflecting user feedback. In this way, by reflecting user feedback, the reward system can be improved and the user's motivation can be maintained. Some or all of the above-mentioned processing in the gamification unit may be performed using, for example, AI, or may be performed without using AI. For example, the gamification unit can input user feedback data into a generation AI and cause the generation AI to improve the reward system.

[0087] The gamification unit can estimate the user's emotions and prioritize rewards based on the estimated user emotions. For example, if the user is sad, the gamification unit can prioritize rewards related to the emotions. For example, if the user is happy, the gamification unit can prioritize positive rewards. Furthermore, if the user is stressed, the gamification unit can prioritize rewards that help the user relax. For example, the gamification unit can estimate the user's emotions and prioritize rewards based on the estimated emotions. In this way, important rewards can be prioritized by prioritizing rewards based on the user's emotions. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's emotion data into a generation AI and cause the generation AI to prioritize rewards.

[0088] The gamification unit can provide rewards by taking into account the user's geographical and cultural backgrounds when managing the progress. For example, the gamification unit can provide region-specific rewards by taking into account the user's geographical background. For example, the gamification unit can provide culturally appropriate rewards by taking into account the user's cultural background. The gamification unit can also provide region-specific rewards based on the user's geographical background. For example, the gamification unit can provide rewards by taking into account the user's geographical and cultural backgrounds. This allows the user to be provided with appropriate rewards and maintain motivation by taking into account the user's geographical and cultural backgrounds. Some or all of the above-described processing in the gamification unit can be performed using, or without, AI. For example, the gamification unit can input the user's geographical and cultural background data into a generation AI and cause the generation AI to provide rewards.

[0089] The gamification unit can analyze the user's social media activities and provide related rewards during progress management. For example, the gamification unit can analyze the user's social media posts and provide related rewards. For example, the gamification unit can provide rewards taking into account the user's social media friendships. The gamification unit can also provide rewards based on the user's social media activity history. For example, the gamification unit can analyze the user's social media activities and provide related rewards. In this way, by analyzing the user's social media activities, it is possible to provide related rewards and maintain motivation. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related rewards.

[0090] The gamification unit can customize the reward system by reflecting the user's past feedback when managing the progress. The gamification unit customizes the reward system based on, for example, feedback provided by the user in the past. For example, the gamification unit adds new rewards by reflecting the user's past feedback. The gamification unit can also adjust the balance of the reward system based on the user's feedback. For example, the gamification unit customizes the reward system by reflecting the user's past feedback. In this way, the reward system can be customized by reflecting the user's past feedback, thereby maintaining motivation. Some or all of the above-described processing in the gamification unit may be performed using, or without, AI. For example, the gamification unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the reward system. === Hard Collateral 1-1 === Each of the multiple elements, including the voice analysis unit, cognitive behavioral therapy provision unit, and gamification unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice analysis unit acquires the user's voice using the microphone 38B of the smart device 14 and analyzes the voice using the control unit 46A. The cognitive behavioral therapy provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate cognitive behavioral therapy based on the analyzed voice data. The gamification unit is implemented, for example, by the control unit 46A of the smart device 14 and manages the user's progress and provides a reward system. === Hard Collateral 1-2 === Each of the multiple elements, including the voice analysis unit, cognitive behavioral therapy provision unit, and gamification unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice analysis unit acquires the user's voice using the microphone 238 of the smart glasses 214 and analyzes the voice using the control unit 46A. The cognitive behavioral therapy provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate cognitive behavioral therapy based on the analyzed voice data. The gamification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and manages the user's progress and provides a reward system. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice analysis unit, cognitive behavioral therapy provision unit, and gamification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice analysis unit acquires the user's voice using the microphone 238 of the headset-type terminal 314 and analyzes the voice using the control unit 46A. The cognitive behavioral therapy provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides appropriate cognitive behavioral therapy based on the analyzed voice data. The gamification unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and manages the user's progress and provides a reward system. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice analysis unit, cognitive behavioral therapy provision unit, and gamification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice analysis unit acquires the user's voice using the microphone 238 of the robot 414 and analyzes the voice using the control unit 46A. The cognitive behavioral therapy provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides appropriate cognitive behavioral therapy based on the analyzed voice data. The gamification unit is realized, for example, by the control unit 46A of the robot 414, and manages the user's progress and provides a reward system.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The counseling system may further include a physiological data acquisition unit that acquires physiological data of the user. The physiological data acquisition unit may measure, for example, the user's heart rate and electrodermal activity to estimate the user's stress level and relaxation level. For example, if the user has a high stress level, the cognitive behavioral therapy provision unit may suggest a relaxation method. If the user is relaxed, the cognitive behavioral therapy provision unit may provide an approach that encourages deeper self-analysis. Furthermore, the physiological data acquisition unit may monitor the user's physiological data over the long term and analyze stress trends. This allows the counseling system to provide a more personalized approach based on the user's physiological data.

[0093] The counseling system may further include a behavioral data acquisition unit that collects user behavioral data. The behavioral data acquisition unit may, for example, collect the user's smartphone usage patterns and location information and analyze the user's daily behavioral patterns. For example, if the user tends to feel stressed when in a particular place, the cognitive behavioral therapy provision unit may provide advice on avoiding that place. Also, if the user feels stressed during a particular time of day, the cognitive behavioral therapy provision unit may suggest ways to relax during that time. Furthermore, the behavioral data acquisition unit may monitor the user's behavioral patterns over the long term and identify stress triggers. This allows the counseling system to provide a more effective approach based on the user's behavioral data.

[0094] The counseling system may further include a dietary data acquisition unit that collects dietary data of the user. The dietary data acquisition unit may, for example, record the content and time of meals eaten by the user and analyze nutritional balance. For example, if the user feels stressed after eating a particular meal, the cognitive behavioral therapy provision unit may provide advice to avoid that meal. Also, if the user's consumption of a nutritionally balanced meal reduces stress, the cognitive behavioral therapy provision unit may recommend that meal. Furthermore, the dietary data acquisition unit may monitor the user's eating patterns over the long term and analyze the relationship between diet and stress. This allows the counseling system to provide a more personalized approach based on the user's dietary data.

[0095] The counseling system may further include a sleep data acquisition unit that collects the user's sleep data. The sleep data acquisition unit may, for example, record the user's sleep time and sleep quality and analyze the sleep pattern. For example, if the user is not getting enough sleep, the cognitive behavioral therapy provision unit may provide advice on improving the quality of sleep. Also, if the user is getting deep sleep during a specific time period, the cognitive behavioral therapy provision unit may suggest a relaxation method tailored to that time period. Furthermore, the sleep data acquisition unit may monitor the user's sleep pattern over the long term and analyze the correlation with stress. This allows the counseling system to provide a more effective approach based on the user's sleep data.

[0096] The counseling system may further include an exercise data acquisition unit that collects the user's exercise data. The exercise data acquisition unit may, for example, record the amount and type of exercise the user does and analyze the exercise patterns. For example, if the user feels stressed due to lack of exercise, the cognitive behavioral therapy provision unit may recommend appropriate exercise. Also, if the user finds that performing a specific exercise reduces stress, the cognitive behavioral therapy provision unit may provide advice for continuing that exercise. Furthermore, the exercise data acquisition unit may monitor the user's exercise patterns over the long term and analyze the relationship between exercise and stress. This allows the counseling system to provide a more personalized approach based on the user's exercise data.

[0097] The counseling system may further include a music providing unit that estimates the user's emotions and provides music based on the estimated emotions. For example, if the user is feeling stressed, the music providing unit may provide relaxing music. For example, if the user is feeling sad, the music providing unit may provide music to lift the user's spirits. Also, if the user is feeling happy, the music providing unit may provide music to maintain the user's emotions. Furthermore, the music providing unit may monitor the user's emotional data over the long term and optimize the selection of music based on the emotional trends. This allows the counseling system to provide more effective music based on the user's emotions.

[0098] The counseling system may further include an aroma providing unit that estimates the user's emotions and provides an aroma based on the estimated emotions. For example, if the user is feeling stressed, the aroma providing unit may provide a relaxing aroma. For example, if the user is feeling sad, the aroma providing unit may provide an aroma to lift the user's mood. Also, if the user is feeling happy, the aroma providing unit may provide an aroma to maintain that emotion. Furthermore, the aroma providing unit may monitor the user's emotional data over the long term and optimize the selection of aromas based on the emotional trends. This allows the counseling system to provide more effective aromas based on the user's emotions.

[0099] The counseling system may further include a lighting adjustment unit that estimates the user's emotions and adjusts lighting based on the estimated emotions. For example, the lighting adjustment unit may provide relaxing lighting when the user is stressed. For example, if the user is sad, the lighting adjustment unit may provide lighting to lift the user's mood. Also, if the user is happy, the lighting adjustment unit may provide lighting to maintain the user's happy emotions. Furthermore, the lighting adjustment unit may monitor the user's emotional data over the long term and optimize lighting settings based on the emotional trends. This allows the counseling system to provide more effective lighting based on the user's emotions.

[0100] The counseling system may further include a VR providing unit that estimates the user's emotions and provides a virtual reality (VR) experience based on the estimated emotions. For example, if the user is feeling stressed, the VR providing unit may provide a virtual environment that helps the user relax. For example, if the user is feeling sad, the VR providing unit may provide a virtual environment that lifts the user's mood. Also, if the user is feeling happy, the VR providing unit may provide a virtual environment that helps the user maintain that emotion. Furthermore, the VR providing unit may monitor the user's emotional data over the long term and optimize the content of the VR experience based on the emotional trends. This allows the counseling system to provide a more effective VR experience based on the user's emotions.

[0101] The counseling system may further include a feedback providing unit that estimates the user's emotions and provides feedback based on the estimated emotions. For example, the feedback providing unit may provide an encouraging message if the user is feeling stressed. For example, if the user is feeling sad, the feedback providing unit may provide a message to lift the user's spirits. Also, if the user is feeling happy, the feedback providing unit may provide a message to maintain the user's emotions. Furthermore, the feedback providing unit may monitor the user's emotional data over the long term and optimize the content of the feedback based on the trend of the emotions. This allows the counseling system to provide more effective feedback based on the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The voice analysis unit analyzes the user's voice. The user's voice may include, for example, complaints or worries, but is not limited to such examples. The voice analysis unit converts the user's speech into text data using, for example, voice recognition technology. The voice analysis unit can also estimate the user's emotions using emotion analysis technology. For example, the voice analysis unit analyzes the tone and speed of the user's voice and calculates an emotion score. The voice analysis unit can also analyze the content of the user's speech and extract specific keywords. For example, the voice analysis unit extracts keywords such as "My work is not going well" from the user's speech and identifies cognitive distortions. Step 2: The cognitive behavioral therapy providing unit provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit. Cognitive behavioral therapy is performed, for example, by providing specific advice and questions, but is not limited to such examples. For example, the cognitive behavioral therapy providing unit asks the user a question such as, "What specifically do you feel is not going well?" The cognitive behavioral therapy providing unit can also provide specific advice to correct the user's cognitive distortions. For example, the cognitive behavioral therapy providing unit provides the user with advice such as, "What can I do to change that way of thinking?" Furthermore, the cognitive behavioral therapy providing unit can select an optimal approach based on the user's specific cognitive patterns and behaviors. For example, if the user has had a specific cognitive distortion in the past, the cognitive behavioral therapy providing unit selects an optimal approach based on that information. Step 3: The gamification unit manages the progress provided by the cognitive behavioral therapy provider and provides a reward system. Progress is managed, for example, by recording the goals and progress achieved by the user, but is not limited to this example. For example, the gamification unit awards points when the user achieves a certain goal. The gamification unit may also enable the user to purchase virtual items using the points. For example, the gamification unit may provide an interface for the user to purchase virtual items using the points. Furthermore, the gamification unit may provide a dashboard for visualizing the progress. For example, the gamification unit may display the progress as a graph or chart so that the user can check their progress.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice analysis unit that analyzes the voice of a user; a cognitive behavioral therapy providing unit that provides cognitive behavioral therapy based on the content analyzed by the voice analysis unit; a gamification unit that manages the progress provided by the cognitive behavioral therapy providing unit and provides a reward system. A system characterized by:

2. The voice analysis unit Identify user complaints and issues 2. The system of claim 1.

3. The cognitive behavioral therapy provider includes: Providing specific advice or questions based on what has been identified 2. The system of claim 1.

4. The gamification unit Give users points for achieving specific goals and allow them to purchase virtual items 2. The system of claim 1.

5. The gamification unit Providing a dashboard to visualize progress 2. The system of claim 1.

6. The cognitive behavioral therapy provider includes: Select the appropriate approach based on the user's specific cognitive patterns and behaviors 2. The system of claim 1.

7. The voice analysis unit Estimate the user's emotions and adjust the accuracy of voice analysis based on the estimated user emotions.

2. The system of claim 1.

8. The voice analysis unit When analyzing voice, the analysis algorithm is optimized by referring to the user's past speech history.

2. The system of claim 1.

9. The voice analysis unit During voice analysis, improve accuracy of analysis based on the user's speaking rate and tone 2. The system of claim 1.

10. The voice analysis unit When analyzing voice, filter out the user's background sounds to improve the accuracy of the analysis.

2. The system of claim 1.

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