System

Abstract

To provide a system according to an embodiment that relieves loneliness of persons living alone.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; a generation unit; a conversation unit; and a recording unit. The reception unit is configured to input basic information on a user. The analysis unit is configured to analyze the information inputted by the reception unit. The generation unit is configured to generate a conversation on the basis of the information analyzed by the analysis unit. The conversation unit is configured to have a conversation on the basis of a conversation content generated by the generation unit. The recording unit is configured to record a conversation content performed by the conversation unit.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] Conventional technologies do not provide sufficient effective means to alleviate the sense of loneliness of people living alone, and there is room for improvement.

[0005] The system according to the embodiment aims to eliminate the sense of loneliness felt by people living alone. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a conversation unit, and a recording unit. The reception unit inputs basic information of a user. The analysis unit analyzes the information input by the reception unit. The generation unit generates conversation content based on the information analyzed by the analysis unit. The conversation unit conducts a conversation based on the conversation content generated by the generation unit. The recording unit records the conversation content conducted by the conversation unit. Effect of the Invention

[0007] The system according to the embodiment can eliminate the sense of loneliness felt by people living alone. [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 conversation buddy system according to the embodiment of the present invention is a system that uses a generation AI to create a chat robot that serves as a conversation buddy that matches the personality and character of the conversation partner, thereby relieving the other person's sense of loneliness. In the conversation buddy system, a user starts a conversation with a chat robot and inputs basic information (age, gender, hobbies, etc.). Next, the generation AI analyzes the user's input information and generates conversation content that matches the personality and character. For example, for a user whose hobby is watching movies, the system provides the latest movie information and conversations about recommended movies. Also, for an introverted user, the system provides a calm conversation that allows the user to relax. Furthermore, the conversation buddy system records the user's conversation history and adjusts the next conversation content based on the past conversation content. This makes the conversation with the user more natural and familiar. For example, a topic that the user showed interest in in the previous conversation can be brought up again in the next conversation. This mechanism allows the user to feel as if they always have a conversation partner without feeling lonely. For example, it is expected to be a source of emotional support for elderly people living alone and people who are socially isolated. As a result, the conversation buddy system can relieve the user's sense of loneliness and provide emotional support.

[0029] The conversation buddy system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a conversation unit, and a recording unit. The reception unit inputs basic information of the user. The basic information of the user includes, for example, age, gender, hobbies, etc., but is not limited to such examples. The reception unit receives, for example, information such as age, gender, and hobbies input by the user. The analysis unit analyzes the information input by the reception unit using a generation AI. The analysis unit identifies the user's individuality and character based on, for example, information such as the user's age, gender, and hobbies. The generation unit generates conversation content based on the information analyzed by the analysis unit using the generation AI. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that allows the user to relax. The conversation unit has a conversation with the user based on the conversation content generated by the generation unit. For example, the conversation unit has a natural conversation with the user based on the generated conversation content. The recording unit records the conversation content conducted by the conversation unit. The recording unit, for example, records a conversation history with the user and adjusts the content of the next conversation. In this way, the conversation buddy system can generate conversation content that matches the user's personality and character based on the user's basic information, thereby eliminating feelings of loneliness.

[0030] The reception unit inputs basic information of the user. The basic information of the user includes, but is not limited to, for example, age, sex, and hobbies. The reception unit receives information such as age, sex, and hobbies input by the user. Specifically, the reception unit provides an interface for efficiently collecting information input by the user. The interface is designed to allow the user to easily input information, and may use, for example, a drop-down menu, a check box, or a text box. The information input by the user is stored in a database in real time so that the subsequent analysis unit and generation unit can access it. Furthermore, the reception unit has a validation function for preventing input errors by the user, and for example, checks whether the age is within an appropriate range and whether the gender is correctly selected. In addition, the reception unit also has a security function for protecting the privacy of the user, and the input information is encrypted and stored. As a result, the reception unit can accurately and safely collect the basic information of the user and improve the reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis unit identifies the user's personality and character based on information such as the user's age, gender, and hobbies. Specifically, the analysis unit utilizes the natural language processing technology of the generation AI to analyze the user's input information in detail. For example, if the user's hobby is watching movies, the analysis unit infers the user's past movie-watching history and favorite genres, and delves into the user's interest in movies. In addition, the analysis unit analyzes general behavior patterns and trends of interests based on age and gender, and infers the user's personality and lifestyle. The analysis unit integrates these pieces of information to build a model for more accurately identifying the user's personality and character. Furthermore, the analysis unit can perform more accurate analysis by utilizing not only the user's input information, but also past conversation history and data from other users. As a result, the analysis unit can grasp the user's personality and character in detail, and provide a foundation for the generation unit to generate appropriate conversation content.

[0032] The generation unit uses the generation AI to generate conversation content based on the information analyzed by the analysis unit. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that relaxes the user. Specifically, the generation unit utilizes the natural language generation technology of the generation AI to automatically generate conversation content that matches the user's interests and personality. For example, if the user's hobby is watching movies, the generation unit provides the latest movie information and reviews of movies that the user may be interested in. In addition, if the user is introverted, the generation unit advances the conversation in a calm tone that relaxes the user. The generation unit can also analyze the user's reactions and feedback in real time and dynamically adjust the conversation content. This allows the generation unit to provide the user with an optimal conversation experience and improve the user's satisfaction. Furthermore, the generation unit can track changes in the user's interests and concerns based on past conversation history and continuously improve the conversation content. This allows the generation unit to always provide the latest information and conversation content that meets the user's needs, thereby building a long-term relationship with the user.

[0033] The conversation unit converses with the user based on the conversation content generated by the generation unit. The conversation unit converses with the user naturally based on the generated conversation content, for example. Specifically, the conversation unit conveys the conversation content provided by the generation unit to the user using a voice synthesis technology or a text display technology. When using the voice synthesis technology, the conversation unit generates a voice with natural pronunciation and intonation and converses with the user in real time. When using the text display technology, the conversation unit displays the generated conversation content on the screen and provides it in a format that is easy for the user to read. Furthermore, the conversation unit analyzes the user's reaction and input in real time and feeds back an appropriate response to the generation unit. This allows the conversation unit to smoothly proceed with the interaction with the user and provide a natural conversation experience. In addition, the conversation unit can also analyze non-verbal information such as the user's facial expression and tone of voice, and dynamically adjust the conversation content. This allows the conversation unit to flexibly respond according to the user's emotions and state, and provide a more human-like conversation experience.

[0034] The recording unit records the contents of the conversation carried out by the conversation unit. For example, the recording unit records the conversation history with the user and adjusts the contents of the next conversation. Specifically, the recording unit records the contents of the conversation, the user's reactions, feedback, etc. in detail and stores them in a database. As a result, the recording unit can track changes in the user's interests and concerns based on the past conversation history and adjust the contents of the next conversation more appropriately. Furthermore, the recording unit can analyze the user's feedback and identify improvements to the conversation contents and new interests. For example, if the user shows a strong interest in a specific topic, the recording unit stores the information and takes up the topic in the next conversation. In addition, the recording unit has a security function for protecting the user's privacy, and the recorded data is encrypted and stored. As a result, the recording unit can safely manage the user's conversation history and improve the reliability of the entire system. Furthermore, the recording unit can perform statistical analysis based on the conversation history to evaluate the performance of the entire system and the satisfaction of the user. As a result, the recording unit can support continuous improvement and optimization of the system and provide the user with a better conversation experience.

[0035] The reception unit can input basic information such as the user's age, gender, and hobbies. The reception unit inputs basic information such as the user's age, gender, and hobbies. Specific ranges and formats of age include, for example, years, months, and numerical formats. Specific classifications and formats of gender include, for example, male, female, and other options. Specific contents and ranges of hobbies include, for example, sports, reading, and music. This allows the reception unit to accurately input the user's basic information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, when inputting the user's basic information, the reception unit can analyze the input contents using a generation AI to improve the accuracy of the input.

[0036] The analysis unit can analyze the basic information of the user and identify the individuality and character. The analysis unit uses the generation AI to analyze the basic information of the user. The analysis unit identifies the individuality and character of the user based on information such as the user's age, sex, and hobbies. Specific definitions and identification methods of individuality include, for example, personality diagnostic tests and behavior pattern analysis. Specific definitions and identification methods of personality include, for example, the Big Five personality traits and MBTI (Myers-Briggs Type Indicator). This allows the analysis unit to obtain basic information for generating conversation content that matches the individuality and character of the user. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the basic information of the user to the generation AI, and the generation AI identifies the individuality and character.

[0037] The generation unit can generate conversation content that matches the user's personality and character based on the analyzed information. The generation unit uses a generation AI to generate conversation content based on the analyzed information. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that relaxes the user. Specific generation methods and standards for conversation content that matches the user's personality and character include, for example, selecting topics based on the user's interests and concerns. This allows the generation unit to generate conversation content that matches the user's personality and character. Part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the analyzed information into the generation AI, and the generation AI generates the conversation content.

[0038] The conversation unit can have a conversation with the user based on the generated conversation content. The conversation unit has a conversation with the user based on the conversation content generated by the generation unit. The conversation unit, for example, has a natural conversation with the user based on the generated conversation content. Specific standards and methods for a natural conversation include, for example, the flow of dialogue and the timing of responses. This allows the conversation unit to have a natural conversation with the user based on the generated conversation content. A part or all of the above-mentioned processing in the conversation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the generated conversation content to the generation AI, and the generation AI supports the progress of the conversation.

[0039] The recording unit can record the conversation history and adjust the next conversation content. The recording unit records the conversation content performed by the conversation unit. The recording unit records, for example, the conversation history with the user and adjusts the next conversation content. Specific contents and recording methods of the conversation history include, for example, text format, audio format, etc. Specific methods and standards of adjustment include, for example, parameter adjustment of an algorithm, a feedback loop, etc. As a result, the recording unit can realize a more natural conversation by adjusting the next conversation content based on the conversation history. A part or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recording unit inputs the conversation history to a generation AI, and the generation AI adjusts the next conversation content.

[0040] The reception unit can analyze the user's past basic information input history and select the optimal input method. The reception unit uses the generation AI to analyze the user's past basic information input history and select the optimal input method. The reception unit, for example, automatically displays information that the user frequently inputs in the past as a candidate. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information to be used in a specific time period from the user's past input history. Specific contents and recording methods of the past basic information input history include, for example, the input date and time, the input contents, etc. Specific criteria and selection methods for an appropriate input method include, for example, the user's input speed, error rate, etc. As a result, the reception unit can improve input efficiency by providing the optimal input method based on the past input history. Part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the past input history to the generation AI, and the generation AI selects the optimal input method.

[0041] The reception unit can customize the input items based on the user's current living situation and field of interest when inputting the basic information. The reception unit uses the generation AI to customize the input items based on the user's current living situation and field of interest when inputting the basic information. For example, if the user has a hobby of watching movies, the reception unit adds input items related to movies. In addition, if the user has a pet, the reception unit can also add input items related to the pet. In addition, if the user likes traveling, the reception unit can also add input items related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the reception unit can collect more appropriate information by providing input items according to the user's living situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's living situation and field of interest to the generation AI, and the generation AI customizes the input items.

[0042] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting basic information. The reception unit uses the generation AI to preferentially input information with high relevance based on the geographical location information of the user when inputting basic information. For example, when the user lives in a specific area, the reception unit allows information related to the area to be preferentially input. In addition, when the user is traveling, the reception unit can also allow information related to the current location to be preferentially input. Furthermore, when the user plans to move, the reception unit can also allow information related to the new address to be preferentially input. Specific acquisition methods and ranges of geographical location information include, for example, GPS data, IP addresses, and the like. Specific criteria and selection methods for highly relevant information include, for example, geographical proximity, cultural relevance, and the like. As a result, the reception unit can collect more appropriate information by preferentially inputting information based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the geographical location information of the user to the generation AI, and the generation AI selects highly relevant information.

[0043] The reception unit can analyze the user's social media activity and input related information when the basic information is input. The reception unit uses the generation AI to analyze the user's social media activity and input related information when the basic information is input. The reception unit, for example, suggests related input items based on the content that the user frequently posts on social media. The reception unit can also analyze the user's social media friendships and input related information. Furthermore, the reception unit can input information related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activity include, for example, the post content, the number of likes, the number of followers, etc. As a result, the reception unit can collect more appropriate information by inputting information based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's social media activity to the generation AI, and the generation AI selects related information.

[0044] The analysis unit can optimize the analysis algorithm based on the user's past basic information at the time of analysis. The analysis unit optimizes the analysis algorithm based on the user's past basic information at the time of analysis using the generation AI. The analysis unit adjusts the analysis algorithm, for example, based on basic information input by the user in the past. The analysis unit can also analyze the user's past input history and select the optimal analysis method. Furthermore, the analysis unit can improve the accuracy of the analysis result by referring to the user's past basic information. Specific contents and recording methods of the past basic information include, for example, the input date and time, the input contents, etc. Specific methods and standards for optimizing the analysis algorithm include, for example, parameter adjustment, feedback loop, etc. As a result, the analysis unit can improve the accuracy of the analysis result by optimizing the analysis algorithm based on the past basic information. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the past basic information to the generation AI, and the generation AI optimizes the analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis based on the user's living situation and field of interest at the time of analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis based on the user's living situation and field of interest at the time of analysis. For example, if the user enjoys watching movies, the analysis unit preferentially analyzes information related to movies. In addition, if the user has a pet, the analysis unit can also preferentially analyze information related to pets. In addition, if the user likes traveling, the analysis unit can also preferentially analyze information related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the analysis unit can improve the accuracy of the analysis result by performing an analysis based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's living situation and field of interest into the generation AI, and the generation AI improves the accuracy of the analysis.

[0046] The analysis unit can perform analysis based on the geographical location information of the user at the time of analysis. The analysis unit uses the generation AI to perform analysis based on the geographical location information of the user at the time of analysis. For example, if the user lives in a specific area, the analysis unit preferentially analyzes information related to that area. In addition, if the user is traveling, the analysis unit can also preferentially analyze information related to the current location. Furthermore, if the user is planning to move, the analysis unit can also preferentially analyze information related to the new address. Specific acquisition methods and ranges of geographical location information include, for example, GPS data, IP addresses, etc. As a result, the analysis unit can obtain more appropriate analysis results by performing analysis based on the geographical location information of the user. Part or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the geographical location information of the user to the generation AI, and the generation AI performs the analysis.

[0047] The analysis unit can improve the accuracy of the analysis based on the user's social media activity during the analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis based on the user's social media activity during the analysis. The analysis unit, for example, preferentially analyzes related information based on the content frequently posted by the user on social media. The analysis unit can also analyze the user's social media friendships and preferentially analyze related information. Furthermore, the analysis unit can preferentially analyze information related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activities include, for example, the posted content, the number of likes, the number of followers, etc. As a result, the analysis unit can improve the accuracy of the analysis results by performing an analysis based on the user's social media activity. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's social media activity into the generation AI, and the generation AI improves the accuracy of the analysis.

[0048] The generation unit can optimize the generation algorithm by referring to the user's past conversation history when generating the conversation content. The generation unit optimizes the generation algorithm by referring to the user's past conversation history when generating the conversation content using the generation AI. The generation unit generates the conversation content based on, for example, topics in which the user has shown interest in the past. The generation unit can also analyze the user's past conversation history and generate optimal conversation content. Furthermore, the generation unit can smooth the flow of the conversation by referring to the user's past conversation history. Specific contents and recording methods of the past conversation history include, for example, text format and audio format. Specific methods and standards for optimizing the generation algorithm include, for example, parameter adjustment and feedback loop. As a result, the generation unit can smooth the flow of the conversation by generating optimal conversation content based on the past conversation history. Part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the past conversation history into the generation AI, and the generation AI optimizes the generation algorithm.

[0049] The generation unit can improve the accuracy of generation based on the user's living situation and field of interest when generating the conversation content. The generation unit uses the generation AI to improve the accuracy of generation based on the user's living situation and field of interest when generating the conversation content. For example, if the user enjoys watching movies, the generation unit generates a conversation content related to movies. In addition, if the user has a pet, the generation unit can also generate a conversation content related to the pet. Furthermore, if the user likes traveling, the generation unit can generate a conversation content related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the generation unit can provide a more appropriate conversation by generating a conversation content based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's living situation and field of interest into the generation AI, and the generation AI improves the accuracy of generation.

[0050] The generation unit can generate the conversation content based on the geographical location information of the user when generating the conversation content. The generation unit uses the generation AI to generate the conversation content based on the geographical location information of the user when generating the conversation content. For example, if the user lives in a specific area, the generation unit generates the conversation content related to the area. In addition, if the user is traveling, the generation unit can also generate the conversation content related to the current location. Furthermore, if the user is planning to move, the generation unit can generate the conversation content related to the new address. Specific acquisition methods and ranges of the geographical location information include, for example, GPS data, IP address, etc. As a result, the generation unit can provide a more appropriate conversation by generating the conversation content based on the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the geographical location information of the user to the generation AI, and the generation AI generates the conversation content.

[0051] The generation unit can improve the accuracy of generation based on the user's social media activity when generating the conversation content. The generation unit uses the generation AI to improve the accuracy of generation based on the user's social media activity when generating the conversation content. The generation unit generates related conversation content, for example, based on the content that the user frequently posts on social media. The generation unit can also analyze the user's social media friendships and generate related conversation content. Furthermore, the generation unit can generate conversation content related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activity include, for example, the post content, the number of likes, the number of followers, etc. As a result, the generation unit can improve the accuracy of the conversation by generating conversation content based on the user's social media activity. Part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's social media activity into the generation AI, and the generation AI generates the conversation content.

[0052] The conversation unit can optimize the progress of the conversation by referring to the user's past conversation history during the conversation. The conversation unit optimizes the progress of the conversation by referring to the user's past conversation history using the generation AI during the conversation. The conversation unit progresses the conversation, for example, based on topics in which the user has shown interest in the past. The conversation unit can also analyze the user's past conversation history and select an optimal way to progress the conversation. Furthermore, the conversation unit can smooth the flow of the conversation by referring to the user's past conversation history. Specific contents and recording methods of the past conversation history include, for example, text format and audio format. Specific methods and standards for optimizing the progress include, for example, selection of an algorithm and adjustment of progress parameters. As a result, the conversation unit can smooth the flow of the conversation by providing an optimal way to progress the conversation based on the past conversation history. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the past conversation history into the generation AI, and the generation AI optimizes the progress of the conversation.

[0053] The conversation unit can customize the content of the conversation based on the user's living situation and field of interest during the conversation. The conversation unit uses the generation AI to customize the content of the conversation based on the user's living situation and field of interest during the conversation. For example, if the user enjoys watching movies, the conversation unit provides topics related to movies. In addition, if the user has a pet, the conversation unit can also provide topics related to pets. In addition, if the user likes traveling, the conversation unit can also provide topics related to travel. Specific contents and ranges of the living situation include, for example, occupation, home environment, health condition, etc. Specific contents and ranges of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the conversation unit can realize a more appropriate conversation by providing conversation content based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the user's living situation and field of interest into the generation AI, and the generation AI customizes the content of the conversation.

[0054] The conversation unit can conduct a conversation based on the geographical location information of the user during the conversation. The conversation unit uses the generation AI to conduct a conversation based on the geographical location information of the user during the conversation. For example, if the user lives in a specific area, the conversation unit provides a topic related to the area. In addition, if the user is traveling, the conversation unit can also provide a topic related to the current location. Furthermore, if the user is planning to move, the conversation unit can also provide a topic related to the new address. Specific acquisition methods and ranges of the geographical location information include, for example, GPS data, IP addresses, and the like. As a result, the conversation unit can realize a more appropriate conversation by providing a conversation content based on the geographical location information of the user. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the geographical location information of the user to the generation AI, and the generation AI generates the conversation content.

[0055] The conversation unit can adjust the content of the conversation based on the user's social media activity during the conversation. The conversation unit adjusts the content of the conversation based on the user's social media activity using the generation AI during the conversation. The conversation unit provides related topics based on, for example, the content frequently posted by the user on social media. The conversation unit can also analyze the user's social media friendships and provide related topics. Furthermore, the conversation unit can provide topics related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activities include, for example, the posted content, the number of likes, the number of followers, etc. As a result, the conversation unit can improve the accuracy of the conversation by providing the conversation content based on the user's social media activity. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the user's social media activity into the generation AI, and the generation AI generates the conversation content.

[0056] The recording unit can optimize the recording algorithm by referring to the user's past conversation history at the time of recording. The recording unit optimizes the recording algorithm by referring to the user's past conversation history using the generation AI at the time of recording. The recording unit adjusts the recording content based on, for example, topics in which the user has shown interest in the past. The recording unit can also analyze the user's past conversation history and select an optimal recording method. Furthermore, the recording unit can improve the accuracy of the recording by referring to the user's past conversation history. Specific contents and recording methods of the past conversation history include, for example, text format and audio format. Specific methods and standards for optimizing the recording algorithm include, for example, parameter adjustment and feedback loop. As a result, the recording unit can improve the accuracy of the recording by providing an optimal recording method based on the past conversation history. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the past conversation history into the generation AI, and the generation AI optimizes the recording algorithm.

[0057] The recording unit can improve the accuracy of the recording based on the user's living situation and field of interest at the time of recording. The recording unit uses the generation AI to improve the accuracy of the recording based on the user's living situation and field of interest at the time of recording. For example, if the user has a hobby of watching movies, the recording unit preferentially records the content of the conversation related to movies. In addition, if the user has a pet, the recording unit can also preferentially record the content of the conversation related to the pet. In addition, if the user likes traveling, the recording unit can also preferentially record the content of the conversation related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the recording unit can improve the accuracy of the recorded content by performing recording based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the user's living situation and field of interest into the generation AI, and the generation AI improves the accuracy of the recording.

[0058] The recording unit can perform recording based on the geographical location information of the user at the time of recording. The recording unit performs recording based on the geographical location information of the user using the generation AI at the time of recording. For example, if the user lives in a specific area, the recording unit preferentially records conversation content related to that area. In addition, if the user is traveling, the recording unit can also preferentially record conversation content related to the current location. Furthermore, if the user is planning to move, the recording unit can also preferentially record conversation content related to the new address. Specific acquisition methods and ranges of geographical location information include, for example, GPS data, IP address, etc. As a result, the recording unit can provide more appropriate recorded content by performing recording based on the geographical location information of the user. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the geographical location information of the user to the generation AI, and the generation AI generates the recorded content.

[0059] The recording unit can improve the accuracy of the recording based on the user's social media activity at the time of recording. The recording unit uses the generation AI to improve the accuracy of the recording based on the user's social media activity at the time of recording. For example, the recording unit preferentially records related conversation content based on the content frequently posted by the user on social media. The recording unit can also analyze the user's social media friendships and preferentially record related conversation content. Furthermore, the recording unit can preferentially record conversation content related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activity include, for example, the posting content, the number of likes, the number of followers, etc. As a result, the recording unit can improve the accuracy of the recorded content by recording based on the user's social media activity. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the user's social media activity into the generation AI, and the generation AI generates the recorded content.

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

[0061] The conversation buddy system may further include a feedback unit. The feedback unit collects feedback provided by the user after a conversation with the conversation buddy system and transmits it to the analysis unit. For example, a questionnaire is provided in which the user evaluates his / her satisfaction with the conversation content, and the analysis unit receives the results and reflects them in the generation of the next conversation content. The feedback unit may also evaluate the degree of interest the user showed in a particular conversation topic and transmit the information to the recording unit. This allows the conversation buddy system to more precisely adjust the conversation content based on the user's feedback and improve the user's satisfaction.

[0062] The reception unit can monitor the user's input speed and frequency of input errors when inputting the user's basic information, and provide input assistance. For example, if the user is taking a long time to input information, the reception unit can provide an auto-completion function to enable the user to input information quickly. Also, if the user frequently makes input errors, the reception unit can display an error message and guide the user to a correct input method. Furthermore, if the user is having difficulty inputting information, the reception unit can suggest an input method using voice input or image recognition. This allows the reception unit to improve the user's input experience and support the user in inputting basic information accurately.

[0063] When analyzing the user's basic information, the analysis unit can refer to the user's past behavioral history and purchase history. For example, the analysis unit can identify the user's interests and concerns based on information about products and services the user has purchased in the past. The analysis unit can also identify the user's personality and preferences in more detail based on information about events and activities the user has participated in in the past. Furthermore, the analysis unit can analyze the user's past online activities and search history to identify the user's current interests. This allows the analysis unit to more accurately identify the user's individuality and character based on multifaceted information about the user.

[0064] The generation unit can take into consideration the user's current health condition and mood when generating conversation content that matches the user's personality and character. For example, if the user is in good health, active and energetic conversation content can be generated. Also, if the user is tired, calm conversation content that helps the user relax can be generated. Furthermore, if the user is feeling stressed, the generation unit can generate conversation content about advice that helps relieve stress and relaxation methods. In this way, the generation unit can provide conversation content that matches the user's health condition and mood, and provide support that matches the user's mental and physical condition.

[0065] When the conversation unit converses with the user based on the generated conversation content, the conversation unit can analyze the user's speaking speed and tone of voice and adjust the progress of the conversation. For example, if the user speaks slowly, the conversation unit can proceed with the conversation at a slow pace. Also, if the user is excited, the conversation unit can proceed with the conversation in a lively tone. Furthermore, if the user is tired, the conversation unit can proceed with the conversation in a calm and subdued tone. In this way, the conversation unit can proceed with the conversation in accordance with the user's speaking style, providing a more natural and comfortable conversation experience.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit inputs basic information of a user. The basic information of a user includes, for example, age, sex, hobbies, etc. The reception unit receives information such as age, sex, hobbies, etc. input by the user. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis unit identifies the user's personality and character based on information such as the user's age, gender, and hobbies. Step 3: The generation unit uses the generation AI to generate conversation content based on the information analyzed by the analysis unit. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that will help the user relax. Step 4: The conversation unit converses with the user based on the conversation content generated by the generation unit. The conversation unit converses naturally with the user based on the generated conversation content. Step 5: The recording unit records the conversation content carried out by the conversation unit. The recording unit records the conversation history with the user and adjusts the next conversation content.

[0068] (Example 2) The conversation buddy system according to the embodiment of the present invention is a system that uses a generation AI to create a chat robot that serves as a conversation buddy that matches the personality and character of the conversation partner, thereby relieving the other person's sense of loneliness. In the conversation buddy system, a user starts a conversation with a chat robot and inputs basic information (age, gender, hobbies, etc.). Next, the generation AI analyzes the user's input information and generates conversation content that matches the personality and character. For example, for a user whose hobby is watching movies, the system provides the latest movie information and conversations about recommended movies. Also, for an introverted user, the system provides a calm conversation that allows the user to relax. Furthermore, the conversation buddy system records the user's conversation history and adjusts the next conversation content based on the past conversation content. This makes the conversation with the user more natural and familiar. For example, a topic that the user showed interest in in the previous conversation can be brought up again in the next conversation. This mechanism allows the user to feel as if they always have a conversation partner without feeling lonely. For example, it is expected to be a source of emotional support for elderly people living alone and people who are socially isolated. As a result, the conversation buddy system can relieve the user's sense of loneliness and provide emotional support.

[0069] The conversation buddy system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a conversation unit, and a recording unit. The reception unit inputs basic information of the user. The basic information of the user includes, for example, age, gender, hobbies, etc., but is not limited to such examples. The reception unit receives, for example, information such as age, gender, and hobbies input by the user. The analysis unit analyzes the information input by the reception unit using a generation AI. The analysis unit identifies the user's individuality and character based on, for example, information such as the user's age, gender, and hobbies. The generation unit generates conversation content based on the information analyzed by the analysis unit using the generation AI. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that allows the user to relax. The conversation unit has a conversation with the user based on the conversation content generated by the generation unit. For example, the conversation unit has a natural conversation with the user based on the generated conversation content. The recording unit records the conversation content conducted by the conversation unit. The recording unit, for example, records a conversation history with the user and adjusts the content of the next conversation. In this way, the conversation buddy system can generate conversation content that matches the user's personality and character based on the user's basic information, thereby eliminating feelings of loneliness.

[0070] The reception unit inputs basic information of the user. The basic information of the user includes, but is not limited to, for example, age, sex, and hobbies. The reception unit receives information such as age, sex, and hobbies input by the user. Specifically, the reception unit provides an interface for efficiently collecting information input by the user. The interface is designed to allow the user to easily input information, and may use, for example, a drop-down menu, a check box, or a text box. The information input by the user is stored in a database in real time so that the subsequent analysis unit and generation unit can access it. Furthermore, the reception unit has a validation function for preventing input errors by the user, and for example, checks whether the age is within an appropriate range and whether the gender is correctly selected. In addition, the reception unit also has a security function for protecting the privacy of the user, and the input information is encrypted and stored. As a result, the reception unit can accurately and safely collect the basic information of the user and improve the reliability of the entire system.

[0071] The analysis unit uses the generation AI to analyze the information input by the reception unit. The analysis unit identifies the user's personality and character based on information such as the user's age, gender, and hobbies. Specifically, the analysis unit utilizes the natural language processing technology of the generation AI to analyze the user's input information in detail. For example, if the user's hobby is watching movies, the analysis unit infers the user's past movie-watching history and favorite genres, and delves into the user's interest in movies. In addition, the analysis unit analyzes general behavior patterns and trends of interests based on age and gender, and infers the user's personality and lifestyle. The analysis unit integrates these pieces of information to build a model for more accurately identifying the user's personality and character. Furthermore, the analysis unit can perform more accurate analysis by utilizing not only the user's input information, but also past conversation history and data from other users. As a result, the analysis unit can grasp the user's personality and character in detail, and provide a foundation for the generation unit to generate appropriate conversation content.

[0072] The generation unit uses the generation AI to generate conversation content based on the information analyzed by the analysis unit. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that relaxes the user. Specifically, the generation unit utilizes the natural language generation technology of the generation AI to automatically generate conversation content that matches the user's interests and personality. For example, if the user's hobby is watching movies, the generation unit provides the latest movie information and reviews of movies that the user may be interested in. In addition, if the user is introverted, the generation unit advances the conversation in a calm tone that relaxes the user. The generation unit can also analyze the user's reactions and feedback in real time and dynamically adjust the conversation content. This allows the generation unit to provide the user with an optimal conversation experience and improve the user's satisfaction. Furthermore, the generation unit can track changes in the user's interests and concerns based on past conversation history and continuously improve the conversation content. This allows the generation unit to always provide the latest information and conversation content that meets the user's needs, thereby building a long-term relationship with the user.

[0073] The conversation unit converses with the user based on the conversation content generated by the generation unit. The conversation unit converses with the user naturally based on the generated conversation content, for example. Specifically, the conversation unit conveys the conversation content provided by the generation unit to the user using a voice synthesis technology or a text display technology. When using the voice synthesis technology, the conversation unit generates a voice with natural pronunciation and intonation and converses with the user in real time. When using the text display technology, the conversation unit displays the generated conversation content on the screen and provides it in a format that is easy for the user to read. Furthermore, the conversation unit analyzes the user's reaction and input in real time and feeds back an appropriate response to the generation unit. This allows the conversation unit to smoothly proceed with the interaction with the user and provide a natural conversation experience. In addition, the conversation unit can also analyze non-verbal information such as the user's facial expression and tone of voice, and dynamically adjust the conversation content. This allows the conversation unit to flexibly respond according to the user's emotions and state, and provide a more human-like conversation experience.

[0074] The recording unit records the contents of the conversation carried out by the conversation unit. For example, the recording unit records the conversation history with the user and adjusts the contents of the next conversation. Specifically, the recording unit records the contents of the conversation, the user's reactions, feedback, etc. in detail and stores them in a database. As a result, the recording unit can track changes in the user's interests and concerns based on the past conversation history and adjust the contents of the next conversation more appropriately. Furthermore, the recording unit can analyze the user's feedback and identify improvements to the conversation contents and new interests. For example, if the user shows a strong interest in a specific topic, the recording unit stores the information and takes up the topic in the next conversation. In addition, the recording unit has a security function for protecting the user's privacy, and the recorded data is encrypted and stored. As a result, the recording unit can safely manage the user's conversation history and improve the reliability of the entire system. Furthermore, the recording unit can perform statistical analysis based on the conversation history to evaluate the performance of the entire system and the satisfaction of the user. As a result, the recording unit can support continuous improvement and optimization of the system and provide the user with a better conversation experience.

[0075] The reception unit can input basic information such as the user's age, gender, and hobbies. The reception unit inputs basic information such as the user's age, gender, and hobbies. Specific ranges and formats of age include, for example, years, months, and numerical formats. Specific classifications and formats of gender include, for example, male, female, and other options. Specific contents and ranges of hobbies include, for example, sports, reading, and music. This allows the reception unit to accurately input the user's basic information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, when inputting the user's basic information, the reception unit can analyze the input contents using a generation AI to improve the accuracy of the input.

[0076] The analysis unit can analyze the basic information of the user and identify the individuality and character. The analysis unit uses the generation AI to analyze the basic information of the user. The analysis unit identifies the individuality and character of the user based on information such as the user's age, sex, and hobbies. Specific definitions and identification methods of individuality include, for example, personality diagnostic tests and behavior pattern analysis. Specific definitions and identification methods of personality include, for example, the Big Five personality traits and MBTI (Myers-Briggs Type Indicator). This allows the analysis unit to obtain basic information for generating conversation content that matches the individuality and character of the user. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the basic information of the user to the generation AI, and the generation AI identifies the individuality and character.

[0077] The generation unit can generate conversation content that matches the user's personality and character based on the analyzed information. The generation unit uses a generation AI to generate conversation content based on the analyzed information. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that relaxes the user. Specific generation methods and standards for conversation content that matches the user's personality and character include, for example, selecting topics based on the user's interests and concerns. This allows the generation unit to generate conversation content that matches the user's personality and character. Part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the analyzed information into the generation AI, and the generation AI generates the conversation content.

[0078] The conversation unit can have a conversation with the user based on the generated conversation content. The conversation unit has a conversation with the user based on the conversation content generated by the generation unit. The conversation unit, for example, has a natural conversation with the user based on the generated conversation content. Specific standards and methods for a natural conversation include, for example, the flow of dialogue and the timing of responses. This allows the conversation unit to have a natural conversation with the user based on the generated conversation content. A part or all of the above-mentioned processing in the conversation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the conversation unit inputs the generated conversation content to the generation AI, and the generation AI supports the progress of the conversation.

[0079] The recording unit can record the conversation history and adjust the next conversation content. The recording unit records the conversation content performed by the conversation unit. The recording unit records, for example, the conversation history with the user and adjusts the next conversation content. Specific contents and recording methods of the conversation history include, for example, text format, audio format, etc. Specific methods and standards of adjustment include, for example, parameter adjustment of an algorithm, a feedback loop, etc. As a result, the recording unit can realize a more natural conversation by adjusting the next conversation content based on the conversation history. A part or all of the above-mentioned processing in the recording unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the recording unit inputs the conversation history to a generation AI, and the generation AI adjusts the next conversation content.

[0080] The reception unit can estimate the user's emotion and adjust the input method of the basic information based on the estimated user's emotion. The reception unit uses the generation AI to estimate the user's emotion and adjust the input method of the basic information based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the basic information to be input quickly. Specific methods and criteria for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and criteria for adjusting the input method include, for example, changing the input interface, changing the order of input items, and the like. In this way, the reception unit can reduce the burden on the user by providing an input method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using 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 reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion to the generation AI, which in turn estimates the emotion and adjusts the input method.

[0081] The reception unit can analyze the user's past basic information input history and select the optimal input method. The reception unit uses the generation AI to analyze the user's past basic information input history and select the optimal input method. The reception unit, for example, automatically displays information that the user frequently inputs in the past as a candidate. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information to be used in a specific time period from the user's past input history. Specific contents and recording methods of the past basic information input history include, for example, the input date and time, the input contents, etc. Specific criteria and selection methods for an appropriate input method include, for example, the user's input speed, error rate, etc. As a result, the reception unit can improve input efficiency by providing the optimal input method based on the past input history. Part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the past input history to the generation AI, and the generation AI selects the optimal input method.

[0082] The reception unit can customize the input items based on the user's current living situation and field of interest when inputting the basic information. The reception unit uses the generation AI to customize the input items based on the user's current living situation and field of interest when inputting the basic information. For example, if the user has a hobby of watching movies, the reception unit adds input items related to movies. In addition, if the user has a pet, the reception unit can also add input items related to the pet. In addition, if the user likes traveling, the reception unit can also add input items related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the reception unit can collect more appropriate information by providing input items according to the user's living situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's living situation and field of interest to the generation AI, and the generation AI customizes the input items.

[0083] The reception unit can estimate the user's emotion and determine the priority of the basic information to be input based on the estimated user's emotion. The reception unit uses the generation AI to estimate the user's emotion and determine the priority of the basic information to be input based on the estimated user's emotion. For example, when the user is nervous, the reception unit prompts the user to input information in order of the most important information. Furthermore, when the user is relaxed, the reception unit can also preferentially input detailed information. Furthermore, when the user is in a hurry, the reception unit can also preferentially input only the minimum amount of information. Specific methods and criteria for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and criteria for determining the priority include, for example, importance score, urgency, and the like. As a result, the reception unit can reduce the burden on the user by inputting basic information in the priority order according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using 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 reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotions into the generation AI, which then estimates the emotions and determines the priority of the input.

[0084] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting basic information. The reception unit uses the generation AI to preferentially input information with high relevance based on the geographical location information of the user when inputting basic information. For example, when the user lives in a specific area, the reception unit allows information related to the area to be preferentially input. In addition, when the user is traveling, the reception unit can also allow information related to the current location to be preferentially input. Furthermore, when the user plans to move, the reception unit can also allow information related to the new address to be preferentially input. Specific acquisition methods and ranges of geographical location information include, for example, GPS data, IP addresses, and the like. Specific criteria and selection methods for highly relevant information include, for example, geographical proximity, cultural relevance, and the like. As a result, the reception unit can collect more appropriate information by preferentially inputting information based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the geographical location information of the user to the generation AI, and the generation AI selects highly relevant information.

[0085] The reception unit can analyze the user's social media activity and input related information when the basic information is input. The reception unit uses the generation AI to analyze the user's social media activity and input related information when the basic information is input. The reception unit, for example, suggests related input items based on the content that the user frequently posts on social media. The reception unit can also analyze the user's social media friendships and input related information. Furthermore, the reception unit can input information related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activity include, for example, the post content, the number of likes, the number of followers, etc. As a result, the reception unit can collect more appropriate information by inputting information based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the user's social media activity to the generation AI, and the generation AI selects related information.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit uses the generation AI to estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. For example, when the user is nervous, the analysis unit uses a simple analysis method and displays the result in an easy-to-understand manner. In addition, when the user is relaxed, the analysis unit can also use a detailed analysis method and display the result in detail. Furthermore, when the user is in a hurry, the analysis unit can perform the analysis quickly and display the result concisely. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and standards for adjusting the analysis method include, for example, selection of an algorithm, adjustment of analysis parameters, and the like. Thereby, the analysis unit can provide an analysis method according to the user's emotion, thereby making it easier to understand the analysis result. The estimation of emotion is realized using an emotion estimation function using 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 analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotions to the generation AI, which in turn estimates the emotions and adjusts the analysis method.

[0087] The analysis unit can optimize the analysis algorithm based on the user's past basic information at the time of analysis. The analysis unit optimizes the analysis algorithm based on the user's past basic information at the time of analysis using the generation AI. The analysis unit adjusts the analysis algorithm, for example, based on basic information input by the user in the past. The analysis unit can also analyze the user's past input history and select the optimal analysis method. Furthermore, the analysis unit can improve the accuracy of the analysis result by referring to the user's past basic information. Specific contents and recording methods of the past basic information include, for example, the input date and time, the input contents, etc. Specific methods and standards for optimizing the analysis algorithm include, for example, parameter adjustment, feedback loop, etc. As a result, the analysis unit can improve the accuracy of the analysis result by optimizing the analysis algorithm based on the past basic information. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the past basic information to the generation AI, and the generation AI optimizes the analysis algorithm.

[0088] The analysis unit can improve the accuracy of the analysis based on the user's living situation and field of interest at the time of analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis based on the user's living situation and field of interest at the time of analysis. For example, if the user enjoys watching movies, the analysis unit preferentially analyzes information related to movies. In addition, if the user has a pet, the analysis unit can also preferentially analyze information related to pets. In addition, if the user likes traveling, the analysis unit can also preferentially analyze information related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the analysis unit can improve the accuracy of the analysis result by performing an analysis based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's living situation and field of interest into the generation AI, and the generation AI improves the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit uses the generation AI to estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. In addition, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and standards for adjusting the display method include, for example, changing the UI, changing the display order, and the like. Thereby, the analysis unit can facilitate understanding of the analysis result by providing a display method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using 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 analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's emotions into the generation AI, which then infers the emotions and adjusts the display method.

[0090] The analysis unit can perform analysis based on the geographical location information of the user at the time of analysis. The analysis unit uses the generation AI to perform analysis based on the geographical location information of the user at the time of analysis. For example, if the user lives in a specific area, the analysis unit preferentially analyzes information related to that area. In addition, if the user is traveling, the analysis unit can also preferentially analyze information related to the current location. Furthermore, if the user is planning to move, the analysis unit can also preferentially analyze information related to the new address. Specific acquisition methods and ranges of geographical location information include, for example, GPS data, IP addresses, etc. As a result, the analysis unit can obtain more appropriate analysis results by performing analysis based on the geographical location information of the user. Part or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the geographical location information of the user to the generation AI, and the generation AI performs the analysis.

[0091] The analysis unit can improve the accuracy of the analysis based on the user's social media activity during the analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis based on the user's social media activity during the analysis. The analysis unit, for example, preferentially analyzes related information based on the content frequently posted by the user on social media. The analysis unit can also analyze the user's social media friendships and preferentially analyze related information. Furthermore, the analysis unit can preferentially analyze information related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activities include, for example, the posted content, the number of likes, the number of followers, etc. As a result, the analysis unit can improve the accuracy of the analysis results by performing an analysis based on the user's social media activity. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's social media activity into the generation AI, and the generation AI improves the accuracy of the analysis.

[0092] The generation unit can estimate the user's emotion and adjust the generation method of the conversation content based on the estimated user's emotion. The generation unit uses the generation AI to estimate the user's emotion and adjust the generation method of the conversation content based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a calm and relaxing conversation content. In addition, when the user is excited, the generation unit can also generate lively conversation content. Furthermore, when the user is sad, the generation unit can generate a comforting conversation content. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and standards for adjusting the generation method include, for example, selection of an algorithm, adjustment of generation parameters, and the like. As a result, the generation unit can realize a more natural conversation by generating a conversation content according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotions into the generation AI, which then estimates the emotions and adjusts the way the conversation content is generated.

[0093] The generation unit can optimize the generation algorithm by referring to the user's past conversation history when generating the conversation content. The generation unit optimizes the generation algorithm by referring to the user's past conversation history when generating the conversation content using the generation AI. The generation unit generates the conversation content based on, for example, topics in which the user has shown interest in the past. The generation unit can also analyze the user's past conversation history and generate optimal conversation content. Furthermore, the generation unit can smooth the flow of the conversation by referring to the user's past conversation history. Specific contents and recording methods of the past conversation history include, for example, text format and audio format. Specific methods and standards for optimizing the generation algorithm include, for example, parameter adjustment and feedback loop. As a result, the generation unit can smooth the flow of the conversation by generating optimal conversation content based on the past conversation history. Part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the past conversation history into the generation AI, and the generation AI optimizes the generation algorithm.

[0094] The generation unit can improve the accuracy of generation based on the user's living situation and field of interest when generating the conversation content. The generation unit uses the generation AI to improve the accuracy of generation based on the user's living situation and field of interest when generating the conversation content. For example, if the user enjoys watching movies, the generation unit generates a conversation content related to movies. In addition, if the user has a pet, the generation unit can also generate a conversation content related to the pet. Furthermore, if the user likes traveling, the generation unit can generate a conversation content related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the generation unit can provide a more appropriate conversation by generating a conversation content based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's living situation and field of interest into the generation AI, and the generation AI improves the accuracy of generation.

[0095] The generation unit can estimate the user's emotion and determine the priority of the conversation content to be generated based on the estimated user's emotion. The generation unit uses the generation AI to estimate the user's emotion and determine the priority of the conversation content to be generated based on the estimated user's emotion. For example, when the user is nervous, the generation unit preferentially generates conversation content that can relax the user. In addition, when the user is relaxed, the generation unit can also preferentially generate interesting topics. Furthermore, when the user is in a hurry, the generation unit can also preferentially generate conversation content that covers the main points. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and standards for determining the priority include, for example, importance score, urgency, and the like. As a result, the generation unit can reduce the burden on the user by generating conversation content in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using 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 processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's emotions to the generation AI, which in turn estimates the emotions and determines the priority of the conversation contents.

[0096] The generation unit can generate the conversation content based on the geographical location information of the user when generating the conversation content. The generation unit uses the generation AI to generate the conversation content based on the geographical location information of the user when generating the conversation content. For example, if the user lives in a specific area, the generation unit generates the conversation content related to the area. In addition, if the user is traveling, the generation unit can also generate the conversation content related to the current location. Furthermore, if the user is planning to move, the generation unit can generate the conversation content related to the new address. Specific acquisition methods and ranges of the geographical location information include, for example, GPS data, IP address, etc. As a result, the generation unit can provide a more appropriate conversation by generating the conversation content based on the geographical location information of the user. Part or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the geographical location information of the user to the generation AI, and the generation AI generates the conversation content.

[0097] The generation unit can improve the accuracy of generation based on the user's social media activity when generating the conversation content. The generation unit uses the generation AI to improve the accuracy of generation based on the user's social media activity when generating the conversation content. The generation unit generates related conversation content, for example, based on the content that the user frequently posts on social media. The generation unit can also analyze the user's social media friendships and generate related conversation content. Furthermore, the generation unit can generate conversation content related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activity include, for example, the post content, the number of likes, the number of followers, etc. As a result, the generation unit can improve the accuracy of the conversation by generating conversation content based on the user's social media activity. Part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's social media activity into the generation AI, and the generation AI generates the conversation content.

[0098] The conversation unit can estimate the user's emotion and adjust the conversation progress method based on the estimated user's emotion. The conversation unit uses the generation AI to estimate the user's emotion and adjust the conversation progress method based on the estimated user's emotion. For example, when the user is nervous, the conversation unit progresses a calm and relaxing conversation. Also, when the user is relaxed, the conversation unit can provide an interesting topic and progress the conversation. Furthermore, when the user is in a hurry, the conversation unit can progress the conversation by focusing on the main points. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc. Specific methods and standards for adjusting the progress method include, for example, the tempo of the conversation, selection of a topic, etc. As a result, the conversation unit can realize a more natural conversation by providing a conversation progress method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using 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 is performed using a generation AI. For example, the conversation unit inputs the user's emotions to the generation AI, which in turn estimates the emotions and adjusts the way the conversation proceeds.

[0099] The conversation unit can optimize the progress of the conversation by referring to the user's past conversation history during the conversation. The conversation unit optimizes the progress of the conversation by referring to the user's past conversation history using the generation AI during the conversation. The conversation unit progresses the conversation, for example, based on topics in which the user has shown interest in the past. The conversation unit can also analyze the user's past conversation history and select an optimal way to progress the conversation. Furthermore, the conversation unit can smooth the flow of the conversation by referring to the user's past conversation history. Specific contents and recording methods of the past conversation history include, for example, text format and audio format. Specific methods and standards for optimizing the progress include, for example, selection of an algorithm and adjustment of progress parameters. As a result, the conversation unit can smooth the flow of the conversation by providing an optimal way to progress the conversation based on the past conversation history. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the past conversation history into the generation AI, and the generation AI optimizes the progress of the conversation.

[0100] The conversation unit can customize the content of the conversation based on the user's living situation and field of interest during the conversation. The conversation unit uses the generation AI to customize the content of the conversation based on the user's living situation and field of interest during the conversation. For example, if the user enjoys watching movies, the conversation unit provides topics related to movies. In addition, if the user has a pet, the conversation unit can also provide topics related to pets. In addition, if the user likes traveling, the conversation unit can also provide topics related to travel. Specific contents and ranges of the living situation include, for example, occupation, home environment, health condition, etc. Specific contents and ranges of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the conversation unit can realize a more appropriate conversation by providing conversation content based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the user's living situation and field of interest into the generation AI, and the generation AI customizes the content of the conversation.

[0101] The conversation unit can estimate the user's emotions and determine the priority of the conversation based on the estimated user's emotions. The conversation unit uses the generation AI to estimate the user's emotions and determine the priority of the conversation based on the estimated user's emotions. For example, when the user is nervous, the conversation unit preferentially provides a topic that can relax the user. In addition, when the user is relaxed, the conversation unit can also preferentially provide an interesting topic. Furthermore, when the user is in a hurry, the conversation unit can also preferentially provide a topic that covers the main points. Specific methods and criteria for estimating emotions include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and criteria for determining the priority include, for example, importance score, urgency, and the like. In this way, the conversation unit can reduce the burden on the user by providing the conversation content in the order of priority according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using 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 is performed using the generation AI. For example, the conversation unit inputs the user's emotions into the generation AI, which then infers the emotions and determines conversation priorities.

[0102] The conversation unit can conduct a conversation based on the geographical location information of the user during the conversation. The conversation unit uses the generation AI to conduct a conversation based on the geographical location information of the user during the conversation. For example, if the user lives in a specific area, the conversation unit provides a topic related to the area. In addition, if the user is traveling, the conversation unit can also provide a topic related to the current location. Furthermore, if the user is planning to move, the conversation unit can also provide a topic related to the new address. Specific acquisition methods and ranges of the geographical location information include, for example, GPS data, IP addresses, and the like. As a result, the conversation unit can realize a more appropriate conversation by providing a conversation content based on the geographical location information of the user. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the geographical location information of the user to the generation AI, and the generation AI generates the conversation content.

[0103] The conversation unit can adjust the content of the conversation based on the user's social media activity during the conversation. The conversation unit adjusts the content of the conversation based on the user's social media activity using the generation AI during the conversation. The conversation unit provides related topics based on, for example, the content frequently posted by the user on social media. The conversation unit can also analyze the user's social media friendships and provide related topics. Furthermore, the conversation unit can provide topics related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activities include, for example, the posted content, the number of likes, the number of followers, etc. As a result, the conversation unit can improve the accuracy of the conversation by providing the conversation content based on the user's social media activity. Part or all of the above-mentioned processing in the conversation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversation unit inputs the user's social media activity into the generation AI, and the generation AI generates the conversation content.

[0104] The recording unit can estimate the user's emotion and adjust the recording method based on the estimated user's emotion. The recording unit uses the generation AI to estimate the user's emotion and adjust the recording method based on the estimated user's emotion. For example, when the user is nervous, the recording unit provides a simple and highly visible recording method. Furthermore, when the user is relaxed, the recording unit can also provide a recording method including detailed information. Furthermore, when the user is in a hurry, the recording unit can also provide a recording method that focuses on the main points. Specific methods and standards for estimating emotions include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and standards for adjusting the recording method include, for example, changing the UI, changing the display order, and the like. Thereby, the recording unit can facilitate understanding of the recorded content by providing a recording method according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using 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 recording unit is performed using the generation AI. For example, the recording unit inputs the user's emotions into the generation AI, which then estimates the emotions and adjusts the recording method.

[0105] The recording unit can optimize the recording algorithm by referring to the user's past conversation history at the time of recording. The recording unit optimizes the recording algorithm by referring to the user's past conversation history using the generation AI at the time of recording. The recording unit adjusts the recording content based on, for example, topics in which the user has shown interest in the past. The recording unit can also analyze the user's past conversation history and select an optimal recording method. Furthermore, the recording unit can improve the accuracy of the recording by referring to the user's past conversation history. Specific contents and recording methods of the past conversation history include, for example, text format and audio format. Specific methods and standards for optimizing the recording algorithm include, for example, parameter adjustment and feedback loop. As a result, the recording unit can improve the accuracy of the recording by providing an optimal recording method based on the past conversation history. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the past conversation history into the generation AI, and the generation AI optimizes the recording algorithm.

[0106] The recording unit can improve the accuracy of the recording based on the user's living situation and field of interest at the time of recording. The recording unit uses the generation AI to improve the accuracy of the recording based on the user's living situation and field of interest at the time of recording. For example, if the user has a hobby of watching movies, the recording unit preferentially records the content of the conversation related to movies. In addition, if the user has a pet, the recording unit can also preferentially record the content of the conversation related to the pet. In addition, if the user likes traveling, the recording unit can also preferentially record the content of the conversation related to travel. The specific content and range of the living situation include, for example, occupation, home environment, health condition, etc. The specific content and range of the field of interest include, for example, hobbies, research themes, news topics, etc. As a result, the recording unit can improve the accuracy of the recorded content by performing recording based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the user's living situation and field of interest into the generation AI, and the generation AI improves the accuracy of the recording.

[0107] The recording unit can estimate the user's emotion and determine the priority of the contents to be recorded based on the estimated user's emotion. The recording unit uses the generation AI to estimate the user's emotion and determine the priority of the contents to be recorded based on the estimated user's emotion. For example, when the user is nervous, the recording unit preferentially records topics that can relax the user. In addition, when the user is relaxed, the recording unit can also preferentially record interesting topics. Furthermore, when the user is in a hurry, the recording unit can also preferentially record topics that focus on the main points. Specific methods and criteria for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, and the like. Specific methods and criteria for determining the priority include, for example, importance score, urgency, and the like. This allows the recording unit to improve the efficiency of recording by providing the recorded content in the order of priority according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using 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 processes in the recording unit are performed using the generation AI. For example, the recording unit inputs the user's emotions to the generation AI, which in turn estimates the emotions and determines the priority of the recorded contents.

[0108] The recording unit can perform recording based on the geographical location information of the user at the time of recording. The recording unit performs recording based on the geographical location information of the user using the generation AI at the time of recording. For example, if the user lives in a specific area, the recording unit preferentially records conversation content related to that area. In addition, if the user is traveling, the recording unit can also preferentially record conversation content related to the current location. Furthermore, if the user is planning to move, the recording unit can also preferentially record conversation content related to the new address. Specific acquisition methods and ranges of geographical location information include, for example, GPS data, IP address, etc. As a result, the recording unit can provide more appropriate recorded content by performing recording based on the geographical location information of the user. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the geographical location information of the user to the generation AI, and the generation AI generates the recorded content.

[0109] The recording unit can improve the accuracy of the recording based on the user's social media activity at the time of recording. The recording unit uses the generation AI to improve the accuracy of the recording based on the user's social media activity at the time of recording. For example, the recording unit preferentially records related conversation content based on the content frequently posted by the user on social media. The recording unit can also analyze the user's social media friendships and preferentially record related conversation content. Furthermore, the recording unit can preferentially record conversation content related to the user's interests based on the user's social media activity history. Specific content and analysis methods of social media activity include, for example, the posting content, the number of likes, the number of followers, etc. As a result, the recording unit can improve the accuracy of the recorded content by recording based on the user's social media activity. Part or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the user's social media activity into the generation AI, and the generation AI generates the recorded content.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The conversation buddy system may further include a feedback unit. The feedback unit collects feedback provided by the user after a conversation with the conversation buddy system and transmits it to the analysis unit. For example, a questionnaire is provided in which the user evaluates his / her satisfaction with the conversation content, and the analysis unit receives the results and reflects them in the generation of the next conversation content. The feedback unit may also evaluate the degree of interest the user showed in a particular conversation topic and transmit the information to the recording unit. This allows the conversation buddy system to more precisely adjust the conversation content based on the user's feedback and improve the user's satisfaction.

[0112] The reception unit can monitor the user's input speed and frequency of input errors when inputting the user's basic information, and provide input assistance. For example, if the user is taking a long time to input information, the reception unit can provide an auto-completion function to enable the user to input information quickly. Also, if the user frequently makes input errors, the reception unit can display an error message and guide the user to a correct input method. Furthermore, if the user is having difficulty inputting information, the reception unit can suggest an input method using voice input or image recognition. This allows the reception unit to improve the user's input experience and support the user in inputting basic information accurately.

[0113] When analyzing the user's basic information, the analysis unit can refer to the user's past behavioral history and purchase history. For example, the analysis unit can identify the user's interests and concerns based on information about products and services the user has purchased in the past. The analysis unit can also identify the user's personality and preferences in more detail based on information about events and activities the user has participated in in the past. Furthermore, the analysis unit can analyze the user's past online activities and search history to identify the user's current interests. This allows the analysis unit to more accurately identify the user's individuality and character based on multifaceted information about the user.

[0114] The generation unit can take into consideration the user's current health condition and mood when generating conversation content that matches the user's personality and character. For example, if the user is in good health, active and energetic conversation content can be generated. Also, if the user is tired, calm conversation content that helps the user relax can be generated. Furthermore, if the user is feeling stressed, the generation unit can generate conversation content about advice that helps relieve stress and relaxation methods. In this way, the generation unit can provide conversation content that matches the user's health condition and mood, and provide support that matches the user's mental and physical condition.

[0115] When the conversation unit converses with the user based on the generated conversation content, the conversation unit can analyze the user's speaking speed and tone of voice and adjust the progress of the conversation. For example, if the user speaks slowly, the conversation unit can proceed with the conversation at a slow pace. Also, if the user is excited, the conversation unit can proceed with the conversation in a lively tone. Furthermore, if the user is tired, the conversation unit can proceed with the conversation in a calm and subdued tone. In this way, the conversation unit can proceed with the conversation in accordance with the user's speaking style, providing a more natural and comfortable conversation experience.

[0116] When recording the conversation history, the recording unit can estimate the user's emotions and classify the recorded content based on the estimated emotions. For example, if the user feels joy during a conversation, the conversation content can be classified as a "positive conversation." Also, if the user feels anxiety during a conversation, the conversation content can be classified as a "negative conversation." Furthermore, if the user shows interest during a conversation, the conversation content can be classified as an "interesting conversation." In this way, the recording unit can classify the conversation history based on the user's emotions and use the classification to generate the next conversation content.

[0117] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated emotion. For example, if the user is nervous, a simple and intuitive design can be provided to reduce the burden of input. Also, if the user is relaxed, customizable detailed input options can be provided. Furthermore, if the user is in a hurry, a design that prioritizes voice input or touch operation to enable quick input can be provided. In this way, the reception unit can provide an input interface according to the user's emotion and improve the user's input experience.

[0118] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the analysis unit can make it easier to understand the analysis results by providing a display method according to the user's emotions.

[0119] The generation unit can estimate the user's emotion and adjust the generation method of the conversation content based on the estimated emotion. For example, when the user is relaxed, calm and relaxing conversation content can be generated. When the user is excited, lively conversation content can be generated. Furthermore, when the user is sad, comforting conversation content can be generated. In this way, the generation unit can realize a more natural conversation by generating conversation content according to the user's emotion.

[0120] The conversation unit can estimate the user's emotions and adjust the way the conversation proceeds based on the estimated emotions. For example, if the user is nervous, the conversation can proceed in a calm and relaxing manner. If the user is relaxed, the conversation can proceed by providing an interesting topic. Furthermore, if the user is in a hurry, the conversation can proceed by focusing on the main points. In this way, the conversation unit can realize a more natural conversation by providing a way to proceed with the conversation according to the user's emotions.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The reception unit inputs basic information of a user. The basic information of a user includes, for example, age, sex, hobbies, etc. The reception unit receives information such as age, sex, hobbies, etc. input by the user. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis unit identifies the user's personality and character based on information such as the user's age, gender, and hobbies. Step 3: The generation unit uses the generation AI to generate conversation content based on the information analyzed by the analysis unit. For example, if the user's hobby is watching movies, the generation unit generates conversation content about the latest movie information and recommended movies. In addition, if the user's personality is introverted, the generation unit can also generate calm conversation content that will help the user relax. Step 4: The conversation unit converses with the user based on the conversation content generated by the generation unit. The conversation unit converses naturally with the user based on the generated conversation content. Step 5: The recording unit records the conversation content carried out by the conversation unit. The recording unit records the conversation history with the user and adjusts the next conversation content.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, conversation unit, and recording unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and inputs basic information of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates conversation content based on the analyzed information. The conversation unit is realized, for example, by the output device 40 of the smart device 14, and has a conversation with the user based on the generated conversation content. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the conversation content. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, conversation unit, and recording unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and inputs basic information of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates conversation content based on the analyzed information. The conversation unit is realized, for example, by the speaker 240 of the smart glasses 214, and has a conversation with the user based on the generated conversation content. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the conversation content. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, conversation unit, and recording unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and inputs basic information of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates conversation content based on the analyzed information. The conversation unit is realized, for example, by the speaker 240 of the headset type terminal 314, and converses with the user based on the generated conversation content. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the conversation content. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, conversation unit, and recording unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and inputs basic information of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates conversation content based on the analyzed information. The conversation unit is realized, for example, by the speaker 240 of the robot 414, and converses with the user based on the generated conversation content. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the conversation content. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Appendix 1) A reception unit for inputting basic information of a user; an analysis unit that analyzes the information input by the reception unit; a generation unit that generates a conversation content based on the information analyzed by the analysis unit; a conversation unit that conducts a conversation based on the conversation content generated by the generation unit; A recording unit that records the contents of the conversation carried out by the conversation unit. A system characterized by: (Appendix 2) The reception unit is Enter basic information about the user's age, gender, and hobbies 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze basic information about users to identify their personalities and characteristics 2. The system of claim 1, (Appendix 4) The generation unit is Based on the analyzed information, the content of the conversation is generated according to the user's personality and character. 2. The system of claim 1, (Appendix 5) The conversation unit includes: Have a conversation with the user based on the generated conversation content 2. The system of claim 1, (Appendix 6) The recording unit is Record conversation history and tailor your next conversation 2. The system of claim 1, (Appendix 7) The reception unit is Infer a user's emotions and adjust the input method of basic information based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past basic information input history and select the appropriate input method 2. The system of claim 1, (Appendix 9) The reception unit is When entering basic information, customize the input fields based on the user's current life situation and interests 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize basic information to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When entering basic information, prioritize the most relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is When you enter your basic information, analyze your social media activity and enter relevant information 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust analysis methods based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the analysis algorithm is optimized based on the user's basic past information. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the analysis is refined based on the user's life circumstances and interests 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is At analysis time, the analysis is based on the user's geographic location. 2. The system of claim 1, (Appendix 18) The analysis unit is During analytics, improve analytics accuracy based on your social media activity 2. The system of claim 1, (Appendix 19) The generation unit is Estimate the user's emotions and adjust the method of generating conversation content based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The generation unit is When generating conversation content, the generation algorithm is optimized by referring to the user's past conversation history. 2. The system of claim 1, (Appendix 21) The generation unit is When generating conversation content, the accuracy of generation is improved based on the user's life situation and areas of interest. 2. The system of claim 1, (Appendix 22) The generation unit is Estimate the user's emotions and prioritize the conversation content to be generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The generation unit is When generating conversation content, it is generated based on the user's geographic location information. 2. The system of claim 1, (Appendix 24) The generation unit is Improve conversation generation accuracy based on users' social media activity 2. The system of claim 1, (Appendix 25) The conversation unit includes: Infer the user's emotions and adjust the way the conversation proceeds based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The conversation unit includes: During a conversation, optimize the conversation by referring to the user's past conversation history 2. The system of claim 1, (Appendix 27) The conversation unit includes: During a conversation, tailor the content of the conversation based on the user's life situation and interests 2. The system of claim 1, (Appendix 28) The conversation unit includes: Estimate user emotions and prioritize conversations based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The conversation unit includes: During a conversation, the conversation is based on the user's geographic location 2. The system of claim 1, (Appendix 30) The conversation unit includes: During a conversation, tailor the content of the conversation based on the user's social media activity 2. The system of claim 1, (Appendix 31) The recording unit is To estimate a user's emotion and adjust a recording method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 32) The recording unit is When recording, the recording algorithm is optimized by referencing the user's past conversation history 2. The system of claim 1, (Appendix 33) The recording unit is When recording, the accuracy of the recording is improved based on the user's life situation and areas of interest. 2. The system of claim 1, (Appendix 34) The recording unit is Estimate the user's emotions and prioritize the contents to be recorded based on the estimated user's emotions. 2. The system of claim 1, (Appendix 35) The recording unit is When recording, the recording is based on the user's geographic location 2. The system of claim 1, (Appendix 36) The recording unit is When recording, improve the accuracy of the recording based on the user's social media activity 2. The system of claim 1, [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting basic information of a user; an analysis unit that analyzes the information input by the reception unit; a generation unit that generates a conversation content based on the information analyzed by the analysis unit; a conversation unit that conducts a conversation based on the conversation content generated by the generation unit; A recording unit that records the contents of the conversation carried out by the conversation unit. A system characterized by:

2. The reception unit is Enter basic information about the user's age, gender, and hobbies 2. The system of claim 1 .

3. The analysis unit is Analyze basic information about users to identify their personalities and characteristics 2. The system of claim 1 .

4. The generation unit is Based on the analyzed information, the content of the conversation is generated according to the user's personality and character.

2. The system of claim 1 .

5. The recording unit is Record conversation history and tailor your next conversation 2. The system of claim 1 .

6. The reception unit is Infer user emotions and adjust the input method of basic information based on the estimated user emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past basic information input history and select the appropriate input method 2. The system of claim 1 .

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