system

Abstract

The system according to this embodiment aims to provide users with personalized counseling services. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives input from the user. The generation unit provides a personalized chat function based on the information received by the reception unit. The analysis unit analyzes the chat content generated by the generation unit. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a lack of personalized counseling services that allow users to consult easily, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized counseling service to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives input from the user. The generation unit provides a personalized chat function based on the information received by the reception unit. The analysis unit analyzes the chat content generated by the generation unit. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with personalized counseling services. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The counseling system according to an embodiment of the present invention is a counseling service equipped with generative AI. This counseling system is particularly aimed at women aged 10 to 60 who are experiencing problems with PMS (premenstrual syndrome) and menopausal symptoms. The counseling system aims to alleviate the user's emotional burden through natural dialogue that makes it feel as if they are talking to a friend. Specifically, it provides a chat service using a messaging platform such as LINE® to help users understand their symptoms and alleviate anxiety. A key feature of this counseling system is that it has a personalized chat function generated by generative AI, based on individual personality assessments. This allows users to receive advice and support tailored to their needs. The target audience is women suffering from menstrual or menopausal symptoms, particularly those who feel hesitant to seek medical attention. For example, it envisions a situation where a woman in her mid-20s experiences monthly premenstrual depression due to PMS, hesitates to talk to friends, and instead spends her time searching for people with similar symptoms on social media. This counseling system utilizes generative AI to analyze the user's mental state from chat interactions and accumulates data. It guides users to easily use LINE's online diagnostic service and offers advice to brighten their mood. Unlike other AI counseling services, it provides a sense of security through natural conversation. Furthermore, by recording symptoms on a regular basis, it enables accurate communication of symptoms to doctors, leading to more satisfying consultations for users. The market size for this counseling system is estimated at 1.4 billion yen if all women suffering from PMS and menopausal symptoms are guided to online medical consultations. Revenue from advertising and referrals to medical consultations is also expected. This counseling system addresses the current situation of increasing working women amidst the progress of reforms in women's working styles, and aims to provide a service that will form the foundation of the future market as the femtech market expands. In this way, the counseling system can reduce the mental burden on users and provide a sense of security.

[0029] The counseling system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives information, for example, when the user enters a text message. The reception unit can also receive what the user says using voice input. For example, the user uses the microphone of their smartphone to perform voice input. Furthermore, the reception unit can also receive images taken by the user using image input. For example, the user sends an image taken with a camera. The generation unit provides a personalized chat function based on the information received by the reception unit using a generation AI. The generation AI generates chats in response to user input, for example, using a text generation AI (e.g., LLM). The generation unit can also perform a personality assessment of the user using the generation AI. For example, the generation AI generates personalized chats based on the user's past behavioral history and personality assessment results. Some or all of the above-described processes in the generation unit may be performed, for example, using a generation AI, or without using a generation AI. The analysis unit analyzes the chat content generated by the generation unit. The analysis is performed using methods such as text mining, sentiment analysis, and topic modeling, but is not limited to these examples. For example, the analysis unit may use text mining to extract important information from the chat content. The analysis unit may also use sentiment analysis to analyze the user's emotional state. For example, the analysis unit may estimate the user's emotions from the chat content. Furthermore, the analysis unit may use topic modeling to analyze the topics of the chat content. For example, the analysis unit may classify the chat content by topic. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. The advice is provided using methods such as text messages, voice messages, and video messages, but is not limited to these examples.The service provider can, for example, provide advice to the user using text messages. The service provider can also provide advice to the user using voice messages. For example, the service provider can record and send voice messages. Furthermore, the service provider can also provide advice to the user using video messages. For example, the service provider can record and send video messages. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. This allows the counseling system according to the embodiment to accept user input, provide a personalized chat function, and provide advice based on the analysis results.

[0030] The reception department receives input from users. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception department receives information when users enter text messages. Specifically, users input text using a smartphone or computer keyboard and send it to the system. The reception department can also receive what users say using voice input. For example, when a user uses a smartphone microphone for voice input, speech recognition technology is used to convert the speech into text and input it into the system. Furthermore, the reception department can receive images taken by users using image input. For example, when a user sends an image taken with a camera, image recognition technology is used to analyze the content of the image and extract the necessary information. This allows the reception department to handle a variety of user input formats and flexibly receive information. In addition, the reception department can centrally manage user input content and collaborate with other departments and systems as needed. For example, the reception department can save user input content to a database and make it accessible to the generation and analysis departments. The reception department can also verify and correct input content to improve its accuracy and reliability. This allows the reception desk to efficiently and accurately receive user input, improving the overall performance of the system.

[0031] The generation unit uses a generation AI to provide a personalized chat function based on information received by the reception unit. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate chats in response to user input. Specifically, the generation AI generates an appropriate response to the text message entered by the user and provides it to the user. The generation unit can also use the generation AI to perform a personality assessment of the user. For example, the generation AI generates a personalized chat based on the user's past behavioral history and personality assessment results. The generation AI uses natural language processing technology to analyze the user's input and generate an appropriate response. For example, if a user asks for advice about stress, the generation AI provides specific advice for stress reduction based on the user's past consultations and behavioral history. Furthermore, the generation unit can also use the generation AI to understand the user's emotional state and generate a response corresponding to that emotion. For example, if a user is feeling sad, the generation AI generates an encouraging response. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. This allows the generation unit to provide users with personalized chat functionality and generate appropriate responses tailored to their needs.

[0032] The analysis unit analyzes the chat content generated by the generation unit. The analysis may, but is not limited to, methods such as text mining, sentiment analysis, and topic modeling. For example, the analysis unit may use text mining to extract important information from the chat content. Specifically, it may use text mining techniques to extract keywords and phrases from the chat content to identify user interests and problems. The analysis unit may also use sentiment analysis to analyze the user's emotional state. For example, it may estimate the user's emotions from the chat content and assess the level of stress and anxiety the user is experiencing. Furthermore, the analysis unit may use topic modeling to analyze the topics of the chat content. For example, it may classify the chat content by topic to understand what themes the user is discussing. Some or all of the above processing in the analysis unit may be performed using AI, or not. This allows the analysis unit to analyze the generated chat content from multiple perspectives and accurately understand the user's needs and emotional state. Furthermore, the analysis unit may utilize historical data and statistical information to analyze long-term trends and patterns. For example, by tracking changes in user behavior and emotions based on past chat data, future risks and problems can be predicted. This allows the analytics department to not only understand the situation in real time but also to handle long-term risk management and trend analysis, improving the reliability and security of the entire system.

[0033] The service provider provides advice to users based on the analysis results obtained by the analysis provider. This advice may be provided in various ways, such as text messages, voice messages, or video messages, but is not limited to these examples. For example, the service provider may provide advice to users using text messages. Specifically, it may send users specific action guidelines or advice via text message based on the analysis results. The service provider may also provide advice to users using voice messages. For example, it may record and send voice messages to provide direct advice to users. Furthermore, the service provider may also provide advice to users using video messages. For example, it may record and send video messages to provide visual advice to users. Some or all of the above processing in the service provider may be performed using AI, or not. This allows the service provider to provide advice in various formats and offer appropriate support tailored to user needs. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it may record how users reacted to the advice provided and revise the advice based on that information. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with advice quickly and reliably and support them in resolving their problems.

[0034] The generation unit can perform a personality assessment of the user using a generation AI. For example, the generation unit performs a personality assessment of the user using a generation AI. For example, the generation AI performs a personality assessment based on the user's past behavioral history and personality assessment results. The generation unit can also use the generation AI to generate personalized chats based on the user's personality assessment results. For example, the generation AI generates chats that are suitable for the user based on the personality assessment results. In this way, a personalized chat function can be provided by performing a personality assessment of the user using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0035] The analysis unit can analyze the user's mental state from chat interactions using generative AI. For example, the analysis unit uses generative AI to analyze the user's mental state from chat interactions. For example, the generative AI analyzes the chat content and estimates the user's mental state. The analysis unit can also use generative AI to analyze the user's emotional state from chat interactions. For example, the generative AI estimates the user's emotions from the chat content. This allows the analysis unit to provide appropriate advice to the user by analyzing the user's mental state from chat interactions using generative AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI.

[0036] The service provider can provide users with mood-boosting advice using a generative AI. For example, the service provider can use a generative AI to provide users with mood-boosting advice. For example, the generative AI analyzes the user's emotional state and generates mood-boosting advice. The service provider can also use a generative AI to provide users with positive messages. For example, the generative AI generates positive messages according to the user's emotional state. By providing mood-boosting advice to users using a generative AI, the emotional burden on users can be reduced. Some or all of the above-described processes in the service provider may be performed using a generative AI, for example, or without using a generative AI.

[0037] The service provider can record the user's symptoms using a generative AI and assist in communicating those symptoms to a doctor. For example, the service provider uses a generative AI to record the user's symptoms. For example, the generative AI analyzes the user's input and records the symptoms. The service provider can also use the generative AI to assist in communicating the user's symptoms to a doctor. For example, the generative AI organizes the user's symptoms and generates a report for communication to the doctor. By recording the user's symptoms using a generative AI and assisting in the communication of those symptoms to a doctor, users can receive more satisfactory medical care. Some or all of the above-described processes in the service provider may be performed using a generative AI, for example, or without using a generative AI.

[0038] The service provider can use a generation AI to guide users to LINE's online diagnostic service in an easy-to-use manner. For example, the service provider can use a generation AI to guide users to LINE's online diagnostic service. For example, the generation AI analyzes the user's input and generates a message encouraging the use of the online diagnostic service. The service provider can also use a generation AI to guide users through the online diagnostic service procedure. For example, the generation AI generates a message explaining the procedure for the online diagnostic service. In this way, by using a generation AI to guide users to LINE's online diagnostic service in an easy-to-use manner, it becomes easier for users to take the diagnostic service. Some or all of the above-described processes in the service provider may be performed using a generation AI, for example, or without using a generation AI.

[0039] The reception desk can analyze the user's past input history and select the optimal reception method. The reception desk can, for example, use AI to analyze the user's past input history. For example, the reception desk can analyze the user's past chat content and input frequency to select the optimal reception method. The reception desk can also analyze the user's past input patterns and suggest the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. This allows the reception desk to select the optimal reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0040] The reception unit can filter input based on the user's current living situation and areas of interest. For example, the reception unit can use AI to analyze the user's current living situation and areas of interest. For example, the reception unit can analyze the user's occupation, family environment, and health status to generate appropriate questions. The reception unit can also prioritize displaying relevant topics based on the user's areas of interest. For example, the reception unit can generate relevant questions based on the user's hobbies and topics of interest. This allows for the generation of appropriate questions by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0041] The reception unit can prioritize receiving highly relevant inputs by considering the user's geographical location information when receiving input. The reception unit can, for example, use AI to analyze the user's geographical location information. For example, the reception unit can prioritize receiving region-specific information based on the user's location information. The reception unit can also prioritize receiving inputs that provide region-specific advice based on the user's location information. For example, if the reception unit is in a specific region, it will prioritize receiving information related to that region. This allows the reception unit to prioritize receiving highly relevant inputs by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0042] The reception unit can analyze the user's social media activity and accept relevant input when receiving input. The reception unit can analyze the user's social media activity using, for example, AI. For example, the reception unit can analyze the user's posts, the number of likes, and the number of followers, and prioritize accepting relevant input. The reception unit can also analyze the user's current interests from their social media activity and automatically generate relevant questions. For example, the reception unit can generate relevant questions based on information the user has shared on social media. This allows the reception unit to prioritize accepting relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI.

[0043] The generation unit can adjust the level of detail in the chat based on the user's personality assessment results when generating the chat. The generation unit analyzes the user's personality assessment results, for example, using a generation AI. For example, the generation unit adjusts the level of detail in the chat based on the user's personality assessment results. The generation unit can also customize the chat content based on the user's personality assessment results using a generation AI. For example, if the user has an introverted personality, the generation unit generates a chat that includes detailed explanations. By adjusting the level of detail in the chat based on the user's personality assessment results, a chat that suits the user can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0044] The generation unit can apply different generation algorithms depending on the user's category when generating chats. For example, the generation unit can analyze the user's category using a generation AI. For example, the generation unit can select an appropriate generation algorithm based on the user's category. The generation unit can also use a generation AI to generate chats that are appropriate for the user's category. For example, if the user has symptoms of PMS, the generation unit can apply a generation algorithm specifically for PMS. By applying different generation algorithms depending on the user's category, more appropriate chats can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0045] The generation unit can determine the priority of chats based on when the user inputs them during chat generation. The generation unit can analyze the user's input timing, for example, using a generation AI. For example, if the user inputs at night, the generation unit will prioritize generating chats with relaxing content. Also, if the user inputs during work, the generation unit can prioritize generating chats that can be read quickly. For example, if the user inputs on a holiday, the generation unit will prioritize generating chats that include detailed advice. In this way, by determining the priority of chats based on when the user inputs them, more appropriate chats can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0046] The generation unit can adjust the order of chats based on user relevance when generating chats. The generation unit can analyze user relevance, for example, using a generation AI. For example, the generation unit can analyze the user's past chat content and current situation and prioritize generating chats with high relevance. The generation unit can also prioritize generating relevant chats based on the user's areas of interest. For example, the generation unit can prioritize generating chats related to topics the user has consulted about in the past. By adjusting the order of chats based on user relevance, more appropriate chats can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0047] The analysis unit can improve the accuracy of its analysis by considering the relationships between chats during the analysis process. For example, the analysis unit can use AI to analyze the relationships between chats. For example, the analysis unit can analyze the relationship between the user's past chat content and the current chat content. The analysis unit can also analyze relationships by considering the flow of the user's chats. For example, the analysis unit can perform a highly accurate analysis by considering the context of the user's chats. This improves the accuracy of the analysis by considering the relationships between chats. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0048] The analysis unit can perform analysis while considering user attribute information. For example, the analysis unit can use AI to analyze user attribute information. For example, the analysis unit can analyze the user's age, gender, and living environment to perform an appropriate analysis. The analysis unit can also perform an appropriate analysis while considering the user's health status. For example, the analysis unit can adjust the analysis criteria based on the user's health status. This allows for an appropriate analysis by considering the user's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0049] The analysis department can perform analysis while considering the geographical distribution of chats. For example, the analysis department can use AI to analyze the geographical distribution of chats. For example, the analysis department can analyze region-specific problems based on user location information. The analysis department can also analyze regional trends by considering the geographical distribution of users. For example, the analysis department can perform analysis based on user location information to propose countermeasures for each region. This allows for the analysis of region-specific problems by considering the geographical distribution of chats. Some or all of the above processing in the analysis department may be performed using AI, for example, or without using AI.

[0050] The analysis department can improve the accuracy of its analysis by referring to relevant literature related to the chat during the analysis process. For example, the analysis department may use AI to refer to relevant literature related to the chat. For example, the analysis department may refer to academic papers and specialized books related to the chat content during the analysis. The analysis department may also refer to the latest research findings related to the chat content during the analysis. For example, the analysis department may refer to technical reports related to the chat content during the analysis. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis department may be performed using AI, for example, or without using AI.

[0051] The service provider can analyze the user's past behavioral history to select the most appropriate advice when providing it. For example, the service provider can use AI to analyze the user's past behavioral history. For example, the service provider can analyze the user's past chat content and behavioral patterns to select the most appropriate advice. The service provider can also analyze the user's past usage history to select the most appropriate advice. For example, the service provider can select the most appropriate advice based on the advice the user has received in the past. In this way, the service provider can select the most appropriate advice by analyzing the user's past behavioral history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI.

[0052] The service provider can customize the means of providing advice based on the user's current living situation. For example, the service provider may use AI to analyze the user's current living situation. For example, the service provider may analyze the user's occupation, family environment, and health status to provide appropriate advice. The service provider can also provide advice tailored to the user's specific circumstances. For example, if the user is busy, the service provider may provide advice that can be implemented in a short amount of time. By customizing the means of advice based on the user's current living situation, more appropriate advice can be provided. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without using AI.

[0053] The service provider can select the most appropriate advice by considering the user's geographical location when providing advice. For example, the service provider can use AI to analyze the user's geographical location. For example, the service provider can provide region-specific advice based on the user's location. The service provider can also provide region-specific information based on the user's location. For example, if the service provider is in a specific region, it can provide advice relevant to that region. This allows the service provider to provide the most appropriate advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0054] The service provider can analyze the user's social media activity and propose methods for providing advice. For example, the service provider can use AI to analyze the user's social media activity. For example, the service provider can analyze the user's posts, the number of likes, and the number of followers, and provide relevant advice. The service provider can also analyze the user's current interests from their social media activity and provide relevant advice. For example, the service provider can provide relevant advice based on information the user has shared on social media. By analyzing the user's social media activity, it is possible to propose more appropriate methods for providing advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

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

[0056] The generation unit can estimate the user's interests and preferences based on their input and provide personalized content. For example, it can analyze keywords and topics that the user frequently enters to estimate their interests. It can also suggest relevant content based on the user's past input history. For instance, if a user frequently enters information related to health, it can suggest articles and videos on health. Furthermore, the generation unit can customize chat content according to the user's interests. This allows for the provision of more relevant content based on the user's interests.

[0057] The analysis unit can estimate a user's lifestyle based on their input and provide advice for improvement. For example, it can analyze a user's input regarding their diet and exercise to estimate their lifestyle. It can also analyze a user's sleep patterns and stress levels to identify areas for improvement in their lifestyle. For instance, if a user stays up late, it can advise them to go to bed earlier and wake up earlier. Furthermore, the analysis unit can suggest specific improvement methods based on the user's lifestyle. This allows the system to understand the user's lifestyle and support them in leading a healthy life.

[0058] The service provider can provide advice to help users achieve their goals based on their input. For example, it can analyze the goals and plans set by the user and suggest specific steps to achieve them. It can also monitor the user's progress and update the advice as needed. For instance, if the user's goal is weight loss, it can provide advice on diet and exercise. Furthermore, the service provider can send encouraging messages to maintain the user's motivation. This helps support the user in achieving their goals and provides them with a sense of accomplishment.

[0059] The reception desk can estimate the user's health status based on the user's input and provide appropriate support. For example, the reception desk analyzes the user's input regarding symptoms and physical condition to estimate their health status. Furthermore, the reception desk can provide appropriate support according to the user's health status. For instance, if a user inputs cold symptoms, it will provide advice on rest and hydration. In addition, the reception desk can encourage the user to see a doctor based on their health status. This allows the system to understand the user's health status and provide appropriate support.

[0060] The generation unit can estimate the user's learning style based on the user's input and propose the optimal learning method. For example, the generation unit analyzes the user's learning-related input to estimate their learning style. Furthermore, the generation unit can propose the optimal learning method according to the user's learning style. For instance, if the user has a visual learning style, it will propose a learning method using diagrams and graphs. In addition, the generation unit can customize the learning plan based on the user's learning style. This allows the system to provide the optimal learning method tailored to the user's learning style.

[0061] The analysis department can estimate a user's communication style based on their input and provide appropriate advice. For example, it can analyze a user's chat content and communication frequency to estimate their communication style. Furthermore, the analysis department can provide appropriate advice based on the user's communication style. For instance, if a user has an introverted communication style, it can suggest ways to express themselves. In addition, the analysis department can suggest ways to improve interpersonal relationships based on the user's communication style. This allows for the provision of appropriate advice tailored to the user's communication style.

[0062] The service provider can provide reminders to help users achieve their goals based on their input. For example, the service provider can analyze the goals and plans set by the user and send reminders to help them achieve them. The service provider can also monitor the user's progress and update reminders as needed. For instance, if a user's goal is weight loss, the service provider can send reminders about meals and exercise. Furthermore, the service provider can send encouraging messages to support the user in achieving their goals. This allows the service provider to support the user in achieving their goals and provide them with a sense of accomplishment.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. For example, information can be received by the user by typing a text message. It can also receive what the user says using voice input. Furthermore, it can receive images taken by the user using image input. Step 2: The generation unit uses a generation AI to provide a personalized chat function based on the information received by the reception unit. The generation AI uses a text generation AI (e.g., LLM) to generate chat in response to user input. The generation unit can also use the generation AI to perform a personality assessment of the user. Step 3: The analysis unit analyzes the chat content generated by the generation unit. The analysis is performed using methods such as text mining, sentiment analysis, and topic modeling. For example, text mining is used to extract important information from the chat content, sentiment analysis is used to analyze the user's emotional state, and topic modeling is used to analyze the topics of the chat content. Step 4: The service provider provides advice to the user based on the analysis results obtained by the analysis provider. The advice is provided via methods such as text messages, voice messages, and video messages. For example, advice can be provided to the user via text message, voice messages can be recorded and sent, and video messages can be recorded and sent.

[0065] (Example of form 2) The counseling system according to an embodiment of the present invention is a counseling service equipped with generative AI. This counseling system is particularly aimed at women aged 10 to 60 who are experiencing problems with PMS (premenstrual syndrome) and menopausal symptoms. The aim of this counseling system is to alleviate the user's mental burden through natural dialogue that makes it feel as if the user is talking to a friend. Specifically, it provides a chat service using messaging platforms such as LINE to help users understand their symptoms and alleviate anxiety. A key feature of this counseling system is that it has a personalized chat function generated by generative AI, based on individual personality assessments. This allows users to receive advice and support tailored to their needs. The target audience is women suffering from menstrual or menopausal symptoms, particularly those who feel hesitant to seek medical attention. For example, it is envisioned as a situation where a woman in her mid-20s experiences monthly premenstrual depression due to PMS, hesitates to talk to friends, and instead spends her time searching for people with similar symptoms on social media. This counseling system uses generative AI to analyze the user's mental state from chat interactions and accumulates data. It guides users to easily use LINE's online diagnostic service and offers advice to brighten their mood. Unlike other AI counseling services, it provides a sense of security through natural conversation. Furthermore, by recording symptoms on a regular basis, it enables accurate communication of symptoms to doctors, leading to more satisfying consultations for users. The market size for this counseling system is estimated at 1.4 billion yen if all women suffering from PMS and menopausal symptoms are guided to online medical consultations. Revenue from advertising and referrals to medical consultations is also expected. This counseling system addresses the current situation of increasing working women amidst the progress of reforms in women's working styles, and aims to provide a service that will form the foundation of the future market as the femtech market expands. In this way, the counseling system can reduce the mental burden on users and provide a sense of security.

[0066] The counseling system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives information, for example, when the user enters a text message. The reception unit can also receive what the user says using voice input. For example, the user uses the microphone of their smartphone to perform voice input. Furthermore, the reception unit can also receive images taken by the user using image input. For example, the user sends an image taken with a camera. The generation unit provides a personalized chat function based on the information received by the reception unit using a generation AI. The generation AI generates chats in response to user input, for example, using a text generation AI (e.g., LLM). The generation unit can also perform a personality assessment of the user using the generation AI. For example, the generation AI generates personalized chats based on the user's past behavioral history and personality assessment results. Some or all of the above-described processes in the generation unit may be performed, for example, using a generation AI, or without using a generation AI. The analysis unit analyzes the chat content generated by the generation unit. The analysis is performed using methods such as text mining, sentiment analysis, and topic modeling, but is not limited to these examples. For example, the analysis unit may use text mining to extract important information from the chat content. The analysis unit may also use sentiment analysis to analyze the user's emotional state. For example, the analysis unit may estimate the user's emotions from the chat content. Furthermore, the analysis unit may use topic modeling to analyze the topics of the chat content. For example, the analysis unit may classify the chat content by topic. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. The provision unit provides advice to the user based on the analysis results obtained by the analysis unit. The advice is provided using methods such as text messages, voice messages, and video messages, but is not limited to these examples.The service provider can, for example, provide advice to the user using text messages. The service provider can also provide advice to the user using voice messages. For example, the service provider can record and send voice messages. Furthermore, the service provider can also provide advice to the user using video messages. For example, the service provider can record and send video messages. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. This allows the counseling system according to the embodiment to accept user input, provide a personalized chat function, and provide advice based on the analysis results.

[0067] The reception department receives input from users. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception department receives information when users enter text messages. Specifically, users input text using a smartphone or computer keyboard and send it to the system. The reception department can also receive what users say using voice input. For example, when a user uses a smartphone microphone for voice input, speech recognition technology is used to convert the speech into text and input it into the system. Furthermore, the reception department can receive images taken by users using image input. For example, when a user sends an image taken with a camera, image recognition technology is used to analyze the content of the image and extract the necessary information. This allows the reception department to handle a variety of user input formats and flexibly receive information. In addition, the reception department can centrally manage user input content and collaborate with other departments and systems as needed. For example, the reception department can save user input content to a database and make it accessible to the generation and analysis departments. The reception department can also verify and correct input content to improve its accuracy and reliability. This allows the reception desk to efficiently and accurately receive user input, improving the overall performance of the system.

[0068] The generation unit uses a generation AI to provide a personalized chat function based on information received by the reception unit. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate chats in response to user input. Specifically, the generation AI generates an appropriate response to the text message entered by the user and provides it to the user. The generation unit can also use the generation AI to perform a personality assessment of the user. For example, the generation AI generates a personalized chat based on the user's past behavioral history and personality assessment results. The generation AI uses natural language processing technology to analyze the user's input and generate an appropriate response. For example, if a user asks for advice about stress, the generation AI provides specific advice for stress reduction based on the user's past consultations and behavioral history. Furthermore, the generation unit can also use the generation AI to understand the user's emotional state and generate a response corresponding to that emotion. For example, if a user is feeling sad, the generation AI generates an encouraging response. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. This allows the generation unit to provide users with personalized chat functionality and generate appropriate responses tailored to their needs.

[0069] The analysis unit analyzes the chat content generated by the generation unit. The analysis may, but is not limited to, methods such as text mining, sentiment analysis, and topic modeling. For example, the analysis unit may use text mining to extract important information from the chat content. Specifically, it may use text mining techniques to extract keywords and phrases from the chat content to identify user interests and problems. The analysis unit may also use sentiment analysis to analyze the user's emotional state. For example, it may estimate the user's emotions from the chat content and assess the level of stress and anxiety the user is experiencing. Furthermore, the analysis unit may use topic modeling to analyze the topics of the chat content. For example, it may classify the chat content by topic to understand what themes the user is discussing. Some or all of the above processing in the analysis unit may be performed using AI, or not. This allows the analysis unit to analyze the generated chat content from multiple perspectives and accurately understand the user's needs and emotional state. Furthermore, the analysis unit may utilize historical data and statistical information to analyze long-term trends and patterns. For example, by tracking changes in user behavior and emotions based on past chat data, future risks and problems can be predicted. This allows the analytics department to not only understand the situation in real time but also to handle long-term risk management and trend analysis, improving the reliability and security of the entire system.

[0070] The service provider provides advice to users based on the analysis results obtained by the analysis provider. This advice may be provided in various ways, such as text messages, voice messages, or video messages, but is not limited to these examples. For example, the service provider may provide advice to users using text messages. Specifically, it may send users specific action guidelines or advice via text message based on the analysis results. The service provider may also provide advice to users using voice messages. For example, it may record and send voice messages to provide direct advice to users. Furthermore, the service provider may also provide advice to users using video messages. For example, it may record and send video messages to provide visual advice to users. Some or all of the above processing in the service provider may be performed using AI, or not. This allows the service provider to provide advice in various formats and offer appropriate support tailored to user needs. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it may record how users reacted to the advice provided and revise the advice based on that information. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with advice quickly and reliably and support them in resolving their problems.

[0071] The generation unit can perform a personality assessment of the user using a generation AI. For example, the generation unit performs a personality assessment of the user using a generation AI. For example, the generation AI performs a personality assessment based on the user's past behavioral history and personality assessment results. The generation unit can also use the generation AI to generate personalized chats based on the user's personality assessment results. For example, the generation AI generates chats that are suitable for the user based on the personality assessment results. In this way, a personalized chat function can be provided by performing a personality assessment of the user using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0072] The analysis unit can analyze the user's mental state from chat interactions using generative AI. For example, the analysis unit uses generative AI to analyze the user's mental state from chat interactions. For example, the generative AI analyzes the chat content and estimates the user's mental state. The analysis unit can also use generative AI to analyze the user's emotional state from chat interactions. For example, the generative AI estimates the user's emotions from the chat content. This allows the analysis unit to provide appropriate advice to the user by analyzing the user's mental state from chat interactions using generative AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI.

[0073] The service provider can provide users with mood-boosting advice using a generative AI. For example, the service provider can use a generative AI to provide users with mood-boosting advice. For example, the generative AI analyzes the user's emotional state and generates mood-boosting advice. The service provider can also use a generative AI to provide users with positive messages. For example, the generative AI generates positive messages according to the user's emotional state. By providing mood-boosting advice to users using a generative AI, the emotional burden on users can be reduced. Some or all of the above-described processes in the service provider may be performed using a generative AI, for example, or without using a generative AI.

[0074] The service provider can record the user's symptoms using a generative AI and assist in communicating those symptoms to a doctor. For example, the service provider uses a generative AI to record the user's symptoms. For example, the generative AI analyzes the user's input and records the symptoms. The service provider can also use the generative AI to assist in communicating the user's symptoms to a doctor. For example, the generative AI organizes the user's symptoms and generates a report for communication to the doctor. By recording the user's symptoms using a generative AI and assisting in the communication of those symptoms to a doctor, users can receive more satisfactory medical care. Some or all of the above-described processes in the service provider may be performed using a generative AI, for example, or without using a generative AI.

[0075] The service provider can use a generation AI to guide users to LINE's online diagnostic service in an easy-to-use manner. For example, the service provider can use a generation AI to guide users to LINE's online diagnostic service. For example, the generation AI analyzes the user's input and generates a message encouraging the use of the online diagnostic service. The service provider can also use a generation AI to guide users through the online diagnostic service procedure. For example, the generation AI generates a message explaining the procedure for the online diagnostic service. In this way, by using a generation AI to guide users to LINE's online diagnostic service in an easy-to-use manner, it becomes easier for users to take the diagnostic service. Some or all of the above-described processes in the service provider may be performed using a generation AI, for example, or without using a generation AI.

[0076] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception unit estimates emotions from the user's facial expressions using facial recognition technology. The reception unit can also estimate emotions from the tone of the user's voice using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate emotions. Furthermore, the reception unit can also estimate emotions from the content of the user's input using text analysis technology. For example, the reception unit analyzes the user's text message to estimate emotions. By adjusting the timing of input acceptance based on the user's emotions, input can be accepted at a more appropriate time. Some or all of the above processing in the reception unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI.

[0077] The reception desk can analyze the user's past input history and select the optimal reception method. The reception desk can, for example, use AI to analyze the user's past input history. For example, the reception desk can analyze the user's past chat content and input frequency to select the optimal reception method. The reception desk can also analyze the user's past input patterns and suggest the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. This allows the reception desk to select the optimal reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without using AI.

[0078] The reception unit can filter input based on the user's current living situation and areas of interest. For example, the reception unit can use AI to analyze the user's current living situation and areas of interest. For example, the reception unit can analyze the user's occupation, family environment, and health status to generate appropriate questions. The reception unit can also prioritize displaying relevant topics based on the user's areas of interest. For example, the reception unit can generate relevant questions based on the user's hobbies and topics of interest. This allows for the generation of appropriate questions by filtering based on the user's current living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0079] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception unit estimates emotions from the user's facial expressions using facial recognition technology. The reception unit can also estimate emotions from the tone of the user's voice using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate emotions. Furthermore, the reception unit can also estimate emotions from the content of the user's input using text analysis technology. For example, the reception unit analyzes the user's text message to estimate emotions. This allows for the priority of inputs with high urgency to be received first by determining the priority of inputs based on the user's emotions. Some or all of the above processing in the reception unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI.

[0080] The reception unit can prioritize receiving highly relevant inputs by considering the user's geographical location information when receiving input. The reception unit can, for example, use AI to analyze the user's geographical location information. For example, the reception unit can prioritize receiving region-specific information based on the user's location information. The reception unit can also prioritize receiving inputs that provide region-specific advice based on the user's location information. For example, if the reception unit is in a specific region, it will prioritize receiving information related to that region. This allows the reception unit to prioritize receiving highly relevant inputs by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0081] The reception unit can analyze the user's social media activity and accept relevant input when receiving input. The reception unit can analyze the user's social media activity using, for example, AI. For example, the reception unit can analyze the user's posts, the number of likes, and the number of followers, and prioritize accepting relevant input. The reception unit can also analyze the user's current interests from their social media activity and automatically generate relevant questions. For example, the reception unit can generate relevant questions based on information the user has shared on social media. This allows the reception unit to prioritize accepting relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI.

[0082] The generation unit can estimate the user's emotions and adjust the chat's expression based on the estimated emotions. The generation unit estimates the user's emotions using, for example, an emotion engine or a generation AI. For example, the generation unit estimates emotions from the user's facial expressions using facial recognition technology. The generation unit can also estimate emotions from the user's voice tone using voice analysis technology. For example, the generation unit analyzes the user's voice tone and speed to estimate emotions. Furthermore, the generation unit can estimate emotions from the user's input using text analysis technology. For example, the generation unit analyzes the user's text message to estimate emotions. This allows for the generation of more appropriate chats by adjusting the chat's expression based on the user's emotions. Some or all of the above-described processes in the generation unit may be performed using, for example, an emotion engine or a generation AI, or without using an emotion engine or a generation AI.

[0083] The generation unit can adjust the level of detail in the chat based on the user's personality assessment results when generating the chat. The generation unit analyzes the user's personality assessment results, for example, using a generation AI. For example, the generation unit adjusts the level of detail in the chat based on the user's personality assessment results. The generation unit can also customize the chat content based on the user's personality assessment results using a generation AI. For example, if the user has an introverted personality, the generation unit generates a chat that includes detailed explanations. By adjusting the level of detail in the chat based on the user's personality assessment results, a chat that suits the user can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0084] The generation unit can apply different generation algorithms depending on the user's category when generating chats. For example, the generation unit can analyze the user's category using a generation AI. For example, the generation unit can select an appropriate generation algorithm based on the user's category. The generation unit can also use a generation AI to generate chats that are appropriate for the user's category. For example, if the user has symptoms of PMS, the generation unit can apply a generation algorithm specifically for PMS. By applying different generation algorithms depending on the user's category, more appropriate chats can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0085] The generation unit can estimate the user's emotions and adjust the chat length based on the estimated emotions. The generation unit estimates the user's emotions using, for example, an emotion engine or a generation AI. For example, the generation unit estimates emotions from the user's facial expressions using facial recognition technology. The generation unit can also estimate emotions from the user's voice tone using voice analysis technology. For example, the generation unit analyzes the user's voice tone and speed to estimate emotions. Furthermore, the generation unit can estimate emotions from the user's input using text analysis technology. For example, the generation unit analyzes the user's text message to estimate emotions. This allows for the generation of more appropriate chats by adjusting the chat length based on the user's emotions. Some or all of the above-described processes in the generation unit may be performed using, for example, an emotion engine or a generation AI, or without using an emotion engine or a generation AI.

[0086] The generation unit can determine the priority of chats based on when the user inputs them during chat generation. The generation unit can analyze the user's input timing, for example, using a generation AI. For example, if the user inputs at night, the generation unit will prioritize generating chats with relaxing content. Also, if the user inputs during work, the generation unit can prioritize generating chats that can be read quickly. For example, if the user inputs on a holiday, the generation unit will prioritize generating chats that include detailed advice. In this way, by determining the priority of chats based on when the user inputs them, more appropriate chats can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0087] The generation unit can adjust the order of chats based on user relevance when generating chats. The generation unit can analyze user relevance, for example, using a generation AI. For example, the generation unit can analyze the user's past chat content and current situation and prioritize generating chats with high relevance. The generation unit can also prioritize generating relevant chats based on the user's areas of interest. For example, the generation unit can prioritize generating chats related to topics the user has consulted about in the past. By adjusting the order of chats based on user relevance, more appropriate chats can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the analysis unit estimates emotions from the user's facial expressions using facial recognition technology. The analysis unit can also estimate emotions from the user's voice tone using voice analysis technology. For example, the analysis unit analyzes the user's voice tone and speed to estimate emotions. Furthermore, the analysis unit can estimate emotions from the user's input using text analysis technology. For example, the analysis unit analyzes the user's text messages to estimate emotions. This allows for more appropriate analysis by adjusting the analysis criteria based on the user's emotions. Some or all of the above-described processes in the analysis unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI.

[0089] The analysis unit can improve the accuracy of its analysis by considering the relationships between chats during the analysis process. For example, the analysis unit can use AI to analyze the relationships between chats. For example, the analysis unit can analyze the relationship between the user's past chat content and the current chat content. The analysis unit can also analyze relationships by considering the flow of the user's chats. For example, the analysis unit can perform a highly accurate analysis by considering the context of the user's chats. This improves the accuracy of the analysis by considering the relationships between chats. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0090] The analysis unit can perform analysis while considering user attribute information. For example, the analysis unit can use AI to analyze user attribute information. For example, the analysis unit can analyze the user's age, gender, and living environment to perform an appropriate analysis. The analysis unit can also perform an appropriate analysis while considering the user's health status. For example, the analysis unit can adjust the analysis criteria based on the user's health status. This allows for an appropriate analysis by considering the user's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0091] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the analysis unit estimates emotions from the user's facial expressions using facial recognition technology. The analysis unit can also estimate emotions from the tone of the user's voice using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate emotions. Furthermore, the analysis unit can also estimate emotions from the user's input using text analysis technology. For example, the analysis unit analyzes the user's text messages to estimate emotions. This allows for the provision of more appropriate analysis results by adjusting the display order of the analysis results based on the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI.

[0092] The analysis department can perform analysis while considering the geographical distribution of chats. For example, the analysis department can use AI to analyze the geographical distribution of chats. For example, the analysis department can analyze region-specific problems based on user location information. The analysis department can also analyze regional trends by considering the geographical distribution of users. For example, the analysis department can perform analysis based on user location information to propose countermeasures for each region. This allows for the analysis of region-specific problems by considering the geographical distribution of chats. Some or all of the above processing in the analysis department may be performed using AI, for example, or without using AI.

[0093] The analysis department can improve the accuracy of its analysis by referring to relevant literature related to the chat during the analysis process. For example, the analysis department may use AI to refer to relevant literature related to the chat. For example, the analysis department may refer to academic papers and specialized books related to the chat content during the analysis. The analysis department may also refer to the latest research findings related to the chat content during the analysis. For example, the analysis department may refer to technical reports related to the chat content during the analysis. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis department may be performed using AI, for example, or without using AI.

[0094] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. The service provider can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the service provider can estimate emotions from the user's facial expressions using facial recognition technology. The service provider can also estimate emotions from the tone of the user's voice using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the service provider can estimate emotions from the user's input using text analysis technology. For example, the service provider can analyze the user's text message to estimate emotions. This allows the service provider to provide more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Some or all of the above processing in the service provider may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI.

[0095] The service provider can analyze the user's past behavioral history to select the most appropriate advice when providing it. For example, the service provider can use AI to analyze the user's past behavioral history. For example, the service provider can analyze the user's past chat content and behavioral patterns to select the most appropriate advice. The service provider can also analyze the user's past usage history to select the most appropriate advice. For example, the service provider can select the most appropriate advice based on the advice the user has received in the past. In this way, the service provider can select the most appropriate advice by analyzing the user's past behavioral history. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI.

[0096] The service provider can customize the means of providing advice based on the user's current living situation. For example, the service provider may use AI to analyze the user's current living situation. For example, the service provider may analyze the user's occupation, family environment, and health status to provide appropriate advice. The service provider can also provide advice tailored to the user's specific circumstances. For example, if the user is busy, the service provider may provide advice that can be implemented in a short amount of time. By customizing the means of advice based on the user's current living situation, more appropriate advice can be provided. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without using AI.

[0097] The service provider can estimate the user's emotions and determine the priority of advice based on the estimated emotions. The service provider can estimate the user's emotions using, for example, an emotion engine or generative AI. For example, the service provider can estimate emotions from the user's facial expressions using facial recognition technology. The service provider can also estimate emotions from the tone of the user's voice using voice analysis technology. For example, the service provider can analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the service provider can estimate emotions from the user's input using text analysis technology. For example, the service provider can analyze the user's text messages to estimate emotions. This allows the service provider to provide more appropriate advice by determining the priority of advice based on the user's emotions. Some or all of the above processing in the service provider may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI.

[0098] The service provider can select the most appropriate advice by considering the user's geographical location when providing advice. For example, the service provider can use AI to analyze the user's geographical location. For example, the service provider can provide region-specific advice based on the user's location. The service provider can also provide region-specific information based on the user's location. For example, if the service provider is in a specific region, it can provide advice relevant to that region. This allows the service provider to provide the most appropriate advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0099] The service provider can analyze the user's social media activity and propose methods for providing advice. For example, the service provider can use AI to analyze the user's social media activity. For example, the service provider can analyze the user's posts, the number of likes, and the number of followers, and provide relevant advice. The service provider can also analyze the user's current interests from their social media activity and provide relevant advice. For example, the service provider can provide relevant advice based on information the user has shared on social media. By analyzing the user's social media activity, it is possible to propose more appropriate methods for providing advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate a user's stress level based on their input. For example, it can analyze the content and frequency of the user's text messages, as well as the tone and speed of their voice input, to estimate their stress level. The reception desk can also identify the cause of stress from the user's input. For instance, if a user frequently inputs work-related worries, it can infer that work is the source of their stress. Furthermore, the reception desk can suggest relaxation methods at appropriate times, depending on the user's stress level. This allows the system to understand the user's stress level and provide appropriate support.

[0102] The generation unit can estimate the user's interests and preferences based on their input and provide personalized content. For example, it can analyze keywords and topics that the user frequently enters to estimate their interests. It can also suggest relevant content based on the user's past input history. For instance, if a user frequently enters information related to health, it can suggest articles and videos on health. Furthermore, the generation unit can customize chat content according to the user's interests. This allows for the provision of more relevant content based on the user's interests.

[0103] The analysis unit can estimate a user's lifestyle based on their input and provide advice for improvement. For example, it can analyze a user's input regarding their diet and exercise to estimate their lifestyle. It can also analyze a user's sleep patterns and stress levels to identify areas for improvement in their lifestyle. For instance, if a user stays up late, it can advise them to go to bed earlier and wake up earlier. Furthermore, the analysis unit can suggest specific improvement methods based on the user's lifestyle. This allows the system to understand the user's lifestyle and support them in leading a healthy life.

[0104] The service provider can provide advice to help users achieve their goals based on their input. For example, it can analyze the goals and plans set by the user and suggest specific steps to achieve them. It can also monitor the user's progress and update the advice as needed. For instance, if the user's goal is weight loss, it can provide advice on diet and exercise. Furthermore, the service provider can send encouraging messages to maintain the user's motivation. This helps support the user in achieving their goals and provides them with a sense of accomplishment.

[0105] The service provider can provide advice that takes into account the user's emotional state based on the user's input. For example, the service provider can analyze the content of the user's text messages or voice input to estimate their emotional state. Furthermore, the service provider can provide appropriate advice according to the user's emotional state. For instance, if the user is feeling down, it can suggest encouraging messages or ways to relax. In addition, the service provider can adjust the way the advice is presented based on the user's emotional state. This allows the service provider to provide appropriate advice that takes the user's emotional state into consideration.

[0106] The reception desk can estimate the user's health status based on the user's input and provide appropriate support. For example, the reception desk analyzes the user's input regarding symptoms and physical condition to estimate their health status. Furthermore, the reception desk can provide appropriate support according to the user's health status. For instance, if a user inputs cold symptoms, it will provide advice on rest and hydration. In addition, the reception desk can encourage the user to see a doctor based on their health status. This allows the system to understand the user's health status and provide appropriate support.

[0107] The generation unit can estimate the user's learning style based on the user's input and propose the optimal learning method. For example, the generation unit analyzes the user's learning-related input to estimate their learning style. Furthermore, the generation unit can propose the optimal learning method according to the user's learning style. For instance, if the user has a visual learning style, it will propose a learning method using diagrams and graphs. In addition, the generation unit can customize the learning plan based on the user's learning style. This allows the system to provide the optimal learning method tailored to the user's learning style.

[0108] The analysis department can estimate a user's communication style based on their input and provide appropriate advice. For example, it can analyze a user's chat content and communication frequency to estimate their communication style. Furthermore, the analysis department can provide appropriate advice based on the user's communication style. For instance, if a user has an introverted communication style, it can suggest ways to express themselves. In addition, the analysis department can suggest ways to improve interpersonal relationships based on the user's communication style. This allows for the provision of appropriate advice tailored to the user's communication style.

[0109] The service provider can provide reminders that take into account the user's emotional state based on the user's input. For example, the service provider can analyze the content of the user's text messages or voice input to estimate their emotional state. Furthermore, the service provider can provide appropriate reminders according to the user's emotional state. For instance, if the user is feeling stressed, it can send a reminder to help them relax. In addition, the service provider can adjust the timing of reminders based on the user's emotional state. This allows the service provider to provide appropriate reminders that take the user's emotional state into consideration.

[0110] The service provider can provide reminders to help users achieve their goals based on their input. For example, the service provider can analyze the goals and plans set by the user and send reminders to help them achieve them. The service provider can also monitor the user's progress and update reminders as needed. For instance, if a user's goal is weight loss, the service provider can send reminders about meals and exercise. Furthermore, the service provider can send encouraging messages to support the user in achieving their goals. This allows the service provider to support the user in achieving their goals and provide them with a sense of accomplishment.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. For example, information can be received by the user by typing a text message. It can also receive what the user says using voice input. Furthermore, it can receive images taken by the user using image input. Step 2: The generation unit uses a generation AI to provide a personalized chat function based on the information received by the reception unit. The generation AI uses a text generation AI (e.g., LLM) to generate chat in response to user input. The generation unit can also use the generation AI to perform a personality assessment of the user. Step 3: The analysis unit analyzes the chat content generated by the generation unit. The analysis is performed using methods such as text mining, sentiment analysis, and topic modeling. For example, text mining is used to extract important information from the chat content, sentiment analysis is used to analyze the user's emotional state, and topic modeling is used to analyze the topics of the chat content. Step 4: The service provider provides advice to the user based on the analysis results obtained by the analysis provider. The advice is provided via methods such as text messages, voice messages, and video messages. For example, advice can be provided to the user via text message, voice messages can be recorded and sent, and video messages can be recorded and sent.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts text input, voice input, and image input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized chat function using generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the generated chat content. The provision unit is implemented by the output device 40 of the smart device 14 and provides advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized chat function using generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the generated chat content. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized chat function using generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the generated chat content. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a personalized chat function using generation AI. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the generated chat content. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception area that receives input from users, A generation unit that provides a personalized chat function based on the information received by the reception unit, An analysis unit analyzes the chat content generated by the generation unit, The system includes a provisioning unit that provides advice to the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates the user's personality to perform a personality assessment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The AI ​​generates data to analyze the user's mental state from chat conversations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Generating AI provides users with uplifting advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The system uses AI to record the user's symptoms and assist in communicating those symptoms to a doctor. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The generated AI guides users to easily access LINE's online diagnostic service. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system filters the data based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the chat's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a chat, adjust the level of detail in the chat based on the user's personality assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating chats, different generation algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the chat length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When creating a chat, the chat priority is determined based on when the user entered their input. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When creating a chat, the order of the chats is adjusted based on the relevance of the users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing, consider the relationships between chat messages to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When performing analysis, user attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During the analysis, the geographical distribution of the chat will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, we refer to relevant literature related to the chat to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, the system analyzes the user's past behavior history to select the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, customize the method of advice based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, the system selects the most appropriate advice by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and suggest methods for providing advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, A generation unit that provides a personalized chat function based on the information received by the reception unit, An analysis unit analyzes the chat content generated by the generation unit, The system includes a provisioning unit that provides advice to the user based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates the user's personality to perform a personality assessment. The system according to feature 1.

3. The aforementioned analysis unit is The AI ​​generates data to analyze the user's mental state from chat conversations. The system according to feature 1.

4. The aforementioned supply unit is, Generating AI provides users with advice to brighten their mood. The system according to feature 1.

5. The aforementioned supply unit is, The system uses AI to record the user's symptoms and assist in communicating those symptoms to a doctor. The system according to feature 1.

6. The aforementioned supply unit is, The AI ​​generates data to encourage users to easily access online diagnostics. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

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

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