System

Abstract

To provide a system providing a conversation party corresponding to a user's individual taste.SOLUTION: A system includes an acquisition section, a personalization section, a conversation section, and a learning section. The acquisition section acquires a user's information. The personalization section personalizes a character on the basis of the information acquired by the acquisition section. The conversation section allows the character personalized by the personalization section to have a conversation. The learning section adjusts conversation contents by learning the contents of a conversation performed by the conversation section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to provide a conversation partner that suited the individual preferences of the user, and there was a problem that it was not able to adequately address loneliness or prevent dementia.

[0005] The system according to the embodiment aims to provide a conversation partner according to the individual preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a personalization unit, a conversation unit, and a learning unit. The acquisition unit acquires user information. The personalization unit personalizes a personality based on the information acquired by the acquisition unit. The conversation unit allows the character personalized by the personalization unit to have a conversation. The learning unit learns the content of the conversation conducted by the conversation unit and adjusts the content of the conversation. Effect of the Invention

[0007] The system according to the embodiment can provide a conversation partner according to a user's individual preferences. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The chatbot system according to the embodiment of the present invention is a system that uses a generation AI to become a conversation partner for elderly people and people living alone who tend to spend a lot of time alone. This system acquires information about the user by having the user access the chatbot and answer several questions. For example, the chatbot answers questions about age, hobbies, favorite topics, etc. This information is input to the generation AI. Next, the generation AI analyzes the input information and personalizes the personality to match the user's preferences. The generation AI sets an appropriate character based on the user's age, hobbies, and favorite topics. For example, a lively and cheerful character is set for the younger generation, and a calm and composed character is set for the elderly. The generated character becomes a casual conversation partner through conversation with the user. For example, by talking about daily events and hobbies, the user's sense of loneliness is reduced. In addition, the generation AI learns the user's reactions and adjusts the content of the conversation appropriately to realize a more natural dialogue. This mechanism allows users to spend their time alone in a fun way, which is expected to prevent dementia and lonely deaths. For example, by elderly people talking to a chatbot every day, the brain is activated, which leads to the prevention of dementia. In addition, by talking to a chatbot, people who live alone can reduce their sense of loneliness and the risk of dying alone. This allows the chatbot system to personalize its personality based on user information, converse, and learn, enabling more natural dialogue.

[0029] The chatbot system according to the embodiment includes an acquisition unit, a personalization unit, a conversation unit, and a learning unit. The acquisition unit acquires information about a user. The information about the user includes, for example, age, hobbies, and favorite topics, but is not limited to such examples. The acquisition unit acquires information by, for example, a user accessing a chatbot and answering some questions. The personalization unit uses a generation AI to personalize a personality based on the information acquired by the acquisition unit. The personalization is performed, for example, based on the user's age, hobbies, and favorite topics, but is not limited to such examples. For example, the generation AI sets a lively and cheerful character for the younger generation and a calm and composed character for the elderly. The conversation unit has a conversation between the characters personalized by the personalization unit. The conversation reduces the user's sense of loneliness, for example, by talking about daily events and hobbies, but is not limited to such examples. For example, the generation AI learns the user's reaction and adjusts the content of the conversation as appropriate, thereby realizing a more natural dialogue. The learning unit learns the content of the conversation performed by the conversation unit and adjusts the content of the conversation. The learning is performed, for example, based on the user's reaction, but is not limited to such examples. For example, the generation AI learns the user's reactions and adjusts the content of the conversation appropriately to realize a more natural conversation. As a result, the chatbot system according to the embodiment can realize a more natural conversation by personalizing the personality based on the user's information, having a conversation, and learning.

[0030] The acquisition unit acquires information about the user. The information about the user includes, but is not limited to, for example, age, hobbies, and favorite topics. The acquisition unit acquires information by, for example, having the user access the chatbot and answering several questions. Specifically, when the user accesses the chatbot for the first time, the acquisition unit presents questions in the form of a simple questionnaire. The questions range from age, gender, hobbies, favorite topics, and daily interests. The answers to these questions are stored in a database in real time and used for subsequent processing. Furthermore, the acquisition unit also collects the user's past conversation history and behavioral patterns. For example, information such as what topics the user has talked about in the past and how he or she responded is accumulated, and based on this, the user's preferences and interests are more deeply understood. In this way, the acquisition unit collects not only basic information about the user, but also dynamic behavioral data, providing a foundation for realizing more accurate personalization. In addition, the acquisition unit takes strict security measures in collecting and storing data to protect the user's privacy. For example, the data is encrypted and stored on a secure server. This allows the acquisition unit to efficiently collect necessary information while gaining the trust of the user.

[0031] The personalization unit uses the generation AI to personalize the personality based on the information acquired by the acquisition unit. Personalization is performed, for example, based on the user's age, hobbies, and favorite topics, but is not limited to such examples. Specifically, the generation AI analyzes the data provided by the acquisition unit and creates a profile of the user. This profile includes the user's age, gender, hobbies, favorite topics, past conversation history, etc. The generation AI sets the optimal character based on this profile. For example, for users of the younger generation, a lively and cheerful character is set, and the conversation tone and topic are also adjusted for young people. On the other hand, for elderly users, a calm and composed character is set, and the conversation tone and topic are also adjusted for elderly people. Furthermore, the generation AI dynamically adjusts the character's behavior and conversation content based on the user's real-time reaction. For example, if a user shows interest in a particular topic, information and questions related to that topic are added. Also, if the user shows an unpleasant reaction, the topic is adjusted to avoid it. As a result, the personalization unit can provide the optimal character for each user and realize more natural and friendly dialogue.

[0032] The conversation unit is a character personalized by the personalization unit that conducts a conversation. The conversation reduces the user's sense of loneliness by, for example, talking about daily events and hobbies, but is not limited to such examples. Specifically, the generation AI generates appropriate conversation content based on the user's profile and real-time reactions. For example, if the user is interested in sports, the AI ​​provides topics about the latest sports news and game results. If the user likes movies, the AI ​​talks about recent movies and recommended movies. Furthermore, the AI ​​learns the user's reactions and adjusts the content of the conversation appropriately. For example, if the user responds positively to a specific topic, the AI ​​adds questions that dig deeper into the topic. On the other hand, for topics in which the user is not interested, the AI ​​switches to another topic. In this way, the conversation unit provides flexible conversation according to the user's interests and concerns, and realizes more natural dialogue. In addition, the conversation unit analyzes the user's emotional state and generates an appropriate response. For example, if the user shows a sad reaction, the AI ​​provides words of encouragement and comfort. On the other hand, if the user is happy, the AI ​​generates a response that shares the joy. In this way, the conversation unit provides a dialogue that is in tune with the user's emotions, and can reduce the user's sense of loneliness.

[0033] The learning unit learns the content of the conversation conducted by the conversation unit and adjusts the conversation content. The learning is performed, for example, based on the user's reaction, but is not limited to such an example. Specifically, the generation AI analyzes the conversation log and learns the user's reaction and behavior patterns. For example, data such as how the user reacted to a specific topic and what questions the user actively responded to is accumulated. Based on these data, the generation AI updates an algorithm that optimizes the content and tone of the conversation. Furthermore, the learning unit collects user feedback and uses it to improve the conversation content. For example, a function is provided for the user to evaluate the content of the conversation, and the quality of the conversation is improved based on the evaluation. In addition, the learning unit can detect unusual patterns and abnormal data using an anomaly detection algorithm and respond early. As a result, the learning unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system. Furthermore, the learning unit also utilizes data from other users to build a more extensive knowledge base. As a result, the learning unit can provide optimal conversation content not only to individual users but also to all users.

[0034] The monitoring unit can monitor the health condition or mood of the user. The monitoring unit can monitor the health condition of the user, such as the heart rate, blood pressure, and stress level of the user. In addition, the monitoring unit can use methods such as questionnaires, facial expression analysis, and voice analysis to monitor the mood of the user. For example, the monitoring unit can capture the facial expression of the user with a camera and estimate the mood using facial expression analysis technology. In addition, the monitoring unit can record the voice of the user and estimate the mood using voice analysis technology. In this way, the monitoring unit can monitor the health condition and mood of the user and take appropriate measures. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the facial expression data of the user to the generation AI and cause the generation AI to execute the estimation of the mood.

[0035] The privacy protection unit can protect the privacy of the user. The privacy protection unit can protect the privacy of the user by using methods such as data encryption and access control. For example, the privacy protection unit encrypts the user's personal information to prevent a third party from accessing it. The privacy protection unit can also limit the authority to access the user's data to protect the privacy. This allows the privacy protection unit to be used safely by protecting the user's privacy. Some or all of the above-mentioned processing in the privacy protection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the privacy protection unit can input the user's data to the generation AI and have the generation AI execute encryption of the data.

[0036] The character setting unit can set a character according to the user's preferences. The character setting unit can set a character based on, for example, the user's questionnaire results or past selection history. For example, the character setting unit sets a character based on the user's hobbies and favorite topics answered in a questionnaire. The character setting unit can also analyze the user's past selection history and set a character according to the user's preferences. As a result, the character setting unit can set a character according to the user's preferences, thereby enabling a more friendly dialogue. A part or all of the above-mentioned processing in the character setting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character setting unit can input the user's questionnaire results to the generation AI and cause the generation AI to execute the character setting.

[0037] The advice providing unit can analyze the conversation history of the user and provide specific advice. The advice providing unit can provide specific advice, such as health advice for the user and suggestions for improving lifestyle habits. For example, the advice providing unit can analyze the conversation history of the user and provide advice regarding the health condition. The advice providing unit can also provide advice regarding the lifestyle habits of the user. As a result, the advice providing unit analyzes the conversation history of the user and provides appropriate advice, thereby improving the satisfaction of the user. A part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the conversation history of the user to the generation AI and cause the generation AI to provide the advice.

[0038] The acquisition unit can analyze the user's past answer history and select the optimal question content. The acquisition unit can select a question based on, for example, a topic in which the user has shown interest in the past. The acquisition unit can also select a question so as to avoid a topic that the user has avoided in the past. Furthermore, the acquisition unit can analyze the user's past answer patterns and select a question content that has received the best response. In this way, the acquisition unit can provide more appropriate question content by analyzing the user's past answer history. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the acquisition unit can input the user's past answer history into the generation AI and cause the generation AI to select the optimal question content.

[0039] The acquisition unit can perform filtering based on the user's current living situation and field of interest when acquiring information. For example, when the user inputs the current living situation, the acquisition unit can preferentially acquire information related to the situation. The acquisition unit can also filter and acquire related information based on the user's field of interest. Furthermore, the acquisition unit can also acquire information at an appropriate time in accordance with the user's life rhythm. As a result, the acquisition unit can provide more relevant information by filtering information based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data on the user's living situation and field of interest to the generation AI and cause the generation AI to filter the information.

[0040] When acquiring information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific area, the acquisition unit can preferentially acquire information related to the area. In addition, when the user is traveling, the acquisition unit can also preferentially acquire information related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also preferentially acquire information around the home. In this way, the acquisition unit can provide more relevant information by considering the geographical location information of the user. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the geographical location information of the user to the generation AI and cause the generation AI to acquire highly relevant information.

[0041] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring information. The acquisition unit can acquire related information based on, for example, the content shared by the user on social media. The acquisition unit can also preferentially acquire information of accounts the user follows on social media. Furthermore, the acquisition unit can analyze the user's social media activity history and acquire information that is likely to interest the user. In this way, the acquisition unit can provide more relevant information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related information.

[0042] The personalization unit can adjust the level of detail of the character based on the user's age and hobbies during personalization. For example, the personalization unit can set a lively and cheerful character for the younger generation. The personalization unit can also set a calm and composed character for the elderly. Furthermore, the personalization unit can set a character knowledgeable about music for a user whose hobby is music. This allows the personalization unit to adjust the level of detail of the character based on the user's age and hobbies, making it possible to have a more friendly conversation. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input data on the user's age and hobbies into the generation AI and cause the generation AI to adjust the level of detail of the character.

[0043] The personalization unit can apply different personalization algorithms depending on the content of the user's answer during personalization. For example, when the user gives a detailed answer, the personalization unit can apply a detailed personalization algorithm. Also, when the user gives a brief answer, the personalization unit can apply a brief personalization algorithm. Furthermore, the personalization unit can select an optimal personalization algorithm based on the content of the user's answer. As a result, the personalization unit can apply different personalization algorithms depending on the content of the user's answer, thereby enabling more appropriate dialogue. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the content of the user's answer to the generation AI and cause the generation AI to apply the personalization algorithm.

[0044] The personalization unit can adjust the character's activity time based on the user's life rhythm during personalization. For example, if the user is an early riser, the personalization unit can set the character to be active in the morning hours. Also, if the user is a night owl, the personalization unit can set the character to be active in the evening hours. Furthermore, the personalization unit can adjust the character's activity time to match the user's life rhythm. As a result, the personalization unit can adjust the character's activity time based on the user's life rhythm, enabling more friendly dialogue. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the personalization unit can input data on the user's life rhythm into the generation AI and cause the generation AI to adjust the character's activity time.

[0045] The personalization unit can adjust the character's language in consideration of the user's cultural background during personalization. For example, if the user is familiar with Japanese culture, the personalization unit can set polite Japanese language. Also, if the user is familiar with English-speaking culture, the personalization unit can set casual English language. Furthermore, the personalization unit can set optimal language in accordance with the user's cultural background. As a result, the personalization unit can adjust the character's language based on the user's cultural background, enabling more friendly dialogue. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input data on the user's cultural background into the generation AI and cause the generation AI to adjust the character's language.

[0046] The conversation unit can provide optimal conversation content by referring to the user's past conversation history during conversation. The conversation unit can provide related conversation content based on topics that the user has talked about in the past, for example. The conversation unit can also provide topics that the user is likely to be interested in from the user's past conversation history. Furthermore, the conversation unit can provide conversation content that avoids topics that the user has avoided in the past. In this way, the conversation unit can provide more appropriate conversation content by referring to the user's past conversation history. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit can input the user's past conversation history into the generation AI and cause the generation AI to provide optimal conversation content.

[0047] The conversation unit can provide related topics during conversation by taking into account the geographical location information of the user. For example, when the user is in a specific area, the conversation unit can provide topics related to the area. In addition, when the user is traveling, the conversation unit can also provide topics related to the travel destination. Furthermore, when the user is at home, the conversation unit can also provide topics around the home. In this way, the conversation unit can provide more relevant topics by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide related topics.

[0048] The conversation unit can provide related topics by analyzing the user's social media activities during the conversation. The conversation unit can provide related topics based on, for example, the content shared by the user on social media. The conversation unit can also provide topics of accounts the user follows on social media. Furthermore, the conversation unit can analyze the user's social media activity history and provide topics that are likely to interest the user. In this way, the conversation unit can provide more relevant topics by analyzing the user's social media activities. Some or all of the above-mentioned processing in the conversation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related topics.

[0049] The learning unit can optimize the learning algorithm by referring to past conversation data during learning. The learning unit can select an optimal learning algorithm based on, for example, past conversation data. The learning unit can also analyze the user's past conversation data and adjust the learning algorithm. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to the past conversation data. As a result, the learning unit improves the accuracy of the learning algorithm by referring to the past conversation data. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input past conversation data to the generation AI and cause the generation AI to optimize the learning algorithm.

[0050] The learning unit can adjust the frequency of learning based on the user's reaction during learning. For example, the learning unit can increase the frequency of learning when the user reacts positively. Also, the learning unit can reduce the frequency of learning when the user does not react. Furthermore, the learning unit can set an optimal learning frequency according to the user's reaction. This allows the learning unit to adjust the learning frequency according to the user's reaction, thereby enabling more appropriate learning. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's reaction data into the generation AI and cause the generation AI to adjust the learning frequency.

[0051] The learning unit can weight the learning data based on the user's life rhythm during learning. For example, if the user is an early riser, the learning unit can focus on conversation data in the morning hours when learning. Also, if the user is a night owl, the learning unit can focus on conversation data in the evening hours when learning. Furthermore, the learning unit can adjust the weighting of the learning data in accordance with the user's life rhythm. This allows the learning unit to weight the learning data based on the user's life rhythm, thereby enabling more appropriate learning. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input data on the user's life rhythm to the generation AI and cause the generation AI to execute weighting of the learning data.

[0052] The learning unit can adjust the learning content in consideration of the cultural background of the user during learning. For example, if the user is familiar with Japanese culture, the learning unit can focus on Japanese conversation data when learning. Also, if the user is familiar with English-speaking culture, the learning unit can also focus on English conversation data when learning. Furthermore, the learning unit can set optimal learning content in accordance with the cultural background of the user. This allows the learning unit to adjust the learning content based on the cultural background of the user, thereby enabling more appropriate learning. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data on the user's cultural background to the generation AI and cause the generation AI to adjust the learning content.

[0053] The monitoring unit can select the optimal monitoring method by referring to the user's past health data during monitoring. The monitoring unit can select the optimal monitoring method based on the user's past health data, for example. The monitoring unit can also analyze the user's health data and adjust the monitoring method. Furthermore, the monitoring unit can also improve the accuracy of the monitoring method by referring to the past health data. As a result, the monitoring unit can provide a more appropriate monitoring method by referring to the user's past health data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's past health data into the generation AI and cause the generation AI to select the optimal monitoring method.

[0054] The monitoring unit can adjust the range of monitoring during monitoring, taking into account the geographical location information of the user. For example, when the user is in a specific area, the monitoring unit can perform monitoring related to the area. In addition, when the user is traveling, the monitoring unit can also perform monitoring related to the travel destination. Furthermore, when the user is at home, the monitoring unit can also perform monitoring around the home. This allows the monitoring unit to perform more appropriate monitoring by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the range of monitoring.

[0055] The privacy protection unit can select an optimal protection method by referring to the user's past privacy settings during privacy protection. The privacy protection unit can select an optimal protection method based on the user's past privacy settings, for example. The privacy protection unit can also analyze the user's privacy settings and adjust the protection method. Furthermore, the privacy protection unit can also improve the accuracy of the protection method by referring to the past privacy settings. As a result, the privacy protection unit can provide more appropriate privacy protection by referring to the user's past privacy settings. A part or all of the above-mentioned processing in the privacy protection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the privacy protection unit can input the user's past privacy settings into the generation AI and cause the generation AI to select an optimal protection method.

[0056] The privacy protection unit can adjust the protection range during privacy protection by taking into account the geographical location information of the user. For example, when the user is in a specific area, the privacy protection unit can perform privacy protection related to the area. In addition, when the user is traveling, the privacy protection unit can also perform privacy protection related to the travel destination. Furthermore, when the user is at home, the privacy protection unit can also perform privacy protection around the home. This allows the privacy protection unit to provide more appropriate privacy protection by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the privacy protection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the privacy protection unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the protection range.

[0057] The character setting unit can provide an optimal setting by referring to the user's past character setting history when setting a character. The character setting unit can provide an optimal setting based on the user's past character setting history, for example. The character setting unit can also analyze the user's character setting history and adjust the setting method. Furthermore, the character setting unit can also improve the accuracy of the setting method by referring to the past character setting history. As a result, the character setting unit can set a more appropriate character by referring to the user's past character setting history. A part or all of the above-mentioned processing in the character setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the character setting unit can input the user's past character setting history to the generation AI and cause the generation AI to provide an optimal setting.

[0058] The character setting unit can set an optimal character in consideration of the geographical location information of the user when setting the character. For example, when the user is in a specific area, the character setting unit can set a character related to the area. In addition, when the user is traveling, the character setting unit can also set a character related to the travel destination. Furthermore, when the user is at home, the character setting unit can also set a character in the vicinity of the home. This allows the character setting unit to set a more appropriate character by considering the geographical location information of the user. Part or all of the above-mentioned processing in the character setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the character setting unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the optimal character setting.

[0059] When providing advice, the advice providing unit can provide optimal advice by referring to the user's past conversation history. The advice providing unit can provide optimal advice based on, for example, the user's past conversation history. The advice providing unit can also analyze the user's conversation history and adjust the advice content. Furthermore, the advice providing unit can also improve the accuracy of the advice by referring to the past conversation history. As a result, the advice providing unit can provide more appropriate advice by referring to the user's past conversation history. A part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's past conversation history into the generation AI and cause the generation AI to provide optimal advice.

[0060] The advice providing unit can provide optimal advice by considering the geographical location information of the user when providing advice. For example, when the user is in a specific area, the advice providing unit can provide advice related to the area. In addition, when the user is traveling, the advice providing unit can also provide advice related to the travel destination. Furthermore, when the user is at home, the advice providing unit can also provide advice around the home. This allows the advice providing unit to provide more appropriate advice by considering the geographical location information of the user. Part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide optimal advice.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The acquisition unit can analyze the user's past conversation history and select the optimal question content. For example, the acquisition unit can select a question based on a topic in which the user has shown interest in the past. The acquisition unit can also select a question so as to avoid a topic that the user has avoided in the past. Furthermore, the acquisition unit can analyze the user's past answer patterns and select a question content that has the best response. In this way, the acquisition unit can provide more appropriate question content by analyzing the user's past answer history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's past answer history into the generation AI and cause the generation AI to select the optimal question content.

[0063] The monitoring unit can monitor the health condition or mood of the user. For example, the monitoring unit can monitor the health condition of the user, such as the heart rate, blood pressure, and stress level. In addition, the monitoring unit can use methods such as questionnaires, facial expression analysis, and voice analysis to monitor the mood of the user. For example, the monitoring unit can capture the facial expression of the user with a camera and estimate the mood using facial expression analysis technology. In addition, the monitoring unit can record the voice of the user and estimate the mood using voice analysis technology. In this way, the monitoring unit can monitor the health condition and mood of the user and take appropriate measures. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the mood.

[0064] The privacy protection unit can protect the privacy of the user. For example, the privacy of the user can be protected by using methods such as data encryption and access control. For example, the privacy protection unit encrypts the user's personal information to prevent a third party from accessing it. The privacy protection unit can also limit the authority to access the user's data to protect the privacy. As a result, the privacy protection unit can be used safely by protecting the user's privacy. Some or all of the above-mentioned processing in the privacy protection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the privacy protection unit can input the user's data to the generation AI and have the generation AI execute encryption of the data.

[0065] The character setting unit can set a character according to the user's preferences. For example, the character setting unit can set a character based on the user's questionnaire results and past selection history. For example, the character setting unit sets a character based on the user's hobbies and favorite topics answered in the questionnaire. The character setting unit can also analyze the user's past selection history and set a character according to the user's preferences. As a result, the character setting unit can set a character according to the user's preferences, thereby enabling a more friendly dialogue. A part or all of the above-mentioned processing in the character setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the character setting unit can input the user's questionnaire results to the generation AI and cause the generation AI to execute the character setting.

[0066] The advice providing unit can analyze the user's conversation history and provide specific advice. For example, specific advice such as health advice for the user or suggestions for improving lifestyle habits can be provided. For example, the advice providing unit can analyze the user's conversation history and provide advice regarding the health condition. The advice providing unit can also provide advice regarding the user's lifestyle habits. As a result, the advice providing unit analyzes the user's conversation history and provides appropriate advice, thereby improving the user's satisfaction. A part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's conversation history to the generation AI and cause the generation AI to provide the advice.

[0067] The acquisition unit can perform filtering based on the user's current living situation and field of interest when acquiring information. For example, when the user inputs the current living situation, information related to the situation can be preferentially acquired. The acquisition unit can also filter and acquire related information based on the user's field of interest. Furthermore, the acquisition unit can also acquire information at an appropriate timing in accordance with the user's life rhythm. As a result, the acquisition unit can provide more relevant information by filtering information based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data on the user's living situation and field of interest to the generation AI and cause the generation AI to execute filtering of the information.

[0068] The process flow of the first embodiment will be briefly described below.

[0069] Step 1: The acquisition unit acquires user information. The user information includes, but is not limited to, for example, age, hobbies, and favorite topics. For example, the acquisition unit acquires information by having the user access a chatbot and answer some questions. Step 2: The personalization unit uses the generation AI to personalize the personality based on the information acquired by the acquisition unit. Personalization is performed based on, for example, the user's age, hobbies, and favorite topics, but is not limited to such examples. For example, the generation AI sets a lively and cheerful character for the younger generation and a calm and composed character for the elderly. Step 3: The conversation unit has a conversation with the character personalized by the personalization unit. The conversation, for example, reduces the user's sense of loneliness by talking about daily events and hobbies, but is not limited to such examples. Step 4: The learning unit learns the content of the conversation carried out by the conversation unit and adjusts the content of the conversation. The learning is performed, for example, based on the user's reaction, but is not limited to this example. For example, the generation AI realizes a more natural dialogue by learning the user's reaction and appropriately adjusting the content of the conversation.

[0070] (Example 2) The chatbot system according to the embodiment of the present invention is a system that uses a generation AI to become a conversation partner for elderly people and people living alone who tend to spend a lot of time alone. This system acquires information about the user by having the user access the chatbot and answer several questions. For example, the chatbot answers questions about age, hobbies, favorite topics, etc. This information is input to the generation AI. Next, the generation AI analyzes the input information and personalizes the personality to match the user's preferences. The generation AI sets an appropriate character based on the user's age, hobbies, and favorite topics. For example, a lively and cheerful character is set for the younger generation, and a calm and composed character is set for the elderly. The generated character becomes a casual conversation partner through conversation with the user. For example, by talking about daily events and hobbies, the user's sense of loneliness is reduced. In addition, the generation AI learns the user's reactions and adjusts the content of the conversation appropriately to realize a more natural dialogue. This mechanism allows users to spend their time alone in a fun way, which is expected to prevent dementia and lonely deaths. For example, by elderly people talking to a chatbot every day, the brain is activated, which leads to the prevention of dementia. In addition, by talking to a chatbot, people who live alone can reduce their sense of loneliness and the risk of dying alone. This allows the chatbot system to personalize its personality based on user information, converse, and learn, enabling more natural dialogue.

[0071] The chatbot system according to the embodiment includes an acquisition unit, a personalization unit, a conversation unit, and a learning unit. The acquisition unit acquires information about a user. The information about the user includes, for example, age, hobbies, and favorite topics, but is not limited to such examples. The acquisition unit acquires information by, for example, a user accessing a chatbot and answering some questions. The personalization unit uses a generation AI to personalize a personality based on the information acquired by the acquisition unit. The personalization is performed, for example, based on the user's age, hobbies, and favorite topics, but is not limited to such examples. For example, the generation AI sets a lively and cheerful character for the younger generation and a calm and composed character for the elderly. The conversation unit has a conversation between the characters personalized by the personalization unit. The conversation reduces the user's sense of loneliness, for example, by talking about daily events and hobbies, but is not limited to such examples. For example, the generation AI learns the user's reaction and adjusts the content of the conversation as appropriate, thereby realizing a more natural dialogue. The learning unit learns the content of the conversation performed by the conversation unit and adjusts the content of the conversation. The learning is performed, for example, based on the user's reaction, but is not limited to such examples. For example, the generation AI learns the user's reactions and adjusts the content of the conversation appropriately to realize a more natural conversation. As a result, the chatbot system according to the embodiment can realize a more natural conversation by personalizing the personality based on the user's information, having a conversation, and learning.

[0072] The acquisition unit acquires information about the user. The information about the user includes, but is not limited to, for example, age, hobbies, and favorite topics. The acquisition unit acquires information by, for example, having the user access the chatbot and answering several questions. Specifically, when the user accesses the chatbot for the first time, the acquisition unit presents questions in the form of a simple questionnaire. The questions range from age, gender, hobbies, favorite topics, and daily interests. The answers to these questions are stored in a database in real time and used for subsequent processing. Furthermore, the acquisition unit also collects the user's past conversation history and behavioral patterns. For example, information such as what topics the user has talked about in the past and how he or she responded is accumulated, and based on this, the user's preferences and interests are more deeply understood. In this way, the acquisition unit collects not only basic information about the user, but also dynamic behavioral data, providing a foundation for realizing more accurate personalization. In addition, the acquisition unit takes strict security measures in collecting and storing data to protect the user's privacy. For example, the data is encrypted and stored on a secure server. This allows the acquisition unit to efficiently collect necessary information while gaining the trust of the user.

[0073] The personalization unit uses the generation AI to personalize the personality based on the information acquired by the acquisition unit. Personalization is performed, for example, based on the user's age, hobbies, and favorite topics, but is not limited to such examples. Specifically, the generation AI analyzes the data provided by the acquisition unit and creates a profile of the user. This profile includes the user's age, gender, hobbies, favorite topics, past conversation history, etc. The generation AI sets the optimal character based on this profile. For example, for users of the younger generation, a lively and cheerful character is set, and the conversation tone and topic are also adjusted for young people. On the other hand, for elderly users, a calm and composed character is set, and the conversation tone and topic are also adjusted for elderly people. Furthermore, the generation AI dynamically adjusts the character's behavior and conversation content based on the user's real-time reaction. For example, if a user shows interest in a particular topic, information and questions related to that topic are added. Also, if the user shows an unpleasant reaction, the topic is adjusted to avoid it. As a result, the personalization unit can provide the optimal character for each user and realize more natural and friendly dialogue.

[0074] The conversation unit is a character personalized by the personalization unit that conducts a conversation. The conversation reduces the user's sense of loneliness by, for example, talking about daily events and hobbies, but is not limited to such examples. Specifically, the generation AI generates appropriate conversation content based on the user's profile and real-time reactions. For example, if the user is interested in sports, the AI ​​provides topics about the latest sports news and game results. If the user likes movies, the AI ​​talks about recent movies and recommended movies. Furthermore, the AI ​​learns the user's reactions and adjusts the content of the conversation appropriately. For example, if the user responds positively to a specific topic, the AI ​​adds questions that dig deeper into the topic. On the other hand, for topics in which the user is not interested, the AI ​​switches to another topic. In this way, the conversation unit provides flexible conversation according to the user's interests and concerns, and realizes more natural dialogue. In addition, the conversation unit analyzes the user's emotional state and generates an appropriate response. For example, if the user shows a sad reaction, the AI ​​provides words of encouragement and comfort. On the other hand, if the user is happy, the AI ​​generates a response that shares the joy. In this way, the conversation unit provides a dialogue that is in tune with the user's emotions, and can reduce the user's sense of loneliness.

[0075] The learning unit learns the content of the conversation conducted by the conversation unit and adjusts the conversation content. The learning is performed, for example, based on the user's reaction, but is not limited to such an example. Specifically, the generation AI analyzes the conversation log and learns the user's reaction and behavior patterns. For example, data such as how the user reacted to a specific topic and what questions the user actively responded to is accumulated. Based on these data, the generation AI updates an algorithm that optimizes the content and tone of the conversation. Furthermore, the learning unit collects user feedback and uses it to improve the conversation content. For example, a function is provided for the user to evaluate the content of the conversation, and the quality of the conversation is improved based on the evaluation. In addition, the learning unit can detect unusual patterns and abnormal data using an anomaly detection algorithm and respond early. As a result, the learning unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system. Furthermore, the learning unit also utilizes data from other users to build a more extensive knowledge base. As a result, the learning unit can provide optimal conversation content not only to individual users but also to all users.

[0076] The monitoring unit can monitor the health condition or mood of the user. The monitoring unit can monitor the health condition of the user, such as the heart rate, blood pressure, and stress level of the user. In addition, the monitoring unit can use methods such as questionnaires, facial expression analysis, and voice analysis to monitor the mood of the user. For example, the monitoring unit can capture the facial expression of the user with a camera and estimate the mood using facial expression analysis technology. In addition, the monitoring unit can record the voice of the user and estimate the mood using voice analysis technology. In this way, the monitoring unit can monitor the health condition and mood of the user and take appropriate measures. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the facial expression data of the user to the generation AI and cause the generation AI to execute the estimation of the mood.

[0077] The privacy protection unit can protect the privacy of the user. The privacy protection unit can protect the privacy of the user by using methods such as data encryption and access control. For example, the privacy protection unit encrypts the user's personal information to prevent a third party from accessing it. The privacy protection unit can also limit the authority to access the user's data to protect the privacy. This allows the privacy protection unit to be used safely by protecting the user's privacy. Some or all of the above-mentioned processing in the privacy protection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the privacy protection unit can input the user's data to the generation AI and have the generation AI execute encryption of the data.

[0078] The character setting unit can set a character according to the user's preferences. The character setting unit can set a character based on, for example, the user's questionnaire results or past selection history. For example, the character setting unit sets a character based on the user's hobbies and favorite topics answered in a questionnaire. The character setting unit can also analyze the user's past selection history and set a character according to the user's preferences. As a result, the character setting unit can set a character according to the user's preferences, thereby enabling a more friendly dialogue. A part or all of the above-mentioned processing in the character setting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character setting unit can input the user's questionnaire results to the generation AI and cause the generation AI to execute the character setting.

[0079] The advice providing unit can analyze the conversation history of the user and provide specific advice. The advice providing unit can provide specific advice, such as health advice for the user and suggestions for improving lifestyle habits. For example, the advice providing unit can analyze the conversation history of the user and provide advice regarding the health condition. The advice providing unit can also provide advice regarding the lifestyle habits of the user. As a result, the advice providing unit analyzes the conversation history of the user and provides appropriate advice, thereby improving the satisfaction of the user. A part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the conversation history of the user to the generation AI and cause the generation AI to provide the advice.

[0080] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring information based on the estimated user's emotion. For example, when the user is stressed, the acquisition unit can delay acquiring information until the user is in a relaxed state. In addition, when the user is excited, the acquisition unit can immediately acquire information and start a conversation. Furthermore, when the user is tired, the acquisition unit can acquire information after the user has taken a rest. This allows the acquisition unit to adjust the timing of acquiring information according to the user's emotion, thereby enabling more appropriate information acquisition. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring information.

[0081] The acquisition unit can analyze the user's past answer history and select the optimal question content. The acquisition unit can select a question based on, for example, a topic in which the user has shown interest in the past. The acquisition unit can also select a question so as to avoid a topic that the user has avoided in the past. Furthermore, the acquisition unit can analyze the user's past answer patterns and select a question content that has received the best response. In this way, the acquisition unit can provide more appropriate question content by analyzing the user's past answer history. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the acquisition unit can input the user's past answer history into the generation AI and cause the generation AI to select the optimal question content.

[0082] The acquisition unit can perform filtering based on the user's current living situation and field of interest when acquiring information. For example, when the user inputs the current living situation, the acquisition unit can preferentially acquire information related to the situation. The acquisition unit can also filter and acquire related information based on the user's field of interest. Furthermore, the acquisition unit can also acquire information at an appropriate time in accordance with the user's life rhythm. As a result, the acquisition unit can provide more relevant information by filtering information based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data on the user's living situation and field of interest to the generation AI and cause the generation AI to filter the information.

[0083] The acquisition unit can estimate the user's emotion and determine the priority of information to be acquired based on the estimated user's emotion. For example, when the user is sad, the acquisition unit can preferentially acquire an encouraging message. Furthermore, when the user is happy, the acquisition unit can preferentially acquire a message of empathy. Furthermore, when the user is feeling anxious, the acquisition unit can preferentially acquire information that reassures the user. In this way, the acquisition unit can provide more appropriate information by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0084] When acquiring information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific area, the acquisition unit can preferentially acquire information related to the area. In addition, when the user is traveling, the acquisition unit can also preferentially acquire information related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also preferentially acquire information around the home. In this way, the acquisition unit can provide more relevant information by considering the geographical location information of the user. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the geographical location information of the user to the generation AI and cause the generation AI to acquire highly relevant information.

[0085] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring information. The acquisition unit can acquire related information based on, for example, the content shared by the user on social media. The acquisition unit can also preferentially acquire information of accounts the user follows on social media. Furthermore, the acquisition unit can analyze the user's social media activity history and acquire information that is likely to interest the user. In this way, the acquisition unit can provide more relevant information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related information.

[0086] The personalization unit can estimate the user's emotions and adjust the personalization method of the personality based on the estimated user's emotions. For example, the personalization unit can set a calm personality when the user is relaxed. In addition, the personalization unit can set a cheerful and bright personality when the user is excited. Furthermore, the personalization unit can set a personality that gently encourages the user when the user is sad. This allows the personalization unit to adjust the personalization method of the personality according to the user's emotions, thereby enabling more appropriate dialogue. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the personalization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the personalization unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the personalization method of the personality.

[0087] The personalization unit can adjust the level of detail of the character based on the user's age and hobbies during personalization. For example, the personalization unit can set a lively and cheerful character for the younger generation. The personalization unit can also set a calm and composed character for the elderly. Furthermore, the personalization unit can set a character knowledgeable about music for a user whose hobby is music. This allows the personalization unit to adjust the level of detail of the character based on the user's age and hobbies, making it possible to have a more friendly conversation. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input data on the user's age and hobbies into the generation AI and cause the generation AI to adjust the level of detail of the character.

[0088] The personalization unit can apply different personalization algorithms depending on the content of the user's answer during personalization. For example, when the user gives a detailed answer, the personalization unit can apply a detailed personalization algorithm. Also, when the user gives a brief answer, the personalization unit can apply a brief personalization algorithm. Furthermore, the personalization unit can select an optimal personalization algorithm based on the content of the user's answer. As a result, the personalization unit can apply different personalization algorithms depending on the content of the user's answer, thereby enabling more appropriate dialogue. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the content of the user's answer to the generation AI and cause the generation AI to apply the personalization algorithm.

[0089] The personalization unit can estimate the user's emotion and adjust the character's expression method based on the estimated user's emotion. For example, the personalization unit can set a gentle expression method when the user is nervous. Also, the personalization unit can set a bright expression method when the user is having fun. Furthermore, the personalization unit can set a gentle expression method when the user is tired. This allows the personalization unit to adjust the character's expression method according to the user's emotion, thereby enabling more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the personalization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the personalization unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the character's expression method.

[0090] The personalization unit can adjust the character's activity time based on the user's life rhythm during personalization. For example, if the user is an early riser, the personalization unit can set the character to be active in the morning hours. Also, if the user is a night owl, the personalization unit can set the character to be active in the evening hours. Furthermore, the personalization unit can adjust the character's activity time to match the user's life rhythm. As a result, the personalization unit can adjust the character's activity time based on the user's life rhythm, enabling more friendly dialogue. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the personalization unit can input data on the user's life rhythm into the generation AI and cause the generation AI to adjust the character's activity time.

[0091] The personalization unit can adjust the character's language in consideration of the user's cultural background during personalization. For example, if the user is familiar with Japanese culture, the personalization unit can set polite Japanese language. Also, if the user is familiar with English-speaking culture, the personalization unit can set casual English language. Furthermore, the personalization unit can set optimal language in accordance with the user's cultural background. As a result, the personalization unit can adjust the character's language based on the user's cultural background, enabling more friendly dialogue. Part or all of the above-mentioned processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input data on the user's cultural background into the generation AI and cause the generation AI to adjust the character's language.

[0092] The conversation unit can estimate the user's emotion and select a conversation topic based on the estimated user's emotion. For example, if the user is sad, the conversation unit can select an encouraging topic. Also, if the user is happy, the conversation unit can select a topic of empathy. Furthermore, if the user is anxious, the conversation unit can select a topic that reassures the user. This allows the conversation unit to select a conversation topic according to the user's emotion, thereby enabling a more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's emotion data to the generation AI and cause the generation AI to select a conversation topic.

[0093] The conversation unit can provide optimal conversation content by referring to the user's past conversation history during conversation. The conversation unit can provide related conversation content based on topics that the user has talked about in the past, for example. The conversation unit can also provide topics that the user is likely to be interested in from the user's past conversation history. Furthermore, the conversation unit can provide conversation content that avoids topics that the user has avoided in the past. In this way, the conversation unit can provide more appropriate conversation content by referring to the user's past conversation history. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the conversation unit can input the user's past conversation history into the generation AI and cause the generation AI to provide optimal conversation content.

[0094] The conversation unit can adjust the tone of the conversation depending on the user's current mood during the conversation. For example, when the user is relaxed, the conversation unit can conduct the conversation in a gentle tone. Also, when the user is excited, the conversation unit can conduct the conversation in a cheerful tone. Furthermore, when the user is sad, the conversation unit can conduct the conversation in a gentle tone. This allows the conversation unit to adjust the tone of the conversation depending on the user's mood, thereby enabling a more appropriate dialogue. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's mood data to the generation AI and cause the generation AI to adjust the tone of the conversation.

[0095] The conversation unit can estimate the user's emotion and adjust the length of the conversation based on the estimated user's emotion. For example, the conversation unit can provide a short conversation when the user is tired. Furthermore, the conversation unit can provide a long conversation when the user is having fun. Furthermore, the conversation unit can provide a short conversation that focuses on the main points when the user is in a hurry. This allows the conversation unit to adjust the length of the conversation according to the user's emotion, thereby enabling more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the conversation.

[0096] The conversation unit can provide related topics during conversation by taking into account the geographical location information of the user. For example, when the user is in a specific area, the conversation unit can provide topics related to the area. In addition, when the user is traveling, the conversation unit can also provide topics related to the travel destination. Furthermore, when the user is at home, the conversation unit can also provide topics around the home. In this way, the conversation unit can provide more relevant topics by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the conversation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide related topics.

[0097] The conversation unit can provide related topics by analyzing the user's social media activities during the conversation. The conversation unit can provide related topics based on, for example, the content shared by the user on social media. The conversation unit can also provide topics of accounts the user follows on social media. Furthermore, the conversation unit can analyze the user's social media activity history and provide topics that are likely to interest the user. In this way, the conversation unit can provide more relevant topics by analyzing the user's social media activities. Some or all of the above-mentioned processing in the conversation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related topics.

[0098] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. For example, the learning unit can select calm conversation data when the user is relaxed. Furthermore, the learning unit can select cheerful conversation data when the user is excited. Furthermore, the learning unit can select gentle conversation data when the user is sad. This allows the learning unit to select learning data according to the user's emotion, thereby enabling more appropriate learning. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's emotion data to the generation AI and cause the generation AI to select the learning data.

[0099] The learning unit can optimize the learning algorithm by referring to past conversation data during learning. The learning unit can select an optimal learning algorithm based on, for example, past conversation data. The learning unit can also analyze the user's past conversation data and adjust the learning algorithm. Furthermore, the learning unit can also improve the accuracy of the learning algorithm by referring to the past conversation data. As a result, the learning unit improves the accuracy of the learning algorithm by referring to the past conversation data. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input past conversation data to the generation AI and cause the generation AI to optimize the learning algorithm.

[0100] The learning unit can adjust the frequency of learning based on the user's reaction during learning. For example, the learning unit can increase the frequency of learning when the user reacts positively. Also, the learning unit can reduce the frequency of learning when the user does not react. Furthermore, the learning unit can set an optimal learning frequency according to the user's reaction. This allows the learning unit to adjust the learning frequency according to the user's reaction, thereby enabling more appropriate learning. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's reaction data into the generation AI and cause the generation AI to adjust the learning frequency.

[0101] The learning unit can estimate the user's emotions and determine the learning priority based on the estimated user's emotions. For example, when the user is sad, the learning unit can preferentially learn encouraging conversation data. Also, when the user is happy, the learning unit can preferentially learn empathetic conversation data. Furthermore, when the user is anxious, the learning unit can preferentially learn reassuring conversation data. This allows the learning unit to determine the learning priority according to the user's emotions, thereby enabling more appropriate learning. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the learning priority.

[0102] The learning unit can weight the learning data based on the user's life rhythm during learning. For example, if the user is an early riser, the learning unit can focus on conversation data in the morning hours when learning. Also, if the user is a night owl, the learning unit can focus on conversation data in the evening hours when learning. Furthermore, the learning unit can adjust the weighting of the learning data in accordance with the user's life rhythm. This allows the learning unit to weight the learning data based on the user's life rhythm, thereby enabling more appropriate learning. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can input data on the user's life rhythm to the generation AI and cause the generation AI to execute weighting of the learning data.

[0103] The learning unit can adjust the learning content in consideration of the cultural background of the user during learning. For example, if the user is familiar with Japanese culture, the learning unit can focus on Japanese conversation data when learning. Also, if the user is familiar with English-speaking culture, the learning unit can also focus on English conversation data when learning. Furthermore, the learning unit can set optimal learning content in accordance with the cultural background of the user. This allows the learning unit to adjust the learning content based on the cultural background of the user, thereby enabling more appropriate learning. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data on the user's cultural background to the generation AI and cause the generation AI to adjust the learning content.

[0104] The monitoring unit can estimate the user's emotion and adjust the monitoring frequency of the health condition based on the estimated user's emotion. For example, the monitoring unit can increase the monitoring frequency when the user is stressed. Also, the monitoring unit can decrease the monitoring frequency when the user is relaxed. Furthermore, the monitoring unit can set an optimal monitoring frequency according to the user's emotion. As a result, the monitoring unit can adjust the monitoring frequency according to the user's emotion, thereby enabling more appropriate health condition monitoring. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the monitoring frequency.

[0105] The monitoring unit can select the optimal monitoring method by referring to the user's past health data during monitoring. The monitoring unit can select the optimal monitoring method based on the user's past health data, for example. The monitoring unit can also analyze the user's health data and adjust the monitoring method. Furthermore, the monitoring unit can also improve the accuracy of the monitoring method by referring to the past health data. As a result, the monitoring unit can provide a more appropriate monitoring method by referring to the user's past health data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's past health data into the generation AI and cause the generation AI to select the optimal monitoring method.

[0106] The monitoring unit can estimate the user's emotion and adjust the display method of the monitoring results based on the estimated user's emotion. For example, when the user is nervous, the monitoring unit can provide a simple and highly visible display method. Furthermore, when the user is relaxed, the monitoring unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This allows the monitoring unit to adjust the display method of the monitoring results according to the user's emotion, thereby enabling more appropriate information provision. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the monitoring results.

[0107] The monitoring unit can adjust the range of monitoring during monitoring, taking into account the geographical location information of the user. For example, when the user is in a specific area, the monitoring unit can perform monitoring related to the area. In addition, when the user is traveling, the monitoring unit can also perform monitoring related to the travel destination. Furthermore, when the user is at home, the monitoring unit can also perform monitoring around the home. This allows the monitoring unit to perform more appropriate monitoring by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the range of monitoring.

[0108] The privacy protection unit can estimate the user's emotion and adjust the level of privacy protection based on the estimated user's emotion. For example, the privacy protection unit can increase the level of privacy protection when the user feels anxious. Also, the privacy protection unit can relax the level of privacy protection when the user is relaxed. Furthermore, the privacy protection unit can set an optimal privacy protection level according to the user's emotion. As a result, the privacy protection unit can adjust the level of privacy protection according to the user's emotion, thereby enabling more appropriate privacy protection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the privacy protection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the privacy protection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the level of privacy protection.

[0109] The privacy protection unit can select an optimal protection method by referring to the user's past privacy settings during privacy protection. The privacy protection unit can select an optimal protection method based on the user's past privacy settings, for example. The privacy protection unit can also analyze the user's privacy settings and adjust the protection method. Furthermore, the privacy protection unit can also improve the accuracy of the protection method by referring to the past privacy settings. As a result, the privacy protection unit can provide more appropriate privacy protection by referring to the user's past privacy settings. A part or all of the above-mentioned processing in the privacy protection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the privacy protection unit can input the user's past privacy settings into the generation AI and cause the generation AI to select an optimal protection method.

[0110] The privacy protection unit can estimate the user's emotion and adjust the notification method of privacy protection based on the estimated user's emotion. For example, when the user is nervous, the privacy protection unit can provide a notification method that is simple and highly visible. Furthermore, when the user is relaxed, the privacy protection unit can also provide a notification method that includes detailed information. Furthermore, when the user is in a hurry, the privacy protection unit can also provide a notification method that focuses on the main points. This allows the privacy protection unit to adjust the notification method of privacy protection according to the user's emotion, thereby enabling more appropriate information provision. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the privacy protection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the privacy protection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the notification method.

[0111] The privacy protection unit can adjust the protection range during privacy protection by taking into account the geographical location information of the user. For example, when the user is in a specific area, the privacy protection unit can perform privacy protection related to the area. In addition, when the user is traveling, the privacy protection unit can also perform privacy protection related to the travel destination. Furthermore, when the user is at home, the privacy protection unit can also perform privacy protection around the home. This allows the privacy protection unit to provide more appropriate privacy protection by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the privacy protection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the privacy protection unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the protection range.

[0112] The character setting unit can estimate the user's emotion and adjust the character setting method based on the estimated user's emotion. For example, when the user is relaxed, the character setting unit can set a calm character. Furthermore, when the user is excited, the character setting unit can also set a lively and cheerful character. Furthermore, when the user is sad, the character setting unit can also set a character that gently encourages the user. This allows the character setting unit to adjust the character setting method according to the user's emotion, thereby enabling more appropriate character setting. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the character setting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the character setting unit can input the user's emotion data to the generation AI and cause the generation AI to execute adjustment of the character setting method.

[0113] The character setting unit can provide an optimal setting by referring to the user's past character setting history when setting a character. The character setting unit can provide an optimal setting based on the user's past character setting history, for example. The character setting unit can also analyze the user's character setting history and adjust the setting method. Furthermore, the character setting unit can also improve the accuracy of the setting method by referring to the past character setting history. As a result, the character setting unit can set a more appropriate character by referring to the user's past character setting history. A part or all of the above-mentioned processing in the character setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the character setting unit can input the user's past character setting history to the generation AI and cause the generation AI to provide an optimal setting.

[0114] The character setting unit can estimate the user's emotion and determine the priority order of characters based on the estimated user's emotion. For example, when the user is sad, the character setting unit can preferentially set an encouraging character. Also, when the user is happy, the character setting unit can preferentially set a sympathetic character. Furthermore, when the user is anxious, the character setting unit can preferentially set a reassuring character. This allows the character setting unit to determine the priority order of characters according to the user's emotion, thereby enabling more appropriate character setting. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the character setting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the character setting unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of characters.

[0115] The character setting unit can set an optimal character in consideration of the geographical location information of the user when setting the character. For example, when the user is in a specific area, the character setting unit can set a character related to the area. In addition, when the user is traveling, the character setting unit can also set a character related to the travel destination. Furthermore, when the user is at home, the character setting unit can also set a character in the vicinity of the home. This allows the character setting unit to set a more appropriate character by considering the geographical location information of the user. Part or all of the above-mentioned processing in the character setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the character setting unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the optimal character setting.

[0116] The advice providing unit can estimate the user's emotion and adjust the content of the advice based on the estimated user's emotion. For example, when the user is sad, the advice providing unit can provide encouraging advice. Furthermore, when the user is happy, the advice providing unit can also provide sympathetic advice. Furthermore, when the user is anxious, the advice providing unit can also provide reassuring advice. This allows the advice providing unit to adjust the content of the advice according to the user's emotion, thereby enabling more appropriate advice. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the advice.

[0117] When providing advice, the advice providing unit can provide optimal advice by referring to the user's past conversation history. The advice providing unit can provide optimal advice based on, for example, the user's past conversation history. The advice providing unit can also analyze the user's conversation history and adjust the advice content. Furthermore, the advice providing unit can also improve the accuracy of the advice by referring to the past conversation history. As a result, the advice providing unit can provide more appropriate advice by referring to the user's past conversation history. A part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's past conversation history into the generation AI and cause the generation AI to provide optimal advice.

[0118] The advice providing unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, when the user is sad, the advice providing unit can provide encouraging advice with priority. Furthermore, when the user is happy, the advice providing unit can also provide empathetic advice with priority. Furthermore, when the user is anxious, the advice providing unit can also provide reassuring advice with priority. This allows the advice providing unit to determine the priority of advice according to the user's emotion, thereby enabling more appropriate advice. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0119] The advice providing unit can provide optimal advice by considering the geographical location information of the user when providing advice. For example, when the user is in a specific area, the advice providing unit can provide advice related to the area. In addition, when the user is traveling, the advice providing unit can also provide advice related to the travel destination. Furthermore, when the user is at home, the advice providing unit can also provide advice around the home. This allows the advice providing unit to provide more appropriate advice by considering the geographical location information of the user. Part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide optimal advice.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The acquisition unit can analyze the user's past conversation history and select the optimal question content. For example, the acquisition unit can select a question based on a topic in which the user has shown interest in the past. The acquisition unit can also select a question so as to avoid a topic that the user has avoided in the past. Furthermore, the acquisition unit can analyze the user's past answer patterns and select a question content that has the best response. In this way, the acquisition unit can provide more appropriate question content by analyzing the user's past answer history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's past answer history into the generation AI and cause the generation AI to select the optimal question content.

[0122] The monitoring unit can monitor the health condition or mood of the user. For example, the monitoring unit can monitor the health condition of the user, such as the heart rate, blood pressure, and stress level. In addition, the monitoring unit can use methods such as questionnaires, facial expression analysis, and voice analysis to monitor the mood of the user. For example, the monitoring unit can capture the facial expression of the user with a camera and estimate the mood using facial expression analysis technology. In addition, the monitoring unit can record the voice of the user and estimate the mood using voice analysis technology. In this way, the monitoring unit can monitor the health condition and mood of the user and take appropriate measures. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the mood.

[0123] The privacy protection unit can protect the privacy of the user. For example, the privacy of the user can be protected by using methods such as data encryption and access control. For example, the privacy protection unit encrypts the user's personal information to prevent a third party from accessing it. The privacy protection unit can also limit the authority to access the user's data to protect the privacy. As a result, the privacy protection unit can be used safely by protecting the user's privacy. Some or all of the above-mentioned processing in the privacy protection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the privacy protection unit can input the user's data to the generation AI and have the generation AI execute encryption of the data.

[0124] The character setting unit can set a character according to the user's preferences. For example, the character setting unit can set a character based on the user's questionnaire results and past selection history. For example, the character setting unit sets a character based on the user's hobbies and favorite topics answered in the questionnaire. The character setting unit can also analyze the user's past selection history and set a character according to the user's preferences. As a result, the character setting unit can set a character according to the user's preferences, thereby enabling a more friendly dialogue. A part or all of the above-mentioned processing in the character setting unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the character setting unit can input the user's questionnaire results to the generation AI and cause the generation AI to execute the character setting.

[0125] The advice providing unit can analyze the user's conversation history and provide specific advice. For example, specific advice such as health advice for the user or suggestions for improving lifestyle habits can be provided. For example, the advice providing unit can analyze the user's conversation history and provide advice regarding the health condition. The advice providing unit can also provide advice regarding the user's lifestyle habits. As a result, the advice providing unit analyzes the user's conversation history and provides appropriate advice, thereby improving the user's satisfaction. A part or all of the above-mentioned processing in the advice providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the advice providing unit can input the user's conversation history to the generation AI and cause the generation AI to provide the advice.

[0126] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring information based on the estimated user's emotion. For example, if the user is stressed, the acquisition unit can delay acquiring information until the user is in a relaxed state. In addition, if the user is excited, the acquisition unit can immediately acquire information and start a conversation. Furthermore, if the user is tired, the acquisition unit can acquire information after the user has taken a rest. This allows the acquisition unit to adjust the timing of acquiring information according to the user's emotion, thereby enabling more appropriate information acquisition. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring information.

[0127] The acquisition unit can perform filtering based on the user's current living situation and field of interest when acquiring information. For example, when the user inputs the current living situation, information related to the situation can be preferentially acquired. The acquisition unit can also filter and acquire related information based on the user's field of interest. Furthermore, the acquisition unit can also acquire information at an appropriate timing in accordance with the user's life rhythm. As a result, the acquisition unit can provide more relevant information by filtering information based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data on the user's living situation and field of interest to the generation AI and cause the generation AI to execute filtering of the information.

[0128] The acquisition unit can estimate the user's emotion and determine the priority of information to be acquired based on the estimated user's emotion. For example, if the user is sad, it can preferentially acquire an encouraging message. Also, if the user is happy, the acquisition unit can preferentially acquire a message of empathy. Furthermore, if the user is feeling anxious, the acquisition unit can preferentially acquire information that reassures the user. In this way, the acquisition unit can provide more appropriate information by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0129] The personalization unit can estimate the user's emotions and adjust the personality personalization method based on the estimated user's emotions. For example, if the user is relaxed, a calm personality can be set. In addition, if the user is excited, the personalization unit can also set a cheerful and bright personality. In addition, if the user is sad, the personalization unit can also set a personality that gently encourages. Thereby, the personalization unit can adjust the personality personalization method according to the user's emotions, thereby enabling more appropriate dialogue. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the personalization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the personalization unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the personality personalization method.

[0130] The conversation unit can estimate the user's emotion and select a conversation topic based on the estimated user's emotion. For example, if the user is sad, it can select an encouraging topic. Also, if the user is happy, the conversation unit can select a topic of empathy. Furthermore, if the user is anxious, the conversation unit can select a topic that reassures the user. This allows the conversation unit to select a conversation topic according to the user's emotion, thereby enabling a more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the conversation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's emotion data to the generation AI and cause the generation AI to select a conversation topic.

[0131] The process flow of the second embodiment will be briefly described below.

[0132] Step 1: The acquisition unit acquires user information. The user information includes, but is not limited to, for example, age, hobbies, and favorite topics. For example, the acquisition unit acquires information by having the user access a chatbot and answer some questions. Step 2: The personalization unit uses the generation AI to personalize the personality based on the information acquired by the acquisition unit. Personalization is performed based on, for example, the user's age, hobbies, and favorite topics, but is not limited to such examples. For example, the generation AI sets a lively and cheerful character for the younger generation and a calm and composed character for the elderly. Step 3: The conversation unit has a conversation with the character personalized by the personalization unit. The conversation, for example, reduces the user's sense of loneliness by talking about daily events and hobbies, but is not limited to such examples. Step 4: The learning unit learns the content of the conversation carried out by the conversation unit and adjusts the content of the conversation. The learning is performed, for example, based on the user's reaction, but is not limited to this example. For example, the generation AI realizes a more natural dialogue by learning the user's reaction and appropriately adjusting the content of the conversation.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0135] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned acquisition unit, personalization unit, conversation unit, learning unit, monitoring unit, privacy protection unit, character setting unit, and advice providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14 and acquires user information. The personalization unit is realized by the specific processing unit 290 of the data processing device 12 and personalizes the personality based on the acquired information. The conversation unit is realized by the control unit 46A of the smart device 14 and the personalized character conducts a conversation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns and adjusts the content of the conversation. The monitoring unit monitors the user's health condition and mood using the camera 42 and microphone 38B of the smart device 14. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The character setting unit is realized by the control unit 46A of the smart device 14 and performs character setting according to the user's preferences. The advice providing unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the conversation history of the user, and provides specific advice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned acquisition unit, personalization unit, conversation unit, learning unit, monitoring unit, privacy protection unit, character setting unit, and advice providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires user information. The personalization unit is realized by the specific processing unit 290 of the data processing device 12 and personalizes the personality based on the acquired information. The conversation unit is realized by the control unit 46A of the smart glasses 214 and the personalized character conducts a conversation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns and adjusts the content of the conversation. The monitoring unit monitors the user's health condition and mood using the camera 42 and microphone 238 of the smart glasses 214. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The character setting unit is realized by the control unit 46A of the smart glasses 214 and performs character setting according to the user's preferences. The advice providing unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the conversation history of the user, and provides specific advice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements including the above-mentioned acquisition unit, personalization unit, conversation unit, learning unit, monitoring unit, privacy protection unit, character setting unit, and advice providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset type terminal 314 and acquires user information. The personalization unit is realized by the specific processing unit 290 of the data processing device 12 and personalizes the personality based on the acquired information. The conversation unit is realized by the control unit 46A of the headset type terminal 314 and the personalized character conducts a conversation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns and adjusts the content of the conversation. The monitoring unit monitors the health condition and mood of the user using the camera 42 and microphone 238 of the headset type terminal 314. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The character setting unit is realized by the control unit 46A of the headset type terminal 314, and sets a character according to the user's preference. The advice providing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's conversation history and provides specific advice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0180] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] Each of the multiple elements including the above-mentioned acquisition unit, personalization unit, conversation unit, learning unit, monitoring unit, privacy protection unit, character setting unit, and advice providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 and acquires user information. The personalization unit is realized by the specific processing unit 290 of the data processing device 12 and personalizes the personality based on the acquired information. The conversation unit is realized by the control unit 46A of the robot 414 and the personalized character conducts a conversation. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns and adjusts the content of the conversation. The monitoring unit monitors the user's health condition and mood using the camera 42 and microphone 238 of the robot 414. The privacy protection unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The character setting unit is realized by the control unit 46A of the robot 414 and performs character setting according to the user's preferences. The advice providing unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the conversation history of the user, and provides specific advice. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0187] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0191] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0198] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0202] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0204] (Appendix 1) An acquisition unit for acquiring user information; a personalization unit that personalizes a personality based on the information acquired by the acquisition unit; a conversation unit in which the character personalized by the personalization unit converses; A learning unit that learns the content of the conversation carried out by the conversation unit and adjusts the content of the conversation. A system characterized by: (Appendix 2) The system described in Appendix 1, further comprising a monitoring unit that monitors the user's health condition or mood. (Appendix 3) Equipped with a privacy protection section that protects user privacy 2. The system of claim 1, (Appendix 4) Equipped with a character setting section that allows users to set characters according to their preferences 2. The system of claim 1, (Appendix 5) The system described in appendix 1, characterized in that the generation AI analyzes the user's conversation history and has an advice providing unit that provides specific advice. (Appendix 6) The acquisition unit is To estimate a user's emotions and adjust the timing of information acquisition based on the estimated user's emotions. 2. The system of claim 1, (Appendix 7) The acquisition unit is Analyze the user's past answer history and select the most appropriate question content 2. The system of claim 1, (Appendix 8) The acquisition unit is Filter information as it is retrieved based on the user's current life situation and interests 2. The system of claim 1, (Appendix 9) The acquisition unit is Estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The acquisition unit is When retrieving information, the system takes into account the user's geographic location information to prioritize retrieving information that is more relevant to the user. 2. The system of claim 1, (Appendix 11) The acquisition unit is When information is collected, the user's social media activity is analyzed to obtain relevant information. 2. The system of claim 1, (Appendix 12) The personalization unit includes: Inferring a user's emotions and adjusting a personality personalization method based on the inferred user's emotions 2. The system of claim 1, (Appendix 13) The personalization unit includes: During personalization, adjust the character's level of detail based on the user's age and hobbies 2. The system of claim 1, (Appendix 14) The personalization unit includes: During personalization, different personalization algorithms are applied depending on the user's answers. 2. The system of claim 1, (Appendix 15) The personalization unit includes: Estimate the user's emotions and adjust the character's expressions based on the estimated user's emotions. 2. The system of claim 1, (Appendix 16) The personalization unit includes: During personalization, adjust the character's activity time based on the user's daily rhythm 2. The system of claim 1, (Appendix 17) The personalization unit includes: During personalization, adjust the character's language to take into account the user's cultural background 2. The system of claim 1, (Appendix 18) The conversation unit includes: Estimate user emotions and select conversation topics based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The conversation unit includes: During a conversation, the system provides the most appropriate conversation content by referring to the user's past conversation history. 2. The system of claim 1, (Appendix 20) The conversation unit includes: When talking, adjust the tone of the conversation depending on the user's current mood 2. The system of claim 1, (Appendix 21) The conversation unit includes: Estimate the user's emotions and adjust the length of the conversation based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The conversation unit includes: During conversations, the app takes into account the user's geographic location to provide relevant topics 2. The system of claim 1, (Appendix 23) The conversation unit includes: Analyzes users' social media activity to provide relevant topics during conversations 2. The system of claim 1, (Appendix 24) The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions. 2. The system of claim 1, (Appendix 25) The learning unit is During learning, past conversation data is referenced to optimize the learning algorithm 2. The system of claim 1, (Appendix 26) The learning unit is During training, adjust the frequency of training based on user response 2. The system of claim 1, (Appendix 27) The learning unit is Estimate user emotions and determine learning priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The learning unit is During learning, the learning data is weighted based on the user's daily rhythm 2. The system of claim 1, (Appendix 29) The learning unit is When learning, adjust the learning content to take into account the user's cultural background 2. The system of claim 1, (Appendix 30) The monitoring unit is To estimate a user's emotion and adjust a frequency of monitoring a health condition based on the estimated user's emotion. 3. The system of claim 2, (Appendix 31) The monitoring unit is When monitoring, the optimal monitoring method is selected by referring to the user's past health data. 3. The system of claim 2, (Appendix 32) The monitoring unit is To estimate a user's emotion and adjust a display method of a monitoring result based on the estimated user's emotion. 3. The system of claim 2, (Appendix 33) The monitoring unit is During monitoring, adjust the monitoring range taking into account the user's geographic location information 3. The system of claim 2, (Appendix 34) The privacy protection unit includes: Estimating user emotions and adjusting the level of privacy protection based on the estimated user emotions 4. The system of claim 3. (Appendix 35) The privacy protection unit includes: When protecting privacy, the system selects the optimal protection method by referring to the user's past privacy settings. 4. The system of claim 3. (Appendix 36) The privacy protection unit includes: Inferring user emotions and adjusting privacy protection notification methods based on the estimated user emotions 4. The system of claim 3. (Appendix 37) The privacy protection unit includes: When protecting privacy, adjust the scope of protection by taking into account the user's geographic location information 4. The system of claim 3. (Appendix 38) The character setting unit includes: Estimate the user's emotions and adjust the character settings based on the estimated user's emotions 5. The system of claim 4. (Appendix 39) The character setting unit includes: When setting up a character, the system provides optimal settings by referencing the user's past character setting history. 5. The system of claim 4. (Appendix 40) The character setting unit includes: Estimate the user's emotions and prioritize characters based on the estimated user's emotions. 5. The system of claim 4. (Appendix 41) The character setting unit includes: When setting a character, the most suitable character is set taking into account the user's geographic location information. 5. The system of claim 4. (Appendix 42) The advice providing unit, Estimate the user's emotions and adjust the advice content based on the estimated user emotions 6. The system of claim 5, (Appendix 43) The advice providing unit, When providing advice, the system refers to the user's past conversation history to provide the most appropriate advice. 6. The system of claim 5, (Appendix 44) The advice providing unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 6. The system of claim 5, (Appendix 45) The advice providing unit, When providing advice, the system takes into account the user's geographic location to provide optimal advice. 6. The system of claim 5, [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit for acquiring user information; a personalization unit that personalizes a personality based on the information acquired by the acquisition unit; a conversation unit in which the character personalized by the personalization unit converses; A learning unit that learns the content of the conversation carried out by the conversation unit and adjusts the content of the conversation. A system characterized by:

2. A monitoring unit that monitors the health condition or mood of the user 2. The system of claim 1 .

3. A privacy protection unit that protects the privacy of the user is provided.

2. The system of claim 1 .

4. A character setting unit that sets a character according to the user's preferences 2. The system of claim 1 .

5. The generation AI includes an advice providing unit that analyzes the conversation history of the user and provides advice to the user.

2. The system of claim 1 .

6. The acquisition unit is The emotion of the user is estimated, and a timing of acquiring information is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

7. The acquisition unit is Analyze the user's past answer history and select the most appropriate question content 2. The system of claim 1 .

8. The acquisition unit is When acquiring information, filtering is performed based on the user's current life situation and areas of interest.

2. The system of claim 1 .

9. The acquisition unit is Estimating the user's emotion and determining a priority order of information to be acquired based on the estimated user's emotion.

2. The system of claim 1 .

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