System

Abstract

An object of a system according to an embodiment is to interact with a stuffed toy based on a personality set by a user.SOLUTION: A system according to an embodiment includes a setting unit, a speech generation unit, a dialogue unit, a storage unit, and a provision unit. The setting unit allows a user to set a character of the stuffed toy. The conversation generation unit generates a conversation based on the personality set by the setting unit. The dialogue unit performs a dialogue on the basis of the conversation generated by the speech generation unit. The storage unit stores the personality set by the setting unit. The providing unit provides a result of the dialogue performed by the dialogue 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technology has the problem that interactions with stuffed toys are patterned and do not allow interactions that reflect the individual personality of the user as desired.

[0005] The system according to the embodiment aims to enable a user to interact with a stuffed toy based on a personality set by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a setting unit, a conversation generation unit, an interaction unit, a storage unit, and a providing unit. The setting unit allows a user to set the personality of a stuffed animal. The conversation generation unit generates a conversation based on the personality set by the setting unit. The interaction unit conducts a dialogue based on the conversation generated by the conversation generation unit. The storage unit stores the personality set by the setting unit. The providing unit provides the results of the dialogue conducted by the interaction unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to interact with a stuffed animal based on a personality set by the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A chatbot system according to an embodiment of the present invention allows a user to set a personality for a stuffed animal and engage in a dialogue based on that personality. The chatbot system provides an interface for the user to set the personality of the stuffed animal, generates a conversation based on the set personality, and engages in a dialogue. It also saves the set personality and provides the results of the dialogue. For example, the chatbot system provides an interface for the user to set the personality and speech style of the stuffed animal in detail. The chatbot then engages in a conversation based on the set personality. For example, if the user asks, "How was your day?", the stuffed animal can respond, "I had a great time today!" The chatbot system also provides a function for saving the personality set by the user, allowing the user to change the setting at any time. It also has a function for providing the results of the dialogue to the user. For example, the dialogue history can be saved and reviewed later. This allows stuffed animal lovers to develop a stronger attachment to their stuffed animals and can be used for psychological support, therapy, and education. For example, children's interactions with stuffed animals can be expected to improve their communication skills and reduce stress. Furthermore, as a form of therapy, patients can interact with stuffed animals to provide psychological care.

[0029] A chatbot system according to an embodiment includes a setting unit, a conversation generation unit, a dialogue unit, a storage unit, and a providing unit. The setting unit provides an interface for a user to set the personality of a stuffed animal. For example, the setting unit provides a graphical user interface for a user to set the personality and speech style of the stuffed animal in detail. The setting unit also allows the user to set the personality by voice using a voice interface. The setting unit also has a function for saving the personality set by the user. The conversation generation unit generates a conversation based on the personality set by the user. For example, the conversation generation unit uses a generation AI to generate appropriate responses based on the personality set by the user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The dialogue unit engages in a dialogue based on the conversation generated by the conversation generation unit. For example, when the dialogue unit asks, "How was your day?", the stuffed animal responds, "I had a lot of fun today!" The storage unit saves the personality set by the user and allows the setting to be changed at any time. For example, the storage unit saves the personality set by the user in a database so that it can be changed later. The providing unit provides the result of the dialogue to the user. For example, the providing unit may store a history of the conversation so that the conversation can be checked later. As a result, the chatbot system according to the embodiment allows the user to set the personality of the stuffed animal and have a conversation based on that personality, which can be used for psychological support, therapy, and education.

[0030] The setting unit can provide an interface for the user to set the personality and speaking style of the stuffed animal. The interface includes, for example, a graphical user interface and a voice interface. The setting unit can provide, for example, a graphical user interface for the user to set the personality and speaking style of the stuffed animal in detail. The setting unit can also allow the user to set the personality by voice using a voice interface. For example, the setting unit can set the personality of the stuffed animal by the user inputting "kind personality" by voice. This allows the user to set the personality and speaking style of the stuffed animal in detail.

[0031] The conversation generation unit can generate responses based on the personality set by the user. The responses include, for example, the appropriateness of the response's writing style and content. The conversation generation unit, for example, uses a generation AI to generate appropriate responses based on the personality set by the user. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the conversation generation unit generates a response that answers the question "How was your day?" with "I had a lot of fun today!" based on the personality set by the user. The conversation generation unit can also use the generation AI to generate responses that include appropriate emotional expressions based on the personality set by the user. For example, the conversation generation unit uses the generation AI to generate responses that express emotions such as joy and sadness based on the personality set by the user. This makes it possible to generate appropriate responses based on the personality set by the user.

[0032] When a user asks, "How was your day?", the dialogue unit can generate an answer based on the stuffed animal's preset personality. The preset personality may include, for example, personality traits and setting procedures. For example, when a user asks, "How was your day?", the dialogue unit can have the stuffed animal reply, "I had a lot of fun today!" The dialogue unit can also use a generation AI to generate an answer based on the preset personality. For example, the dialogue unit uses a generation AI to generate an answer that includes an appropriate emotional expression based on the personality set by the user. This allows the user to enjoy natural dialogue with the stuffed animal.

[0033] The storage unit may store the personality set by the user and allow the user to change the setting at any time. The setting that can be changed at any time may include, for example, a change procedure and changeable items. The storage unit may store the personality set by the user in a database and allow the user to change the personality later. The storage unit may also provide an interface for storing the personality set by the user and allowing the user to change the personality at any time. For example, the storage unit may provide a button or menu for storing the personality set by the user and changing it later. This allows the user to store the personality set by the user and change it at any time.

[0034] The providing unit can save the dialogue history so that it can be checked later. The dialogue history includes, for example, the type of data to be saved and the storage period. The providing unit can, for example, save the dialogue history in a database so that it can be checked later. The providing unit can also provide an interface for saving the dialogue history and checking it later. For example, the providing unit can provide a button or menu for displaying the dialogue history. This allows the user to save the dialogue history and check it later.

[0035] The setting unit can analyze the user's past setting history and automatically suggest personality settings. The past setting history includes, for example, the type of data to be saved and the storage period. The setting unit, for example, analyzes the tendencies of personality settings that the user has previously set and suggests similar personalities. The setting unit can also use a generation AI to analyze the user's past setting history and automatically suggest personality settings. For example, the setting unit uses a generation AI to suggest optimal personality settings based on the user's preferred speaking style and tone in the past. This makes it possible to suggest optimal personality settings based on the user's past setting history.

[0036] The setting unit can select a setting means according to the user's input method at the time of setting. Input methods include, for example, voice input, text input, and image input. For example, when the user uses voice input, the setting unit sets the personality using voice recognition technology. The setting unit can also select a setting means according to the user's input method at the time of setting using a generation AI. For example, when the user uses text input, the setting unit uses a generation AI to set the personality using text analysis technology. This makes it possible to select the optimal setting means according to the user's input method.

[0037] The setting unit can propose highly relevant personality settings by taking into account the user's geographical location information during setup. Geographical location information includes, for example, GPS data and location information services. For example, if the user lives in a cold region, the setting unit proposes a stuffed animal with a warm personality. The setting unit can also propose highly relevant personality settings by using the generation AI during setup, taking into account the user's geographical location information. For example, if the user lives in an urban area, the setting unit can use the generation AI to propose a stuffed animal with an urban and sophisticated personality. This makes it possible to propose optimal personality settings based on the user's geographical location information.

[0038] The setting unit can analyze the user's social media activity at the time of setting and suggest related personality settings. Social media activity includes, for example, analyzing the content of posts and evaluating the frequency of activity. The setting unit can suggest personality settings based on, for example, words and expressions frequently used by the user on social media. The setting unit can also use a generation AI to analyze the user's social media activity at the time of setting and suggest related personality settings. For example, the setting unit can use a generation AI to analyze the trends of accounts followed by the user and suggest related personality settings. This makes it possible to suggest optimal personality settings based on the user's social media activity.

[0039] The setting unit can customize the setting method by reflecting the user's past feedback during setup. The past feedback includes, for example, the type of feedback and the storage period. The setting unit can improve the personality setting suggestions, for example, based on feedback provided by the user in the past. The setting unit can also customize the setting method by using the generation AI during setup by reflecting the user's past feedback. For example, the setting unit can use the generation AI to preferentially suggest setting methods that the user has preferred in the past. This makes it possible to suggest the optimal setting method based on the user's past feedback.

[0040] The conversation generation unit can adjust the level of detail of the conversation based on the personality of the stuffed animal when generating the conversation. The level of detail of the conversation includes, for example, detailed explanations and concise responses. For example, the conversation generation unit provides polite and detailed explanations for a stuffed animal with a kind personality. The conversation generation unit can also adjust the level of detail of the conversation based on the personality of the stuffed animal when generating the conversation using the generation AI. For example, the conversation generation unit uses the generation AI to provide concise and cheerful conversations for a stuffed animal with a lively personality. This makes it possible to adjust the level of detail of the conversation based on the personality of the stuffed animal.

[0041] The conversation generation unit can improve the accuracy of the conversation when generating a conversation by referring to the user's past conversation history. The past conversation history includes, for example, the type of data to be saved and the storage period. The conversation generation unit, for example, suggests related topics based on content that the user has spoken in the past. The conversation generation unit can also improve the accuracy of the conversation by using the generation AI to refer to the user's past conversation history when generating a conversation. For example, the conversation generation unit uses the generation AI to analyze preferred topics from the user's past conversation history and reflect them in the conversation. This improves the accuracy of the conversation based on the user's past conversation history.

[0042] The conversation generation unit can determine the priority of conversations based on the time of submission by the user when generating a conversation. The submission time includes, for example, the submission date and time and the frequency of submission. For example, when the user is in a hurry, the conversation generation unit provides important information preferentially. The conversation generation unit can also determine the priority of conversations based on the time of submission by the user when generating a conversation using the generation AI. For example, when the user is relaxed, the conversation generation unit provides detailed information using the generation AI. This makes it possible to determine the priority of conversations based on the time of submission by the user.

[0043] The conversation generation unit can adjust the order of conversations based on the user's relevance when generating a conversation. Relevance includes, for example, the degree of topic agreement and related keywords. The conversation generation unit, for example, prioritizes providing topics that interest the user. The conversation generation unit can also use the generation AI to adjust the order of conversations based on the user's relevance when generating a conversation. For example, the conversation generation unit uses the generation AI to prioritize providing topics related to content that the user has previously discussed. This makes it possible to adjust the order of conversations based on the user's relevance.

[0044] The conversation generation unit can adjust the use of technical terms in the conversation according to the user's level of expertise when generating the conversation. Expertise levels include, for example, knowledge tests and past experience. For example, if the user has expertise, the conversation generation unit provides a conversation that makes extensive use of technical terms. The conversation generation unit can also use the generation AI to adjust the use of technical terms in the conversation according to the user's level of expertise when generating the conversation. For example, if the user is a beginner, the conversation generation unit uses the generation AI to provide a conversation that explains things in simple terms. This makes it possible to adjust the use of technical terms in the conversation according to the user's level of expertise.

[0045] The dialogue unit can adjust the level of detail of the dialogue based on the personality of the stuffed animal during the dialogue. The level of detail of the dialogue includes, for example, detailed explanations and concise responses. For example, in the case of a stuffed animal with a kind personality, the dialogue unit provides a polite and detailed explanation. The dialogue unit can also use the generation AI to adjust the level of detail of the dialogue based on the personality of the stuffed animal during the dialogue. For example, in the case of a stuffed animal with a lively personality, the dialogue unit uses the generation AI to provide a concise and cheerful dialogue. This makes it possible to adjust the level of detail of the dialogue based on the personality of the stuffed animal.

[0046] The dialogue unit can improve the accuracy of the dialogue by referring to the user's past dialogue history during the dialogue. The past dialogue history includes, for example, the type of data to be saved and the storage period. The dialogue unit, for example, suggests related topics based on what the user has said in the past. The dialogue unit can also improve the accuracy of the dialogue by referring to the user's past dialogue history during the dialogue using the generation AI. For example, the dialogue unit uses the generation AI to analyze the user's preferred topics from the user's past dialogue history and reflect them in the dialogue. This improves the accuracy of the dialogue based on the user's past dialogue history.

[0047] The dialogue unit can determine the priority of dialogues based on the time of user submission during dialogue. The submission time includes, for example, the submission date and time and the frequency of submission. For example, if the user is in a hurry, the dialogue unit can provide important information preferentially. The dialogue unit can also use the generation AI to determine the priority of dialogues based on the time of user submission during dialogue. For example, the dialogue unit can use the generation AI to provide detailed information if the user is relaxed. This makes it possible to determine the priority of dialogues based on the time of user submission.

[0048] The dialogue unit can adjust the order of dialogue based on the user's relevance during dialogue. Relevance includes, for example, the degree of topic agreement and related keywords. For example, the dialogue unit prioritizes providing topics that the user is interested in. The dialogue unit can also use the generation AI to adjust the order of dialogue based on the user's relevance during dialogue. For example, the dialogue unit uses the generation AI to prioritize providing topics related to content that the user has previously spoken about. This makes it possible to adjust the order of dialogue based on the user's relevance.

[0049] The dialogue unit can adjust the use of technical terms in the dialogue depending on the user's level of expertise during the dialogue. Expertise levels include, for example, knowledge tests and past experience. For example, if the user has expertise, the dialogue unit provides a dialogue that uses a lot of technical terms. The dialogue unit can also use the generation AI to adjust the use of technical terms in the dialogue depending on the user's level of expertise during the dialogue. For example, if the user is a beginner, the dialogue unit can use the generation AI to provide a dialogue that explains things in simple terms. This makes it possible to adjust the use of technical terms in the dialogue depending on the user's level of expertise.

[0050] The storage unit can improve the accuracy of storage by referring to the user's past setting history when saving. The past setting history includes, for example, the type of data to be saved and the storage period. The storage unit, for example, analyzes the tendencies of personality settings previously set by the user and prioritizes saving similar personality settings. The storage unit can also improve the accuracy of storage by using the generation AI to refer to the user's past setting history when saving. For example, the storage unit uses the generation AI to save optimal personality settings based on the user's preferred speaking style and tone in the past. This improves the accuracy of storage based on the user's past setting history.

[0051] The storage unit can select the optimal storage means depending on the user's input method when saving. Input methods include, for example, voice input, text input, and image input. For example, when the user uses voice input, the storage unit saves the personality settings using voice recognition technology. The storage unit can also select the optimal storage means depending on the user's input method when saving using the generation AI. For example, when the user uses text input, the storage unit saves the personality settings using text analysis technology. This makes it possible to select the optimal storage means depending on the user's input method.

[0052] The storage unit can save highly relevant settings by taking into account the user's geographic location information when saving. Geographic location information includes, for example, GPS data and location information services. For example, if the user lives in a cold region, the storage unit saves a warm personality setting. The storage unit can also save highly relevant settings by using the generation AI when saving, taking into account the user's geographic location information. For example, if the user lives in an urban area, the storage unit saves an urban and sophisticated personality setting using the generation AI. This makes it possible to save highly relevant settings based on the user's geographic location information.

[0053] The storage unit may analyze the user's social media activity and store related settings when saving. Social media activity may include, for example, analyzing posted content and evaluating activity frequency. The storage unit may store personality settings based on, for example, words and expressions frequently used by the user on social media. The storage unit may also use a generation AI to analyze the user's social media activity and store related settings when saving. For example, the storage unit may use a generation AI to analyze the trends of accounts followed by the user and store related personality settings. This allows related settings to be stored based on the user's social media activity.

[0054] The storage unit can customize the storage method by reflecting the user's past feedback when saving. The past feedback includes, for example, the type of feedback and the storage period. The storage unit improves the method for saving personality settings, for example, based on feedback provided by the user in the past. The storage unit can also customize the storage method by reflecting the user's past feedback when saving, using the generation AI. For example, the storage unit uses the generation AI to preferentially suggest storage methods that the user has preferred in the past. This allows the storage method to be customized based on the user's past feedback.

[0055] The providing unit can select the optimal display method by referring to the user's past interaction history when providing the data. The past interaction history includes, for example, the type of data to be saved and the storage period. The providing unit, for example, suggests the optimal display method based on the display methods used by the user in the past. The providing unit can also select the optimal display method by referring to the user's past interaction history when providing the data, using the generation AI. For example, the providing unit uses the generation AI to analyze and suggest preferred display methods from the user's past interaction history. This makes it possible to select the optimal display method based on the user's past interaction history.

[0056] The providing unit can select the optimal display means depending on the user's input method at the time of providing. Input methods include, for example, voice input, text input, and image input. For example, when the user uses voice input, the providing unit provides display content using voice recognition technology. The providing unit can also select the optimal display means depending on the user's input method at the time of providing using the generation AI. For example, when the user uses text input, the providing unit uses text analysis technology to provide display content. This makes it possible to select the optimal display means depending on the user's input method.

[0057] The providing unit can prioritize displaying highly relevant histories by taking into account the user's geographical location information when providing the history. Geographical location information includes, for example, GPS data and location information services. For example, if the user lives in a cold region, the providing unit prioritizes displaying warm dialogue histories. The providing unit can also use the generation AI to prioritize displaying highly relevant histories by taking into account the user's geographical location information when providing the history. For example, if the user lives in an urban area, the providing unit uses the generation AI to prioritize displaying urban and sophisticated dialogue histories. This makes it possible to prioritize displaying highly relevant histories based on the user's geographical location information.

[0058] The providing unit can analyze the user's social media activity at the time of providing the data and display related history. Social media activity includes, for example, analyzing the content of posts and evaluating the frequency of activity. The providing unit can display the interaction history, for example, based on words and expressions frequently used by the user on social media. The providing unit can also use the generation AI to analyze the user's social media activity at the time of providing the data and display related history. For example, the providing unit can use the generation AI to analyze trends in accounts followed by the user and display related interaction history. This makes it possible to display related history based on the user's social media activity.

[0059] The providing unit can customize the display method by reflecting the user's past feedback when providing the information. The past feedback includes, for example, the type of feedback and the storage period. The providing unit improves the display method of the dialogue history, for example, based on feedback provided by the user in the past. The providing unit can also customize the display method by reflecting the user's past feedback when providing the information, using the generation AI. For example, the providing unit uses the generation AI to preferentially suggest display methods that the user has preferred in the past. This makes it possible to customize the display method based on the user's past feedback.

[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 setting unit can analyze the user's past interaction history and automatically suggest personality settings that the user prefers. For example, if the user has selected a "kind" personality in many past interactions, the setting unit will preferentially suggest a "kind" personality the next time the user selects the setting. Also, if the user tends to prefer a specific personality at a specific time of day, the setting unit can suggest a personality setting that matches that time of day. Furthermore, if the user tends to change their personality depending on a specific event or season, the setting unit can suggest a personality setting that matches that event or season. This makes it possible to provide a more personalized personality setting based on the user's past interaction history.

[0062] The conversation generation unit can detect the user's current activity status and generate conversations that correspond to that status. For example, if the user is exercising, the conversation generation unit can generate short, energetic responses. If the user is reading, the conversation generation unit can generate quiet, calm conversations. Furthermore, if the user is working, the conversation generation unit can generate efficient, to-the-point conversations. This makes it possible to provide appropriate conversations that correspond to the user's activity status.

[0063] The storage unit can analyze the user's past feedback and automatically adjust the priority of personality settings to be saved. For example, if the user has previously rated the "kind" personality highly, the storage unit can prioritize saving the "kind" personality. Also, if the user has given a low rating to a specific personality setting, the storage unit can lower the priority of saving that personality setting. Furthermore, if the user tends to prefer a specific personality setting in a specific situation, the storage unit can prioritize saving a personality setting that suits that situation. This makes it possible to save more appropriate personality settings based on the user's past feedback.

[0064] The setting unit can propose highly relevant personality settings by taking into account the user's geographical location information. For example, if the user lives in a cold region, a stuffed animal with a warm personality can be proposed. If the user lives in an urban area, a stuffed animal with an urbane and sophisticated personality can be proposed. Furthermore, if the user lives in an area rich in nature, a stuffed animal with a nature-loving personality can be proposed. This makes it possible to propose more appropriate personality settings based on the user's geographical location information.

[0065] The conversation generation unit can analyze the user's past conversation history and generate conversations that prioritize topics that the user likes. For example, if the user has talked about "sports" in many past conversations, the conversation generation unit can prioritize topics related to "sports" in the next conversation. Also, if the user tends to prefer certain topics at certain times of the day, the conversation generation unit can provide topics that match those times of day. Furthermore, if the user tends to change topics depending on certain events or seasons, the conversation generation unit can provide topics that match those events or seasons. This makes it possible to provide more personalized conversations based on the user's past conversation history.

[0066] The storage unit can analyze the user's social media activity and store related personality settings. For example, personality settings can be stored based on words and expressions frequently used by the user on social media. The storage unit can also analyze the trends of accounts the user follows and store related personality settings. Furthermore, if the user tends to change their personality settings in response to specific events or trends, personality settings tailored to those events or trends can be stored. This allows more appropriate personality settings to be stored based on the user's social media activity.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The setting unit provides an interface for the user to set the personality of the stuffed animal. For example, the setting unit provides a graphical user interface for the user to set the personality and speech style of the stuffed animal in detail. The setting unit also allows the user to set the personality by voice using a voice interface. Furthermore, the setting unit has a function for saving the personality set by the user. Step 2: The conversation generation unit generates a conversation based on the personality set by the user. For example, the conversation generation unit uses a generation AI to generate appropriate responses based on the personality set by the user. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The dialogue unit carries out a dialogue based on the dialogue generated by the dialogue generation unit. For example, when the user asks, "How was your day?", the dialogue unit replies, "I had a lot of fun today!" Step 4: The storage unit stores the personality set by the user and allows the user to change the setting at any time. For example, the storage unit stores the personality set by the user in a database and allows the user to change the setting later. Step 5: The providing unit provides the result of the interaction to the user. For example, the providing unit may store the history of the interaction so that the history can be checked later.

[0069] (Example 2) A chatbot system according to an embodiment of the present invention allows a user to set a personality for a stuffed animal and engage in a dialogue based on that personality. The chatbot system provides an interface for the user to set the personality of the stuffed animal, generates a conversation based on the set personality, and engages in a dialogue. It also saves the set personality and provides the results of the dialogue. For example, the chatbot system provides an interface for the user to set the personality and speech style of the stuffed animal in detail. The chatbot then engages in a conversation based on the set personality. For example, if the user asks, "How was your day?", the stuffed animal can respond, "I had a great time today!" The chatbot system also provides a function for saving the personality set by the user, allowing the user to change the setting at any time. It also has a function for providing the results of the dialogue to the user. For example, the dialogue history can be saved and reviewed later. This allows stuffed animal lovers to develop a stronger attachment to their stuffed animals and can be used for psychological support, therapy, and education. For example, children's interactions with stuffed animals can be expected to improve their communication skills and reduce stress. Furthermore, as a form of therapy, patients can interact with stuffed animals to provide psychological care.

[0070] A chatbot system according to an embodiment includes a setting unit, a conversation generation unit, a dialogue unit, a storage unit, and a providing unit. The setting unit provides an interface for a user to set the personality of a stuffed animal. For example, the setting unit provides a graphical user interface for a user to set the personality and speech style of the stuffed animal in detail. The setting unit also allows the user to set the personality by voice using a voice interface. The setting unit also has a function for saving the personality set by the user. The conversation generation unit generates a conversation based on the personality set by the user. For example, the conversation generation unit uses a generation AI to generate appropriate responses based on the personality set by the user. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The dialogue unit engages in a dialogue based on the conversation generated by the conversation generation unit. For example, when the dialogue unit asks, "How was your day?", the stuffed animal responds, "I had a lot of fun today!" The storage unit saves the personality set by the user and allows the setting to be changed at any time. For example, the storage unit saves the personality set by the user in a database so that it can be changed later. The providing unit provides the result of the dialogue to the user. For example, the providing unit may store a history of the conversation so that the conversation can be checked later. As a result, the chatbot system according to the embodiment allows the user to set the personality of the stuffed animal and have a conversation based on that personality, which can be used for psychological support, therapy, and education.

[0071] The setting unit can provide an interface for the user to set the personality and speaking style of the stuffed animal. The interface includes, for example, a graphical user interface and a voice interface. The setting unit can provide, for example, a graphical user interface for the user to set the personality and speaking style of the stuffed animal in detail. The setting unit can also allow the user to set the personality by voice using a voice interface. For example, the setting unit can set the personality of the stuffed animal by the user inputting "kind personality" by voice. This allows the user to set the personality and speaking style of the stuffed animal in detail.

[0072] The conversation generation unit can generate responses based on the personality set by the user. The responses include, for example, the appropriateness of the response's writing style and content. The conversation generation unit, for example, uses a generation AI to generate appropriate responses based on the personality set by the user. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the conversation generation unit generates a response that answers the question "How was your day?" with "I had a lot of fun today!" based on the personality set by the user. The conversation generation unit can also use the generation AI to generate responses that include appropriate emotional expressions based on the personality set by the user. For example, the conversation generation unit uses the generation AI to generate responses that express emotions such as joy and sadness based on the personality set by the user. This makes it possible to generate appropriate responses based on the personality set by the user.

[0073] When a user asks, "How was your day?", the dialogue unit can generate an answer based on the stuffed animal's preset personality. The preset personality may include, for example, personality traits and setting procedures. For example, when a user asks, "How was your day?", the dialogue unit can have the stuffed animal reply, "I had a lot of fun today!" The dialogue unit can also use a generation AI to generate an answer based on the preset personality. For example, the dialogue unit uses a generation AI to generate an answer that includes an appropriate emotional expression based on the personality set by the user. This allows the user to enjoy natural dialogue with the stuffed animal.

[0074] The storage unit may store the personality set by the user and allow the user to change the setting at any time. The setting that can be changed at any time may include, for example, a change procedure and changeable items. The storage unit may store the personality set by the user in a database and allow the user to change the personality later. The storage unit may also provide an interface for storing the personality set by the user and allowing the user to change the personality at any time. For example, the storage unit may provide a button or menu for storing the personality set by the user and changing it later. This allows the user to store the personality set by the user and change it at any time.

[0075] The providing unit can save the dialogue history so that it can be checked later. The dialogue history includes, for example, the type of data to be saved and the storage period. The providing unit can, for example, save the dialogue history in a database so that it can be checked later. The providing unit can also provide an interface for saving the dialogue history and checking it later. For example, the providing unit can provide a button or menu for displaying the dialogue history. This allows the user to save the dialogue history and check it later.

[0076] The setting unit can estimate the user's emotions and suggest a personality setting for the stuffed animal based on the estimated user's emotions. Estimating emotions includes, for example, the algorithm used and the accuracy of the estimation. For example, if the user is feeling stressed, the setting unit can suggest a stuffed animal with a gentle personality that helps them relax. The setting unit can also use the generation AI to estimate the user's emotions and suggest a personality setting for the stuffed animal based on the estimated user's emotions. For example, if the user is feeling energetic, the setting unit can use the generation AI to suggest a stuffed animal with an active and cheerful personality. This makes it possible to suggest optimal personality settings based on the user's emotions.

[0077] The setting unit can analyze the user's past setting history and automatically suggest personality settings. The past setting history includes, for example, the type of data to be saved and the storage period. The setting unit, for example, analyzes the tendencies of personality settings that the user has previously set and suggests similar personalities. The setting unit can also use a generation AI to analyze the user's past setting history and automatically suggest personality settings. For example, the setting unit uses a generation AI to suggest optimal personality settings based on the user's preferred speaking style and tone in the past. This makes it possible to suggest optimal personality settings based on the user's past setting history.

[0078] The setting unit can customize the personality setting based on the user's current psychological state during setup. The current psychological state includes, for example, psychological tests and behavioral analysis. For example, if the user is nervous, the setting unit suggests a stuffed animal with a calm personality. The setting unit can also customize the personality setting based on the user's current psychological state during setup using the generation AI. For example, if the user is happy, the setting unit uses the generation AI to suggest a stuffed animal with a bright and cheerful personality. This makes it possible to suggest the optimal personality setting based on the user's current psychological state.

[0079] The setting unit can select a setting means according to the user's input method at the time of setting. Input methods include, for example, voice input, text input, and image input. For example, when the user uses voice input, the setting unit sets the personality using voice recognition technology. The setting unit can also select a setting means according to the user's input method at the time of setting using a generation AI. For example, when the user uses text input, the setting unit uses a generation AI to set the personality using text analysis technology. This makes it possible to select the optimal setting means according to the user's input method.

[0080] The setting unit can estimate the user's emotions and determine the priority of the personality to be set based on the estimated user's emotions. The personality priority includes, for example, an evaluation criterion for importance and a method for setting the priority. For example, if the user is feeling stressed, the setting unit prioritizes a personality that is relaxing. The setting unit can also use the generation AI to estimate the user's emotions and determine the priority of the personality to be set based on the estimated user's emotions. For example, if the user is feeling energetic, the setting unit uses the generation AI to prioritize a lively personality. This makes it possible to determine the priority of personality settings based on the user's emotions.

[0081] The setting unit can propose highly relevant personality settings by taking into account the user's geographical location information during setup. Geographical location information includes, for example, GPS data and location information services. For example, if the user lives in a cold region, the setting unit proposes a stuffed animal with a warm personality. The setting unit can also propose highly relevant personality settings by using the generation AI during setup, taking into account the user's geographical location information. For example, if the user lives in an urban area, the setting unit can use the generation AI to propose a stuffed animal with an urban and sophisticated personality. This makes it possible to propose optimal personality settings based on the user's geographical location information.

[0082] The setting unit can analyze the user's social media activity at the time of setting and suggest related personality settings. Social media activity includes, for example, analyzing the content of posts and evaluating the frequency of activity. The setting unit can suggest personality settings based on, for example, words and expressions frequently used by the user on social media. The setting unit can also use a generation AI to analyze the user's social media activity at the time of setting and suggest related personality settings. For example, the setting unit can use a generation AI to analyze the trends of accounts followed by the user and suggest related personality settings. This makes it possible to suggest optimal personality settings based on the user's social media activity.

[0083] The setting unit can customize the setting method by reflecting the user's past feedback during setup. The past feedback includes, for example, the type of feedback and the storage period. The setting unit can improve the personality setting suggestions, for example, based on feedback provided by the user in the past. The setting unit can also customize the setting method by using the generation AI during setup by reflecting the user's past feedback. For example, the setting unit can use the generation AI to preferentially suggest setting methods that the user has preferred in the past. This makes it possible to suggest the optimal setting method based on the user's past feedback.

[0084] The conversation generation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user's emotions. The way the conversation is expressed includes, for example, changing the writing style and adjusting the emotional expression. For example, if the user is relaxed, the conversation generation unit will speak in a calm tone. The conversation generation unit can also estimate the user's emotions using the generation AI and adjust the way the conversation is expressed based on the estimated user's emotions. For example, if the user is nervous, the conversation generation unit will speak in a calm tone using the generation AI. This makes it possible to adjust the way the conversation is expressed based on the user's emotions.

[0085] The conversation generation unit can adjust the level of detail of the conversation based on the personality of the stuffed animal when generating the conversation. The level of detail of the conversation includes, for example, detailed explanations and concise responses. For example, the conversation generation unit provides polite and detailed explanations for a stuffed animal with a kind personality. The conversation generation unit can also adjust the level of detail of the conversation based on the personality of the stuffed animal when generating the conversation using the generation AI. For example, the conversation generation unit uses the generation AI to provide concise and cheerful conversations for a stuffed animal with a lively personality. This makes it possible to adjust the level of detail of the conversation based on the personality of the stuffed animal.

[0086] The conversation generation unit can improve the accuracy of the conversation when generating a conversation by referring to the user's past conversation history. The past conversation history includes, for example, the type of data to be saved and the storage period. The conversation generation unit, for example, suggests related topics based on content that the user has spoken in the past. The conversation generation unit can also improve the accuracy of the conversation by using the generation AI to refer to the user's past conversation history when generating a conversation. For example, the conversation generation unit uses the generation AI to analyze preferred topics from the user's past conversation history and reflect them in the conversation. This improves the accuracy of the conversation based on the user's past conversation history.

[0087] The conversation generation unit can customize the content of the conversation based on the user's current psychological state when generating the conversation. The current psychological state includes, for example, psychological tests and behavioral analysis. For example, if the user is nervous, the conversation generation unit provides a topic that will help the user relax. The conversation generation unit can also use the generation AI to customize the content of the conversation based on the user's current psychological state when generating the conversation. For example, if the user is happy, the conversation generation unit uses the generation AI to provide a fun topic. This allows the conversation content to be customized based on the user's current psychological state.

[0088] The conversation generation unit can estimate the user's emotions and adjust the length of the conversation based on the estimated user's emotions. The length of the conversation includes, for example, the length of the responses and the duration of the conversation. For example, if the user is in a hurry, the conversation generation unit provides a short, to-the-point conversation. The conversation generation unit can also estimate the user's emotions using the generation AI and adjust the length of the conversation based on the estimated user's emotions. For example, if the user is relaxed, the conversation generation unit uses the generation AI to provide a longer conversation that includes detailed explanations. This allows the length of the conversation to be adjusted based on the user's emotions.

[0089] The conversation generation unit can determine the priority of conversations based on the time of submission by the user when generating a conversation. The submission time includes, for example, the submission date and time and the frequency of submission. For example, when the user is in a hurry, the conversation generation unit provides important information preferentially. The conversation generation unit can also determine the priority of conversations based on the time of submission by the user when generating a conversation using the generation AI. For example, when the user is relaxed, the conversation generation unit provides detailed information using the generation AI. This makes it possible to determine the priority of conversations based on the time of submission by the user.

[0090] The conversation generation unit can adjust the order of conversations based on the user's relevance when generating a conversation. Relevance includes, for example, the degree of topic agreement and related keywords. The conversation generation unit, for example, prioritizes providing topics that interest the user. The conversation generation unit can also use the generation AI to adjust the order of conversations based on the user's relevance when generating a conversation. For example, the conversation generation unit uses the generation AI to prioritize providing topics related to content that the user has previously discussed. This makes it possible to adjust the order of conversations based on the user's relevance.

[0091] The conversation generation unit can adjust the use of technical terms in the conversation according to the user's level of expertise when generating the conversation. Expertise levels include, for example, knowledge tests and past experience. For example, if the user has expertise, the conversation generation unit provides a conversation that makes extensive use of technical terms. The conversation generation unit can also use the generation AI to adjust the use of technical terms in the conversation according to the user's level of expertise when generating the conversation. For example, if the user is a beginner, the conversation generation unit uses the generation AI to provide a conversation that explains things in simple terms. This makes it possible to adjust the use of technical terms in the conversation according to the user's level of expertise.

[0092] The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated user's emotions. The way the dialogue proceeds includes, for example, the tempo of the dialogue and the order of responses. For example, if the user is nervous, the dialogue unit proceeds with the dialogue at a slower pace. The dialogue unit can also estimate the user's emotions using the generation AI and adjust the way the dialogue proceeds based on the estimated user's emotions. For example, if the user is relaxed, the dialogue unit proceeds with the dialogue at a natural pace using the generation AI. This makes it possible to adjust the way the dialogue proceeds based on the user's emotions.

[0093] The dialogue unit can adjust the level of detail of the dialogue based on the personality of the stuffed animal during the dialogue. The level of detail of the dialogue includes, for example, detailed explanations and concise responses. For example, in the case of a stuffed animal with a kind personality, the dialogue unit provides a polite and detailed explanation. The dialogue unit can also use the generation AI to adjust the level of detail of the dialogue based on the personality of the stuffed animal during the dialogue. For example, in the case of a stuffed animal with a lively personality, the dialogue unit uses the generation AI to provide a concise and cheerful dialogue. This makes it possible to adjust the level of detail of the dialogue based on the personality of the stuffed animal.

[0094] The dialogue unit can improve the accuracy of the dialogue by referring to the user's past dialogue history during the dialogue. The past dialogue history includes, for example, the type of data to be saved and the storage period. The dialogue unit, for example, suggests related topics based on what the user has said in the past. The dialogue unit can also improve the accuracy of the dialogue by referring to the user's past dialogue history during the dialogue using the generation AI. For example, the dialogue unit uses the generation AI to analyze the user's preferred topics from the user's past dialogue history and reflect them in the dialogue. This improves the accuracy of the dialogue based on the user's past dialogue history.

[0095] The dialogue unit can customize the dialogue content based on the user's current psychological state during the dialogue. The current psychological state includes, for example, psychological tests and behavioral analysis. For example, if the user is nervous, the dialogue unit can provide a topic that will help the user relax. The dialogue unit can also use the generation AI to customize the dialogue content based on the user's current psychological state during the dialogue. For example, if the user is happy, the dialogue unit can use the generation AI to provide a fun topic. This allows the dialogue content to be customized based on the user's current psychological state.

[0096] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated user's emotions. The dialogue length includes, for example, the length of the response and the duration of the dialogue. For example, if the user is in a hurry, the dialogue unit provides a short, to-the-point dialogue. The dialogue unit can also use the generation AI to estimate the user's emotions and adjust the length of the dialogue based on the estimated user's emotions. For example, if the user is relaxed, the dialogue unit uses the generation AI to provide a longer dialogue with detailed explanations. This allows the length of the dialogue to be adjusted based on the user's emotions.

[0097] The dialogue unit can determine the priority of dialogues based on the time of user submission during dialogue. The submission time includes, for example, the submission date and time and the frequency of submission. For example, if the user is in a hurry, the dialogue unit can provide important information preferentially. The dialogue unit can also use the generation AI to determine the priority of dialogues based on the time of user submission during dialogue. For example, the dialogue unit can use the generation AI to provide detailed information if the user is relaxed. This makes it possible to determine the priority of dialogues based on the time of user submission.

[0098] The dialogue unit can adjust the order of dialogue based on the user's relevance during dialogue. Relevance includes, for example, the degree of topic agreement and related keywords. For example, the dialogue unit prioritizes providing topics that the user is interested in. The dialogue unit can also use the generation AI to adjust the order of dialogue based on the user's relevance during dialogue. For example, the dialogue unit uses the generation AI to prioritize providing topics related to content that the user has previously spoken about. This makes it possible to adjust the order of dialogue based on the user's relevance.

[0099] The dialogue unit can adjust the use of technical terms in the dialogue depending on the user's level of expertise during the dialogue. Expertise levels include, for example, knowledge tests and past experience. For example, if the user has expertise, the dialogue unit provides a dialogue that uses a lot of technical terms. The dialogue unit can also use the generation AI to adjust the use of technical terms in the dialogue depending on the user's level of expertise during the dialogue. For example, if the user is a beginner, the dialogue unit can use the generation AI to provide a dialogue that explains things in simple terms. This makes it possible to adjust the use of technical terms in the dialogue depending on the user's level of expertise.

[0100] The storage unit can estimate the user's emotions and determine the priority of personality settings to be saved based on the estimated user's emotions. The priority of personality settings includes, for example, an evaluation criterion for importance and a method for setting the priority. For example, if the user is feeling stressed, the storage unit will preferentially save a personality setting that allows the user to relax. The storage unit can also use the generation AI to estimate the user's emotions and determine the priority of personality settings to be saved based on the estimated user's emotions. For example, if the user is feeling energetic, the storage unit will use the generation AI to preferentially save a lively personality setting. This makes it possible to determine the priority of personality settings to be saved based on the user's emotions.

[0101] The storage unit can improve the accuracy of storage by referring to the user's past setting history when saving. The past setting history includes, for example, the type of data to be saved and the storage period. The storage unit, for example, analyzes the tendencies of personality settings previously set by the user and prioritizes saving similar personality settings. The storage unit can also improve the accuracy of storage by using the generation AI to refer to the user's past setting history when saving. For example, the storage unit uses the generation AI to save optimal personality settings based on the user's preferred speaking style and tone in the past. This improves the accuracy of storage based on the user's past setting history.

[0102] The storage unit can customize the saved content based on the user's current psychological state when saving. The current psychological state includes, for example, psychological tests and behavioral analysis. For example, if the user is nervous, the storage unit will preferentially save a calm personality setting. The storage unit can also use the generation AI to customize the saved content based on the user's current psychological state when saving. For example, if the user is happy, the storage unit will use the generation AI to preferentially save a cheerful and energetic personality setting. This allows the saved content to be customized based on the user's current psychological state.

[0103] The storage unit can select the optimal storage means depending on the user's input method when saving. Input methods include, for example, voice input, text input, and image input. For example, when the user uses voice input, the storage unit saves the personality settings using voice recognition technology. The storage unit can also select the optimal storage means depending on the user's input method when saving using the generation AI. For example, when the user uses text input, the storage unit saves the personality settings using text analysis technology. This makes it possible to select the optimal storage means depending on the user's input method.

[0104] The storage unit can estimate the user's emotions and determine the priority of personality settings to be saved based on the estimated user's emotions. The priority of personality settings includes, for example, an evaluation criterion for importance and a method for setting the priority. For example, if the user is feeling stressed, the storage unit will preferentially save a personality setting that allows the user to relax. The storage unit can also use the generation AI to estimate the user's emotions and determine the priority of personality settings to be saved based on the estimated user's emotions. For example, if the user is feeling energetic, the storage unit will use the generation AI to preferentially save a lively personality setting. This makes it possible to determine the priority of personality settings to be saved based on the user's emotions.

[0105] The storage unit can save highly relevant settings by taking into account the user's geographic location information when saving. Geographic location information includes, for example, GPS data and location information services. For example, if the user lives in a cold region, the storage unit saves a warm personality setting. The storage unit can also save highly relevant settings by using the generation AI when saving, taking into account the user's geographic location information. For example, if the user lives in an urban area, the storage unit saves an urban and sophisticated personality setting using the generation AI. This makes it possible to save highly relevant settings based on the user's geographic location information.

[0106] The storage unit may analyze the user's social media activity and store related settings when saving. Social media activity may include, for example, analyzing posted content and evaluating activity frequency. The storage unit may store personality settings based on, for example, words and expressions frequently used by the user on social media. The storage unit may also use a generation AI to analyze the user's social media activity and store related settings when saving. For example, the storage unit may use a generation AI to analyze the trends of accounts followed by the user and store related personality settings. This allows related settings to be stored based on the user's social media activity.

[0107] The storage unit can customize the storage method by reflecting the user's past feedback when saving. The past feedback includes, for example, the type of feedback and the storage period. The storage unit improves the method for saving personality settings, for example, based on feedback provided by the user in the past. The storage unit can also customize the storage method by reflecting the user's past feedback when saving, using the generation AI. For example, the storage unit uses the generation AI to preferentially suggest storage methods that the user has preferred in the past. This allows the storage method to be customized based on the user's past feedback.

[0108] The providing unit can estimate the user's emotions and adjust the display method of the dialogue history based on the estimated user's emotions. The display method of the dialogue history includes, for example, the display format and the display order. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. The providing unit can also estimate the user's emotions using the generation AI and adjust the display method of the dialogue history based on the estimated user's emotions. For example, if the user is relaxed, the providing unit uses the generation AI to provide a display method including detailed information. This makes it possible to adjust the display method of the dialogue history based on the user's emotions.

[0109] The providing unit can select the optimal display method by referring to the user's past interaction history when providing the data. The past interaction history includes, for example, the type of data to be saved and the storage period. The providing unit, for example, suggests the optimal display method based on the display methods used by the user in the past. The providing unit can also select the optimal display method by referring to the user's past interaction history when providing the data, using the generation AI. For example, the providing unit uses the generation AI to analyze and suggest preferred display methods from the user's past interaction history. This makes it possible to select the optimal display method based on the user's past interaction history.

[0110] The providing unit can customize the display content based on the user's current psychological state at the time of providing. The current psychological state includes, for example, a psychological test or behavioral analysis. For example, if the user is nervous, the providing unit provides display content that helps the user relax. The providing unit can also customize the display content based on the user's current psychological state at the time of providing, using the generation AI. For example, if the user is happy, the providing unit provides fun display content using the generation AI. This allows the display content to be customized based on the user's current psychological state.

[0111] The providing unit can select the optimal display means depending on the user's input method at the time of providing. Input methods include, for example, voice input, text input, and image input. For example, when the user uses voice input, the providing unit provides display content using voice recognition technology. The providing unit can also select the optimal display means depending on the user's input method at the time of providing using the generation AI. For example, when the user uses text input, the providing unit uses text analysis technology to provide display content. This makes it possible to select the optimal display means depending on the user's input method.

[0112] The providing unit can estimate the user's emotions and determine the priority of the dialogue history based on the estimated user's emotions. The priority of the dialogue history includes, for example, an evaluation criterion for importance and a method for setting the priority. For example, if the user is feeling stressed, the providing unit preferentially displays a dialogue history that is relaxing. The providing unit can also use the generation AI to estimate the user's emotions and determine the priority of the dialogue history based on the estimated user's emotions. For example, if the user is feeling energetic, the providing unit uses the generation AI to preferentially display a dialogue history that is lively. This makes it possible to determine the priority of the dialogue history based on the user's emotions.

[0113] The providing unit can prioritize displaying highly relevant histories by taking into account the user's geographical location information when providing the history. Geographical location information includes, for example, GPS data and location information services. For example, if the user lives in a cold region, the providing unit prioritizes displaying warm dialogue histories. The providing unit can also use the generation AI to prioritize displaying highly relevant histories by taking into account the user's geographical location information when providing the history. For example, if the user lives in an urban area, the providing unit uses the generation AI to prioritize displaying urban and sophisticated dialogue histories. This makes it possible to prioritize displaying highly relevant histories based on the user's geographical location information.

[0114] The providing unit can analyze the user's social media activity at the time of providing the data and display related history. Social media activity includes, for example, analyzing the content of posts and evaluating the frequency of activity. The providing unit can display the interaction history, for example, based on words and expressions frequently used by the user on social media. The providing unit can also use the generation AI to analyze the user's social media activity at the time of providing the data and display related history. For example, the providing unit can use the generation AI to analyze trends in accounts followed by the user and display related interaction history. This makes it possible to display related history based on the user's social media activity.

[0115] The providing unit can customize the display method by reflecting the user's past feedback when providing the information. The past feedback includes, for example, the type of feedback and the storage period. The providing unit improves the display method of the dialogue history, for example, based on feedback provided by the user in the past. The providing unit can also customize the display method by reflecting the user's past feedback when providing the information, using the generation AI. For example, the providing unit uses the generation AI to preferentially suggest display methods that the user has preferred in the past. This makes it possible to customize the display method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the setting unit, conversation generation unit, interaction unit, storage unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to set the personality of the stuffed animal. The conversation generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The interaction unit is realized by the control unit 46A of the smart device 14 and conducts a dialogue based on the generated conversation. The storage unit saves the personality setting in the database 24 of the data processing device 12. The provision unit provides the result of the dialogue to the user via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the setting unit, conversation generation unit, interaction unit, storage unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to set the personality of the stuffed animal. The conversation generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The interaction unit is realized by the control unit 46A of the smart glasses 214 and conducts a dialogue based on the generated conversation. The storage unit saves the personality setting in the database 24 of the data processing device 12. The provision unit provides the result of the dialogue to the user via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, conversation generation unit, interaction unit, storage unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for the user to set the personality of the stuffed animal. The conversation generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The interaction unit is realized by the control unit 46A of the headset type terminal 314 and conducts a dialogue based on the generated conversation. The storage unit saves the personality setting in the database 24 of the data processing device 12. The provision unit provides the result of the dialogue to the user via the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the setting unit, conversation generation unit, interaction unit, storage unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to set the personality of the stuffed animal. The conversation generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a conversation using a generation AI. The interaction unit is realized by the control unit 46A of the robot 414 and conducts a dialogue based on the generated dialogue. The storage unit saves the personality setting in the database 24 of the data processing device 12. The provision unit provides the result of the dialogue to the user via the control unit 46A of the robot 414.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The setting unit can analyze the user's past interaction history and automatically suggest personality settings that the user prefers. For example, if the user has selected a "kind" personality in many past interactions, the setting unit will preferentially suggest a "kind" personality the next time the user selects the setting. Also, if the user tends to prefer a specific personality at a specific time of day, the setting unit can suggest a personality setting that matches that time of day. Furthermore, if the user tends to change their personality depending on a specific event or season, the setting unit can suggest a personality setting that matches that event or season. This makes it possible to provide a more personalized personality setting based on the user's past interaction history.

[0118] The conversation generation unit can detect the user's current activity status and generate conversations that correspond to that status. For example, if the user is exercising, the conversation generation unit can generate short, energetic responses. If the user is reading, the conversation generation unit can generate quiet, calm conversations. Furthermore, if the user is working, the conversation generation unit can generate efficient, to-the-point conversations. This makes it possible to provide appropriate conversations that correspond to the user's activity status.

[0119] The dialogue unit can estimate the user's emotions and adjust the tempo of the dialogue based on the estimated user's emotions. For example, if the user is nervous, the dialogue unit can proceed with the dialogue at a slower tempo. If the user is relaxed, the dialogue can proceed with the dialogue at a more natural tempo. Furthermore, if the user is in a hurry, the dialogue unit can provide a dialogue that quickly gets to the point. This allows the dialogue tempo to be adjusted based on the user's emotions.

[0120] The storage unit can analyze the user's past feedback and automatically adjust the priority of personality settings to be saved. For example, if the user has previously rated the "kind" personality highly, the storage unit can prioritize saving the "kind" personality. Also, if the user has given a low rating to a specific personality setting, the storage unit can lower the priority of saving that personality setting. Furthermore, if the user tends to prefer a specific personality setting in a specific situation, the storage unit can prioritize saving a personality setting that suits that situation. This makes it possible to save more appropriate personality settings based on the user's past feedback.

[0121] The providing unit can estimate the user's emotions and adjust the display method of the dialogue history based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, important information can be displayed with priority. In this way, the display method of the dialogue history can be adjusted based on the user's emotions.

[0122] The setting unit can propose highly relevant personality settings by taking into account the user's geographical location information. For example, if the user lives in a cold region, a stuffed animal with a warm personality can be proposed. If the user lives in an urban area, a stuffed animal with an urbane and sophisticated personality can be proposed. Furthermore, if the user lives in an area rich in nature, a stuffed animal with a nature-loving personality can be proposed. This makes it possible to propose more appropriate personality settings based on the user's geographical location information.

[0123] The conversation generation unit can analyze the user's past conversation history and generate conversations that prioritize topics that the user likes. For example, if the user has talked about "sports" in many past conversations, the conversation generation unit can prioritize topics related to "sports" in the next conversation. Also, if the user tends to prefer certain topics at certain times of the day, the conversation generation unit can provide topics that match those times of day. Furthermore, if the user tends to change topics depending on certain events or seasons, the conversation generation unit can provide topics that match those events or seasons. This makes it possible to provide more personalized conversations based on the user's past conversation history.

[0124] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated user's emotions. For example, if the user is relaxed, a longer dialogue including detailed explanations can be provided. If the user is in a hurry, a short dialogue that is to the point can be provided. Furthermore, if the user is feeling stressed, a topic that will help the user relax can be provided. In this way, the length of the dialogue can be adjusted based on the user's emotions.

[0125] The storage unit can analyze the user's social media activity and store related personality settings. For example, personality settings can be stored based on words and expressions frequently used by the user on social media. The storage unit can also analyze the trends of accounts the user follows and store related personality settings. Furthermore, if the user tends to change their personality settings in response to specific events or trends, personality settings tailored to those events or trends can be stored. This allows more appropriate personality settings to be stored based on the user's social media activity.

[0126] The providing unit can estimate the user's emotions and determine the priority of the dialogue history based on the estimated user's emotions. For example, if the user is feeling stressed, a dialogue history that is relaxing can be preferentially displayed. Also, if the user is in a lively mood, a dialogue history that is active can be preferentially displayed. Furthermore, if the user is in a hurry, important information can be preferentially displayed. In this way, the priority of the dialogue history can be determined based on the user's emotions.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The setting unit provides an interface for the user to set the personality of the stuffed animal. For example, the setting unit provides a graphical user interface for the user to set the personality and speech style of the stuffed animal in detail. The setting unit also allows the user to set the personality by voice using a voice interface. Furthermore, the setting unit has a function for saving the personality set by the user. Step 2: The conversation generation unit generates a conversation based on the personality set by the user. For example, the conversation generation unit uses a generation AI to generate appropriate responses based on the personality set by the user. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The dialogue unit carries out a dialogue based on the dialogue generated by the dialogue generation unit. For example, when the user asks, "How was your day?", the dialogue unit replies, "I had a lot of fun today!" Step 4: The storage unit stores the personality set by the user and allows the user to change the setting at any time. For example, the storage unit stores the personality set by the user in a database and allows the user to change the setting later. Step 5: The providing unit provides the result of the interaction to the user. For example, the providing unit may store the history of the interaction so that the history can be checked later.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

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

[0136] 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.

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

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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.

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

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0152] 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.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] 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.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] 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.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] 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.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 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 setting unit for allowing a user to set the personality of a stuffed toy; a conversation generation unit that generates a conversation based on the personality set by the setting unit; a dialogue unit that conducts a dialogue based on the conversation generated by the conversation generation unit; a storage unit for storing the personality set by the setting unit; a providing unit that provides a result of the dialogue performed by the dialogue unit; Equipped with A system characterized by:

2. The setting unit Provides an interface for users to set the personality and speech style of their stuffed animal 2. The system of claim 1.

3. The conversation generation unit Generate responses based on user-defined personality 2. The system of claim 1.

4. The storage unit Save user preferences and allow users to change their preferences at any time 2. The system of claim 1.

5. The providing unit Save conversation history for later review 2. The system of claim 1.

6. The setting unit Estimates the user's emotions and suggests personality settings for stuffed toys based on the estimated user emotions.

2. The system of claim 1.

7. The setting unit Analyzes the user's past settings history and automatically suggests personality settings 2. The system of claim 1.

Images