System

Abstract

To provide a system that reduce isolation and loneliness by allowing users to imitate conversations with the deceased.SOLUTION: A system according to the embodiment comprises a storage unit, an analysis unit, and a provision unit. The storage unit stores a user's text information, video information, and call information. The analysis unit analyzes data stored by the storage unit, and generates a response. The provision unit provides the response generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technology has the problem that it does not adequately provide services that mimic conversations with deceased people for users who feel isolated or lonely.

[0005] The system according to the embodiment aims to reduce isolation and loneliness by allowing the user to mimic a conversation with a deceased person. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, an analysis unit, and a provision unit. The storage unit stores text information, video information, and call information of a user. The analysis unit analyzes the data stored by the storage unit and generates a response. The provision unit provides the response generated by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can reduce isolation and loneliness by allowing the user to mimic a conversation with the deceased. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system for preparing for isolation and loneliness according to the embodiment of the present invention is a system that stores the text information, video information, and call information of a user, infers how an AI would respond if the user were deceased, and provides a response as if the user were talking to the deceased through chat or call on a messenger app. This system enters into a contract with a user in advance in a subscription format, and stores the user's text information (email or chat on a messenger app), video information, and call information such as voice and video calls from the date of contract conclusion. Based on the stored data, the system is a service that infers how an AI would respond if the user were deceased, and provides a response as if the user were talking to the deceased through chat or call on a messenger app. For example, a user registers for the service and concludes a contract. After the contract is concluded, the user's text information (email or chat on a messenger app), video information, and call information such as voice and video calls are automatically saved. These data are used to learn the user's communication patterns, language, tone of voice, etc. Next, the AI ​​analyzes the saved data and infers how the user would respond if the user were deceased. For example, it learns the user's frequently used phrases and reactions to specific topics, and generates replies based on this. Finally, the AI-generated replies are provided in chats and calls on the messenger app. For example, when the user's friends or family start a chat on the messenger app, the AI ​​responds as if the deceased were speaking. In voice and video calls, the AI ​​also reproduces the voice and image of the deceased, providing an experience as if they were talking to the deceased. This allows the user's friends and family to spend time without feeling isolated or lonely while reminiscing about their memories with the deceased. In this way, the system to prepare for isolation and loneliness allows the user's friends and family to reproduce conversations with the deceased, allowing them to spend time without feeling isolated or lonely.

[0029] The system for preparing for isolation and loneliness according to the embodiment includes a storage unit, an analysis unit, and a provision unit. The storage unit stores text information, video information, and call information of a user. The text information of a user includes, for example, chat history of emails and messenger apps. The video information includes, for example, recordings of video calls. The call information includes, for example, recordings of voice calls. The storage unit automatically stores these data and uses them to learn the user's communication patterns, language, tone of voice, and the like. The analysis unit analyzes the data stored by the storage unit and generates a response. The analysis unit learns, for example, phrases that the user frequently uses and reactions to specific topics, and generates a response based on this. The analysis unit uses a generation AI to infer what kind of response the user would make if the user were deceased. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and learns the user's communication patterns and generates an appropriate response. The provision unit provides the response generated by the analysis unit. The providing unit provides a response generated by the AI, for example, in a chat or call on a messenger app. When the user's friends or family start a chat on a messenger app, the providing unit has the AI ​​respond as if the deceased were the deceased. In addition, the providing unit also reproduces the voice and image of the deceased in a voice call or video call, providing an experience as if one were talking to the deceased. In this way, the system for preparing for isolation and loneliness according to the embodiment allows the user's friends and family to reproduce conversations with the deceased, allowing the user to spend time without feeling isolated or lonely.

[0030] The storage unit stores the user's text information, video information, and call information. The user's text information includes, for example, chat history of emails and messenger apps. These text information are part of the user's daily communication and record the user's language and expression in detail. The storage unit automatically collects these text information and stores them in a database. The video information includes, for example, recordings of video calls. Recordings of video calls include non-verbal communication elements that cannot be captured by text information, such as the user's facial expressions, gestures, and tone of voice. This allows the storage unit to store the user's communication style more comprehensively. The call information includes, for example, recordings of voice calls. Recordings of voice calls record the user's tone of voice, speaking style, emotional changes, and the like in detail. The storage unit automatically stores these data and uses them to learn the user's communication patterns, language, tone of voice, and the like. The storage unit centrally manages these data and allows the analysis unit and provision unit to access them as necessary. Furthermore, the storage unit allows the user to customize the data storage period and privacy settings, thereby protecting the user's privacy. This allows the storage unit to store the user's communication data efficiently and safely, improving the performance of the entire system.

[0031] The analysis unit analyzes the data stored by the storage unit and generates a response. The analysis unit learns, for example, the user's frequently used phrases and reactions to specific topics, and generates a response based on this. Specifically, the analysis unit uses a generation AI to analyze the user's communication pattern in detail. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and comprehensively analyzes the user's text information, video information, and call information. The text generation AI analyzes the user's chat history and email content to learn the user's frequently used phrases and reactions to specific topics. The multimodal generation AI analyzes recordings of video calls and voice calls to learn the user's facial expressions, tone of voice, gestures, etc. This allows the analysis unit to infer what kind of response the user would make if the user were deceased. The analysis unit uses the generation AI to reproduce the user's communication pattern and generate an appropriate response. For example, a natural conversation is generated based on the user's frequently used phrases and reactions to specific topics. The analysis unit also utilizes emotion and intent analysis techniques to understand the user's emotions and intentions and generate appropriate responses, allowing the analysis unit to faithfully reproduce the user's communication style and provide a natural conversation.

[0032] The providing unit provides the response generated by the analysis unit. For example, the providing unit provides the response generated by the AI ​​in a chat or call on a messenger app. Specifically, when the user's friends or family start a chat on a messenger app, the providing unit has the AI ​​respond as if the deceased. The providing unit receives data from the analysis unit in real time and provides responses at the appropriate time in order to reproduce the user's communication pattern and provide a natural conversation. In addition, the providing unit also reproduces the voice and image of the deceased in voice calls and video calls, providing an experience as if the user was talking to the deceased. In voice calls, the AI ​​reproduces the user's tone of voice and speaking style to provide a natural conversation. In video calls, the AI ​​reproduces the user's facial expressions and gestures to provide a more realistic experience. Furthermore, the providing unit provides responses at the appropriate time so that the user's friends and family do not feel isolated or lonely. For example, on certain anniversaries or events, the AI ​​can send messages as if the deceased, allowing the user's friends and family to feel connected to the deceased. This allows the providing unit to re-create conversations between the user's friends and family and the deceased, allowing them to spend time without feeling isolated or lonely.

[0033] Furthermore, the system for preparing for isolation and loneliness includes a protection unit that protects the privacy of the stored data. The protection unit protects the privacy of the stored data. The protection unit protects privacy using, for example, data encryption, access restriction, anonymization technology, and the like. The data encryption encrypts the stored data and protects it from unauthorized access. The access restriction restricts access to the data so that only specific users can access it. The anonymization technology deletes information that can identify an individual from the data and protects privacy. This makes it possible to protect the privacy of the stored data. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can cause AI to encrypt the data and set access restrictions.

[0034] Furthermore, the system for preparing for isolation and loneliness includes a deletion unit that allows a user to request the deletion of data. The deletion unit allows a user to request the deletion of data. The deletion unit provides, for example, a procedure by which a user requests the deletion of data. The deletion unit promptly deletes data requested by a user to be deleted. The deletion unit clarifies the range of data to be deleted and the timing of deletion. This allows a user to request the deletion of data. Some or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit can have AI analyze the user's deletion request and delete appropriate data.

[0035] Furthermore, the system for preparing for isolation and loneliness includes a reproduction unit in which the AI ​​reproduces the user's voice and image. The reproduction unit reproduces the user's voice and image using AI. The reproduction unit reproduces the user's voice and image using, for example, voice synthesis technology or image generation technology. The voice synthesis technology reproduces the user's voice based on stored voice data. The image generation technology reproduces the user's image based on stored image data. The reproduction unit analyzes the stored data in detail to improve the reproduction accuracy of the user's voice and image. This allows the AI ​​to reproduce the user's voice and image. A part or all of the above-mentioned processing in the reproduction unit is performed using a generation AI. For example, the reproduction unit can input the stored voice data to the generation AI and cause the generation AI to reproduce the user's voice. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and reproduces the user's voice and image with high accuracy.

[0036] The storage unit can analyze the user's communication pattern in real time at the time of saving, and can save specific data preferentially. The storage unit can, for example, analyze phrases and wording frequently used by the user, and save data related thereto preferentially. In addition, when the user frequently interacts with a specific person, the storage unit can also save data of the interaction preferentially. Furthermore, when the user frequently talks about a specific topic, the storage unit can also save data related to the topic preferentially. This makes it possible to analyze the user's communication pattern in real time, and to save important data preferentially. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can cause the generation AI to analyze the user's communication pattern, and cause the generation AI to save important data preferentially.

[0037] The storage unit can adjust the frequency of data storage based on the user's life situation and activity history at the time of storage. For example, when the user is busy, the storage unit stores only important data and reduces the storage frequency. In addition, the storage unit can frequently store detailed data when the user is relaxed. Furthermore, the storage unit can increase the storage frequency during a specific time period based on the user's activity history. This makes it possible to adjust the frequency of data storage based on the user's life situation and activity history. Part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the user's activity history data to the generation AI and cause the generation AI to adjust the storage frequency.

[0038] The storage unit can, at the time of storage, preferentially store highly relevant data in consideration of the geographical location information of the user. For example, when the user is traveling, the storage unit preferentially stores conversations and photos at the travel destination. In addition, when the user is at home, the storage unit can also preferentially store daily conversations and activities. Furthermore, when the user is participating in a specific event, the storage unit can also preferentially store data related to the event. This makes it possible to preferentially store highly relevant data in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute storage of highly relevant data.

[0039] At the time of saving, the storage unit can analyze the user's social media activity and save specific data. The storage unit can, for example, save photos and videos shared by the user on social media. The storage unit can also analyze the user's interactions on social media and save important conversations. Furthermore, the storage unit can save related data based on the user's activity history on social media. This makes it possible to analyze the user's social media activity and save related data. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using AI or can be performed without using AI. For example, the storage unit can input the user's social media activity data to the generation AI and cause the generation AI to save the related data.

[0040] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past communication history. The analysis unit can improve the accuracy of the reply by, for example, referring to phrases and wording used by the user in the past. In addition, when the user frequently interacts with a specific person, the analysis unit can also generate a reply by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the analysis unit can also generate a reply by referring to past interactions related to the topic. This can improve the accuracy of the analysis by referring to the user's past communication history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past communication history data to the generation AI to improve the accuracy of the analysis in the generation AI.

[0041] During the analysis, the analysis unit can learn the user's reaction to a specific topic and improve the accuracy of the response. For example, when a user has a positive reaction to a specific topic, the analysis unit learns the reaction and generates a response. In addition, when a user has a negative reaction to a specific topic, the analysis unit can also learn the reaction and generate a response. Furthermore, when a user has a neutral reaction to a specific topic, the analysis unit can also learn the reaction and generate a response. In this way, the user's reaction to a specific topic can be learned and the accuracy of the response can be improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's reaction data to a specific topic into the generation AI and improve the accuracy of the response to the generation AI.

[0042] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the user. For example, if the user lives in a specific area, the analysis unit preferentially analyzes information related to that area. In addition, if the user is traveling, the analysis unit can also preferentially analyze information related to the travel destination. Furthermore, if the user frequently visits a specific area, the analysis unit can also preferentially analyze information related to that area. This makes it possible to perform the analysis while taking into consideration the geographical distribution of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of the user to the generation AI and cause the generation AI to execute the analysis.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. For example, when the user talks about a specific topic, the analysis unit performs the analysis by referring to literature related to the topic. In addition, when the user is knowledgeable about a specific field, the analysis unit can also perform the analysis by referring to literature related to the field. Furthermore, when the user frequently talks about a specific topic, the analysis unit can also perform the analysis by referring to literature related to the topic. This can improve the accuracy of the analysis by referring to the user's related literature. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's related literature data into the generation AI to improve the accuracy of the analysis in the generation AI.

[0044] The providing unit can provide an appropriate reply by referring to the user's past communication history at the time of providing. The providing unit provides the reply by referring to, for example, phrases and wording used by the user in the past. In addition, when the user frequently interacts with a specific person, the providing unit can also provide a reply by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the providing unit can also provide a reply by referring to past interactions related to the topic. This makes it possible to provide an optimal reply by referring to the user's past communication history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past communication history data to the generating AI and cause the generating AI to provide an appropriate reply.

[0045] The providing unit can learn the user's reaction to a specific topic at the time of providing and improve the accuracy of the reply. For example, when the user shows a positive reaction to a specific topic, the providing unit learns the reaction and provides a reply. In addition, when the user shows a negative reaction to a specific topic, the providing unit can also learn the reaction and provide a reply. Furthermore, when the user shows a neutral reaction to a specific topic, the providing unit can also learn the reaction and provide a reply. In this way, the user's reaction to a specific topic can be learned and the accuracy of the reply can be improved. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's reaction data to a specific topic to the generating AI and improve the accuracy of the reply to the generating AI.

[0046] The providing unit can provide an appropriate response in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit provides a response including information related to the area. In addition, when the user is traveling, the providing unit can also provide a response including information related to the travel destination. Furthermore, when the user frequently visits a specific area, the providing unit can also provide a response including information related to the area. This makes it possible to provide an optimal response in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide an appropriate response.

[0047] The providing unit can analyze the user's social media activity and provide a relevant response at the time of providing. The providing unit can provide a response based on, for example, information shared by the user on social media. The providing unit can also analyze the user's interactions on social media and provide a response based on important conversations. Furthermore, the providing unit can also provide a relevant response based on the user's activity history on social media. This makes it possible to analyze the user's social media activity and provide a relevant response. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to provide a relevant response.

[0048] The protection unit can select an appropriate protection method by referring to the user's past data usage history during privacy protection. For example, if the user has frequently used specific data in the past, the protection unit increases the protection level of the data. In addition, if the user has hardly used specific data in the past, the protection unit can set the protection level of the data to normal. Furthermore, the protection unit can analyze the user's past data usage history and select an optimal protection method. This makes it possible to select an optimal protection method by referring to the user's past data usage history. Part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI or may be performed without using AI. For example, the protection unit can input the user's past data usage history data to the generation AI and have the generation AI select an appropriate protection method.

[0049] The protection unit can select an appropriate protection method in consideration of the geographical location information of the user during privacy protection. For example, when the user is in a specific area, the protection unit selects a protection method based on the privacy regulations of the area. In addition, when the user is traveling, the protection unit can also select a protection method based on the privacy regulations of the travel destination. Furthermore, when the user frequently visits a specific area, the protection unit can also select a protection method based on the privacy regulations of the area. This makes it possible to select an optimal protection method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an appropriate protection method.

[0050] The deletion unit can select an appropriate deletion method by referring to the user's past data usage history when deleting data. For example, if the user has frequently used specific data in the past, the deletion unit carefully selects a deletion method for the data. In addition, if the user has hardly used specific data in the past, the deletion unit can also set the deletion method for the data to normal. Furthermore, the deletion unit can analyze the user's past data usage history and select an optimal deletion method. This makes it possible to select an optimal deletion method by referring to the user's past data usage history. Part or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI or may be performed without using AI. For example, the deletion unit can input the user's past data usage history data to the generation AI and have the generation AI select an appropriate deletion method.

[0051] The deletion unit can select an appropriate deletion method in consideration of the geographical location information of the user when deleting data. For example, when the user is in a specific area, the deletion unit selects the deletion method based on the data deletion regulations of the area. In addition, when the user is traveling, the deletion unit can also select the deletion method based on the data deletion regulations of the travel destination. Furthermore, when the user frequently visits a specific area, the deletion unit can also select the deletion method based on the data deletion regulations of the area. This makes it possible to select the optimal deletion method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an appropriate deletion method.

[0052] The reproduction unit can select an appropriate reproduction method by referring to the user's past communication history at the time of reproduction. The reproduction unit reproduces, for example, by referring to phrases and expressions used by the user in the past. In addition, when the user frequently interacts with a specific person, the reproduction unit can also reproduce by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the reproduction unit can also reproduce by referring to past interactions related to the topic. This makes it possible to select an optimal reproduction method by referring to the user's past communication history. A part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI or may be performed without using AI. For example, the reproduction unit inputs the user's past communication history data to the generation AI and causes the generation AI to select an appropriate reproduction method.

[0053] The reproduction unit can learn the user's reaction to a specific topic during reproduction and improve the accuracy of reproduction. For example, when a user shows a positive reaction to a specific topic, the reproduction unit learns the reaction and reproduces it. In addition, when a user shows a negative reaction to a specific topic, the reproduction unit can also learn and reproduce the reaction. Furthermore, when a user shows a neutral reaction to a specific topic, the reproduction unit can also learn and reproduce the reaction. In this way, the user's reaction to a specific topic can be learned and the accuracy of reproduction can be improved. A part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI or may be performed without using AI. For example, the reproduction unit can input the user's reaction data to a specific topic to the generation AI and improve the accuracy of reproduction in the generation AI.

[0054] The reproduction unit can select an appropriate reproduction method in consideration of the geographical location information of the user at the time of reproduction. For example, when the user is in a specific area, the reproduction unit performs reproduction including information related to the area. In addition, when the user is traveling, the reproduction unit can also perform reproduction including information related to the travel destination. Furthermore, when the user frequently visits a specific area, the reproduction unit can also perform reproduction including information related to the area. This makes it possible to select an optimal reproduction method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the reproduction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an appropriate reproduction method.

[0055] The reproducing unit can analyze the user's social media activities at the time of reproduction and provide a related reproduction method. The reproducing unit performs reproduction based on, for example, information shared by the user on social media. The reproducing unit can also analyze the user's interactions on social media and perform reproduction based on important conversations. Furthermore, the reproducing unit can perform related reproduction based on the user's activity history on social media. This makes it possible to analyze the user's social media activities and provide a related reproduction method. A part or all of the above-mentioned processing in the reproducing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproducing unit can input the user's social media activity data to the generating AI and cause the generating AI to provide a related reproduction method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The storage unit can preferentially store highly relevant data in consideration of the geographical location information of the user. For example, when the user is traveling, conversations and photos at the travel destination are preferentially stored. In addition, when the user is at home, the storage unit can also preferentially store daily conversations and activities. Furthermore, when the user is participating in a specific event, the storage unit can also preferentially store data related to the event. This makes it possible to preferentially store highly relevant data in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the storage of highly relevant data.

[0058] At the time of saving, the storage unit can analyze the user's social media activity and save specific data. For example, the storage unit can save photos and videos shared by the user on social media. The storage unit can also analyze the user's interactions on social media and save important conversations. Furthermore, the storage unit can save related data based on the user's social media activity history. This makes it possible to analyze the user's social media activity and save related data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's social media activity data to the generation AI and cause the generation AI to save the related data.

[0059] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past communication history. For example, the accuracy of the reply can be improved by referring to phrases and wording used by the user in the past. In addition, when the user frequently interacts with a specific person, the analysis unit can also generate a reply by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the analysis unit can also generate a reply by referring to past interactions related to the topic. This can improve the accuracy of the analysis by referring to the user's past communication history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past communication history data to the generation AI to improve the accuracy of the analysis in the generation AI.

[0060] The providing unit can provide an appropriate response in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit can provide a response including information related to the area. In addition, when the user is traveling, the providing unit can also provide a response including information related to the travel destination. Furthermore, when the user frequently visits a specific area, the providing unit can also provide a response including information related to the area. This makes it possible to provide an optimal response in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide an appropriate response.

[0061] The protection unit can select an appropriate protection method by referring to the user's past data usage history during privacy protection. For example, if the user has frequently used specific data in the past, the protection unit can increase the protection level of the data. In addition, if the user has hardly used specific data in the past, the protection unit can set the protection level of the data to normal. Furthermore, the protection unit can analyze the user's past data usage history and select an optimal protection method. This makes it possible to select an optimal protection method by referring to the user's past data usage history. Part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI or may be performed without using AI. For example, the protection unit can input the user's past data usage history data to the generation AI and have the generation AI select an appropriate protection method.

[0062] The deletion unit can select an appropriate deletion method in consideration of the geographical location information of the user when deleting data. For example, when the user is in a specific area, the deletion unit selects the deletion method based on the data deletion regulations of the area. In addition, when the user is traveling, the deletion unit can also select the deletion method based on the data deletion regulations of the travel destination. Furthermore, when the user frequently visits a specific area, the deletion unit can also select the deletion method based on the data deletion regulations of the area. This makes it possible to select the optimal deletion method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit inputs the geographical location information of the user to the generation AI and causes the generation AI to select an appropriate deletion method.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The storage unit stores the user's text information, video information, and call information. The user's text information includes chat history from emails and messenger apps, the video information includes recordings of video calls, and the call information includes recordings of voice calls. The storage unit automatically stores this data and uses it to learn the user's communication patterns, language choices, tone of voice, etc. Step 2: The analysis unit analyzes the data stored by the storage unit and generates a response. The analysis unit learns the user's frequently used phrases and reactions to specific topics, and generates a response based on this. The generation AI is used to infer what kind of response the user would make if they were deceased. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and it learns the user's communication patterns and generates an appropriate response. Step 3: The provision unit provides the response generated by the analysis unit. The provision unit provides the AI-generated response in chats or calls on the messenger app. When the user's friends or family start a chat on the messenger app, the AI ​​responds as if the deceased were speaking. The provision unit also reproduces the voice and image of the deceased in voice and video calls, providing an experience as if one is talking to the deceased.

[0065] (Example 2) The system for preparing for isolation and loneliness according to the embodiment of the present invention is a system that stores the text information, video information, and call information of a user, infers how an AI would respond if the user were deceased, and provides a response as if the user were talking to the deceased through chat or call on a messenger app. This system enters into a contract with a user in advance in a subscription format, and stores the user's text information (email or chat on a messenger app), video information, and call information such as voice and video calls from the date of contract conclusion. Based on the stored data, the system is a service that infers how an AI would respond if the user were deceased, and provides a response as if the user were talking to the deceased through chat or call on a messenger app. For example, a user registers for the service and concludes a contract. After the contract is concluded, the user's text information (email or chat on a messenger app), video information, and call information such as voice and video calls are automatically saved. These data are used to learn the user's communication patterns, language, tone of voice, etc. Next, the AI ​​analyzes the saved data and infers how the user would respond if the user were deceased. For example, it learns the user's frequently used phrases and reactions to specific topics, and generates replies based on this. Finally, the AI-generated replies are provided in chats and calls on the messenger app. For example, when the user's friends or family start a chat on the messenger app, the AI ​​responds as if the deceased were speaking. In voice and video calls, the AI ​​also reproduces the voice and image of the deceased, providing an experience as if they were talking to the deceased. This allows the user's friends and family to spend time without feeling isolated or lonely while reminiscing about their memories with the deceased. In this way, the system to prepare for isolation and loneliness allows the user's friends and family to reproduce conversations with the deceased, allowing them to spend time without feeling isolated or lonely.

[0066] The system for preparing for isolation and loneliness according to the embodiment includes a storage unit, an analysis unit, and a provision unit. The storage unit stores text information, video information, and call information of a user. The text information of a user includes, for example, chat history of emails and messenger apps. The video information includes, for example, recordings of video calls. The call information includes, for example, recordings of voice calls. The storage unit automatically stores these data and uses them to learn the user's communication patterns, language, tone of voice, and the like. The analysis unit analyzes the data stored by the storage unit and generates a response. The analysis unit learns, for example, phrases that the user frequently uses and reactions to specific topics, and generates a response based on this. The analysis unit uses a generation AI to infer what kind of response the user would make if the user were deceased. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and learns the user's communication patterns and generates an appropriate response. The provision unit provides the response generated by the analysis unit. The providing unit provides a response generated by the AI, for example, in a chat or call on a messenger app. When the user's friends or family start a chat on a messenger app, the providing unit has the AI ​​respond as if the deceased were the deceased. In addition, the providing unit also reproduces the voice and image of the deceased in a voice call or video call, providing an experience as if one were talking to the deceased. In this way, the system for preparing for isolation and loneliness according to the embodiment allows the user's friends and family to reproduce conversations with the deceased, allowing the user to spend time without feeling isolated or lonely.

[0067] The storage unit stores the user's text information, video information, and call information. The user's text information includes, for example, chat history of emails and messenger apps. These text information are part of the user's daily communication and record the user's language and expression in detail. The storage unit automatically collects these text information and stores them in a database. The video information includes, for example, recordings of video calls. Recordings of video calls include non-verbal communication elements that cannot be captured by text information, such as the user's facial expressions, gestures, and tone of voice. This allows the storage unit to store the user's communication style more comprehensively. The call information includes, for example, recordings of voice calls. Recordings of voice calls record the user's tone of voice, speaking style, emotional changes, and the like in detail. The storage unit automatically stores these data and uses them to learn the user's communication patterns, language, tone of voice, and the like. The storage unit centrally manages these data and allows the analysis unit and provision unit to access them as necessary. Furthermore, the storage unit allows the user to customize the data storage period and privacy settings, thereby protecting the user's privacy. This allows the storage unit to store the user's communication data efficiently and safely, improving the performance of the entire system.

[0068] The analysis unit analyzes the data stored by the storage unit and generates a response. The analysis unit learns, for example, the user's frequently used phrases and reactions to specific topics, and generates a response based on this. Specifically, the analysis unit uses a generation AI to analyze the user's communication pattern in detail. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and comprehensively analyzes the user's text information, video information, and call information. The text generation AI analyzes the user's chat history and email content to learn the user's frequently used phrases and reactions to specific topics. The multimodal generation AI analyzes recordings of video calls and voice calls to learn the user's facial expressions, tone of voice, gestures, etc. This allows the analysis unit to infer what kind of response the user would make if the user were deceased. The analysis unit uses the generation AI to reproduce the user's communication pattern and generate an appropriate response. For example, a natural conversation is generated based on the user's frequently used phrases and reactions to specific topics. The analysis unit also utilizes emotion and intent analysis techniques to understand the user's emotions and intentions and generate appropriate responses, allowing the analysis unit to faithfully reproduce the user's communication style and provide a natural conversation.

[0069] The providing unit provides the response generated by the analysis unit. For example, the providing unit provides the response generated by the AI ​​in a chat or call on a messenger app. Specifically, when the user's friends or family start a chat on a messenger app, the providing unit has the AI ​​respond as if the deceased. The providing unit receives data from the analysis unit in real time and provides responses at the appropriate time in order to reproduce the user's communication pattern and provide a natural conversation. In addition, the providing unit also reproduces the voice and image of the deceased in voice calls and video calls, providing an experience as if the user was talking to the deceased. In voice calls, the AI ​​reproduces the user's tone of voice and speaking style to provide a natural conversation. In video calls, the AI ​​reproduces the user's facial expressions and gestures to provide a more realistic experience. Furthermore, the providing unit provides responses at the appropriate time so that the user's friends and family do not feel isolated or lonely. For example, on certain anniversaries or events, the AI ​​can send messages as if the deceased, allowing the user's friends and family to feel connected to the deceased. This allows the providing unit to re-create conversations between the user's friends and family and the deceased, allowing them to spend time without feeling isolated or lonely.

[0070] Furthermore, the system for preparing for isolation and loneliness includes a protection unit that protects the privacy of the stored data. The protection unit protects the privacy of the stored data. The protection unit protects privacy using, for example, data encryption, access restriction, anonymization technology, and the like. The data encryption encrypts the stored data and protects it from unauthorized access. The access restriction restricts access to the data so that only specific users can access it. The anonymization technology deletes information that can identify an individual from the data and protects privacy. This makes it possible to protect the privacy of the stored data. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can cause AI to encrypt the data and set access restrictions.

[0071] Furthermore, the system for preparing for isolation and loneliness includes a deletion unit that allows a user to request the deletion of data. The deletion unit allows a user to request the deletion of data. The deletion unit provides, for example, a procedure by which a user requests the deletion of data. The deletion unit promptly deletes data requested by a user to be deleted. The deletion unit clarifies the range of data to be deleted and the timing of deletion. This allows a user to request the deletion of data. Some or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit can have AI analyze the user's deletion request and delete appropriate data.

[0072] Furthermore, the system for preparing for isolation and loneliness includes a reproduction unit in which the AI ​​reproduces the user's voice and image. The reproduction unit reproduces the user's voice and image using AI. The reproduction unit reproduces the user's voice and image using, for example, voice synthesis technology or image generation technology. The voice synthesis technology reproduces the user's voice based on stored voice data. The image generation technology reproduces the user's image based on stored image data. The reproduction unit analyzes the stored data in detail to improve the reproduction accuracy of the user's voice and image. This allows the AI ​​to reproduce the user's voice and image. A part or all of the above-mentioned processing in the reproduction unit is performed using a generation AI. For example, the reproduction unit can input the stored voice data to the generation AI and cause the generation AI to reproduce the user's voice. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, and reproduces the user's voice and image with high accuracy.

[0073] The storage unit can estimate the user's emotion and adjust the type of data to be stored based on the estimated user's emotion. For example, when the user is sad, the storage unit preferentially stores text and voice data expressing the emotion. In addition, when the user is happy, the storage unit can also preferentially store videos and images with positive content. Furthermore, when the user is stressed, the storage unit can also preferentially store relaxing conversation and music data. This makes it possible to adjust the type of data to be stored based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the type of data to be stored.

[0074] The storage unit can analyze the user's communication pattern in real time at the time of saving, and can save specific data preferentially. The storage unit can, for example, analyze phrases and wording frequently used by the user, and save data related thereto preferentially. In addition, when the user frequently interacts with a specific person, the storage unit can also save data of the interaction preferentially. Furthermore, when the user frequently talks about a specific topic, the storage unit can also save data related to the topic preferentially. This makes it possible to analyze the user's communication pattern in real time, and to save important data preferentially. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can cause the generation AI to analyze the user's communication pattern, and cause the generation AI to save important data preferentially.

[0075] The storage unit can adjust the frequency of data storage based on the user's life situation and activity history at the time of storage. For example, when the user is busy, the storage unit stores only important data and reduces the storage frequency. In addition, the storage unit can frequently store detailed data when the user is relaxed. Furthermore, the storage unit can increase the storage frequency during a specific time period based on the user's activity history. This makes it possible to adjust the frequency of data storage based on the user's life situation and activity history. Part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the user's activity history data to the generation AI and cause the generation AI to adjust the storage frequency.

[0076] The storage unit can estimate the user's emotion and determine the priority of data to be stored based on the estimated user's emotion. For example, when the user is in an emotional state, the storage unit preferentially stores data that represents the emotion. In addition, when the user is in a calm state, the storage unit can also preferentially store daily conversation data. Furthermore, when the user is excited, the storage unit can also preferentially store data that represents the excitement. This makes it possible to determine the priority of data to be stored based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of data to be stored.

[0077] The storage unit can, at the time of storage, preferentially store highly relevant data in consideration of the geographical location information of the user. For example, when the user is traveling, the storage unit preferentially stores conversations and photos at the travel destination. In addition, when the user is at home, the storage unit can also preferentially store daily conversations and activities. Furthermore, when the user is participating in a specific event, the storage unit can also preferentially store data related to the event. This makes it possible to preferentially store highly relevant data in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute storage of highly relevant data.

[0078] At the time of saving, the storage unit can analyze the user's social media activity and save specific data. The storage unit can, for example, save photos and videos shared by the user on social media. The storage unit can also analyze the user's interactions on social media and save important conversations. Furthermore, the storage unit can save related data based on the user's activity history on social media. This makes it possible to analyze the user's social media activity and save related data. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using AI or can be performed without using AI. For example, the storage unit can input the user's social media activity data to the generation AI and cause the generation AI to save the related data.

[0079] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is sad, the analysis unit uses an algorithm that generates a response that eases the emotion. In addition, when the user is happy, the analysis unit can also use an algorithm that generates a positive response. Furthermore, when the user is stressed, the analysis unit can also use an algorithm that generates a response that relaxes the user. This allows the analysis algorithm to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0080] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past communication history. The analysis unit can improve the accuracy of the reply by, for example, referring to phrases and wording used by the user in the past. In addition, when the user frequently interacts with a specific person, the analysis unit can also generate a reply by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the analysis unit can also generate a reply by referring to past interactions related to the topic. This can improve the accuracy of the analysis by referring to the user's past communication history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's past communication history data to the generation AI to improve the accuracy of the analysis in the generation AI.

[0081] During the analysis, the analysis unit can learn the user's reaction to a specific topic and improve the accuracy of the response. For example, when a user has a positive reaction to a specific topic, the analysis unit learns the reaction and generates a response. In addition, when a user has a negative reaction to a specific topic, the analysis unit can also learn the reaction and generate a response. Furthermore, when a user has a neutral reaction to a specific topic, the analysis unit can also learn the reaction and generate a response. In this way, the user's reaction to a specific topic can be learned and the accuracy of the response can be improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's reaction data to a specific topic into the generation AI and improve the accuracy of the response to the generation AI.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0083] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the user. For example, if the user lives in a specific area, the analysis unit preferentially analyzes information related to that area. In addition, if the user is traveling, the analysis unit can also preferentially analyze information related to the travel destination. Furthermore, if the user frequently visits a specific area, the analysis unit can also preferentially analyze information related to that area. This makes it possible to perform the analysis while taking into consideration the geographical distribution of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of the user to the generation AI and cause the generation AI to execute the analysis.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. For example, when the user talks about a specific topic, the analysis unit performs the analysis by referring to literature related to the topic. In addition, when the user is knowledgeable about a specific field, the analysis unit can also perform the analysis by referring to literature related to the field. Furthermore, when the user frequently talks about a specific topic, the analysis unit can also perform the analysis by referring to literature related to the topic. This can improve the accuracy of the analysis by referring to the user's related literature. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's related literature data into the generation AI to improve the accuracy of the analysis in the generation AI.

[0085] The providing unit can estimate the user's emotion and adjust the way of expressing the reply based on the estimated emotion of the user. For example, when the user is sad, the providing unit provides the reply in a gentle manner. Also, when the user is happy, the providing unit can provide the reply in a cheerful manner. Furthermore, when the user is stressed, the providing unit can provide the reply in a relaxing manner. This makes it possible to adjust the way of expressing the reply based on the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the reply.

[0086] The providing unit can provide an appropriate reply by referring to the user's past communication history at the time of providing. The providing unit provides the reply by referring to, for example, phrases and wording used by the user in the past. In addition, when the user frequently interacts with a specific person, the providing unit can also provide a reply by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the providing unit can also provide a reply by referring to past interactions related to the topic. This makes it possible to provide an optimal reply by referring to the user's past communication history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past communication history data to the generating AI and cause the generating AI to provide an appropriate reply.

[0087] The providing unit can learn the user's reaction to a specific topic at the time of providing and improve the accuracy of the reply. For example, when the user shows a positive reaction to a specific topic, the providing unit learns the reaction and provides a reply. In addition, when the user shows a negative reaction to a specific topic, the providing unit can also learn the reaction and provide a reply. Furthermore, when the user shows a neutral reaction to a specific topic, the providing unit can also learn the reaction and provide a reply. In this way, the user's reaction to a specific topic can be learned and the accuracy of the reply can be improved. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's reaction data to a specific topic to the generating AI and improve the accuracy of the reply to the generating AI.

[0088] The providing unit can estimate the user's emotion and determine the priority of replies based on the estimated user's emotion. For example, when the user is in an emotional state, the providing unit preferentially provides a reply corresponding to the emotion. In addition, when the user is in a calm state, the providing unit can also preferentially provide a daily reply. Furthermore, when the user is excited, the providing unit can also preferentially provide a reply corresponding to the excitement. This makes it possible to determine the priority of replies based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of replies.

[0089] The providing unit can provide an appropriate response in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit provides a response including information related to the area. In addition, when the user is traveling, the providing unit can also provide a response including information related to the travel destination. Furthermore, when the user frequently visits a specific area, the providing unit can also provide a response including information related to the area. This makes it possible to provide an optimal response in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide an appropriate response.

[0090] The providing unit can analyze the user's social media activity and provide a relevant response at the time of providing. The providing unit can provide a response based on, for example, information shared by the user on social media. The providing unit can also analyze the user's interactions on social media and provide a response based on important conversations. Furthermore, the providing unit can also provide a relevant response based on the user's activity history on social media. This makes it possible to analyze the user's social media activity and provide a relevant response. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to provide a relevant response.

[0091] The protection unit can estimate the user's emotion and adjust the level of privacy protection based on the estimated user's emotion. For example, the protection unit increases the level of privacy protection when the user feels anxious. The protection unit can also set the level of privacy protection to normal when the user feels relaxed. Furthermore, the protection unit can also strengthen the level of privacy protection when the user feels stressed. This makes it possible to adjust the level of privacy protection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input the user's emotion data to the generation AI and have the generation AI adjust the level of privacy protection.

[0092] The protection unit can select an appropriate protection method by referring to the user's past data usage history during privacy protection. For example, if the user has frequently used specific data in the past, the protection unit increases the protection level of the data. In addition, if the user has hardly used specific data in the past, the protection unit can set the protection level of the data to normal. Furthermore, the protection unit can analyze the user's past data usage history and select an optimal protection method. This makes it possible to select an optimal protection method by referring to the user's past data usage history. Part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI or may be performed without using AI. For example, the protection unit can input the user's past data usage history data to the generation AI and have the generation AI select an appropriate protection method.

[0093] The protection unit can estimate the user's emotion and determine the priority of privacy protection based on the estimated user's emotion. For example, the protection unit increases the priority of privacy protection when the user feels anxious. The protection unit can also set the priority of privacy protection to normal when the user feels relaxed. Furthermore, the protection unit can also strengthen the priority of privacy protection when the user feels stressed. This makes it possible to determine the priority of privacy protection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of privacy protection.

[0094] The protection unit can select an appropriate protection method in consideration of the geographical location information of the user during privacy protection. For example, when the user is in a specific area, the protection unit selects a protection method based on the privacy regulations of the area. In addition, when the user is traveling, the protection unit can also select a protection method based on the privacy regulations of the travel destination. Furthermore, when the user frequently visits a specific area, the protection unit can also select a protection method based on the privacy regulations of the area. This makes it possible to select an optimal protection method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an appropriate protection method.

[0095] The deletion unit can estimate the user's emotion and adjust the timing of data deletion based on the estimated user's emotion. For example, when the user feels anxious, the deletion unit can advance the timing of data deletion. In addition, when the user feels relaxed, the deletion unit can also set the timing of data deletion to normal. Furthermore, when the user feels stressed, the deletion unit can adjust the timing of data deletion. This makes it possible to adjust the timing of data deletion based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the deletion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the deletion unit can input the user's emotion data to the generation AI and have the generation AI adjust the timing of data deletion.

[0096] The deletion unit can select an appropriate deletion method by referring to the user's past data usage history when deleting data. For example, if the user has frequently used specific data in the past, the deletion unit carefully selects a deletion method for the data. In addition, if the user has hardly used specific data in the past, the deletion unit can also set the deletion method for the data to normal. Furthermore, the deletion unit can analyze the user's past data usage history and select an optimal deletion method. This makes it possible to select an optimal deletion method by referring to the user's past data usage history. Part or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI or may be performed without using AI. For example, the deletion unit can input the user's past data usage history data to the generation AI and have the generation AI select an appropriate deletion method.

[0097] The deletion unit can estimate the user's emotion and determine the priority of data deletion based on the estimated user's emotion. For example, when the user feels anxious, the deletion unit increases the priority of data deletion. In addition, when the user feels relaxed, the deletion unit can also set the priority of data deletion to normal. Furthermore, when the user feels stressed, the deletion unit can also strengthen the priority of data deletion. This makes it possible to determine the priority of data deletion based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the deletion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the deletion unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of data deletion.

[0098] The deletion unit can select an appropriate deletion method in consideration of the geographical location information of the user when deleting data. For example, when the user is in a specific area, the deletion unit selects the deletion method based on the data deletion regulations of the area. In addition, when the user is traveling, the deletion unit can also select the deletion method based on the data deletion regulations of the travel destination. Furthermore, when the user frequently visits a specific area, the deletion unit can also select the deletion method based on the data deletion regulations of the area. This makes it possible to select the optimal deletion method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an appropriate deletion method.

[0099] The reproduction unit can estimate the user's emotion and adjust the reproduction method of the voice and image based on the estimated user's emotion. For example, when the user is sad, the reproduction unit reproduces the user with a gentle voice and image. Also, when the user is happy, the reproduction unit can reproduce the user with a bright voice and image. Furthermore, when the user is stressed, the reproduction unit can reproduce the user with a voice and image that relaxes the user. This makes it possible to adjust the reproduction method of the voice and image based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reproduction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the reproduction method of the voice and image.

[0100] The reproduction unit can select an appropriate reproduction method by referring to the user's past communication history at the time of reproduction. The reproduction unit reproduces, for example, by referring to phrases and expressions used by the user in the past. In addition, when the user frequently interacts with a specific person, the reproduction unit can also reproduce by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the reproduction unit can also reproduce by referring to past interactions related to the topic. This makes it possible to select an optimal reproduction method by referring to the user's past communication history. A part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI or may be performed without using AI. For example, the reproduction unit inputs the user's past communication history data to the generation AI and causes the generation AI to select an appropriate reproduction method.

[0101] The reproduction unit can learn the user's reaction to a specific topic during reproduction and improve the accuracy of reproduction. For example, when a user shows a positive reaction to a specific topic, the reproduction unit learns the reaction and reproduces it. In addition, when a user shows a negative reaction to a specific topic, the reproduction unit can also learn and reproduce the reaction. Furthermore, when a user shows a neutral reaction to a specific topic, the reproduction unit can also learn and reproduce the reaction. In this way, the user's reaction to a specific topic can be learned and the accuracy of reproduction can be improved. A part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI or may be performed without using AI. For example, the reproduction unit can input the user's reaction data to a specific topic to the generation AI and improve the accuracy of reproduction in the generation AI.

[0102] The reproduction unit can estimate the user's emotion and determine the priority of reproduction based on the estimated user's emotion. For example, when the user is in an emotional state, the reproduction unit prioritizes reproduction corresponding to the emotion. In addition, when the user is in a calm state, the reproduction unit can also prioritize daily reproduction. Furthermore, when the user is excited, the reproduction unit can also prioritize reproduction corresponding to the excitement. This makes it possible to determine the priority of reproduction based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reproduction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of reproduction.

[0103] The reproduction unit can select an appropriate reproduction method in consideration of the geographical location information of the user at the time of reproduction. For example, when the user is in a specific area, the reproduction unit performs reproduction including information related to the area. In addition, when the user is traveling, the reproduction unit can also perform reproduction including information related to the travel destination. Furthermore, when the user frequently visits a specific area, the reproduction unit can also perform reproduction including information related to the area. This makes it possible to select an optimal reproduction method in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the reproduction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an appropriate reproduction method.

[0104] The reproducing unit can analyze the user's social media activities at the time of reproduction and provide a related reproduction method. The reproducing unit performs reproduction based on, for example, information shared by the user on social media. The reproducing unit can also analyze the user's interactions on social media and perform reproduction based on important conversations. Furthermore, the reproducing unit can perform related reproduction based on the user's activity history on social media. This makes it possible to analyze the user's social media activities and provide a related reproduction method. A part or all of the above-mentioned processing in the reproducing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproducing unit can input the user's social media activity data to the generating AI and cause the generating AI to provide a related reproduction method.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is sad, an algorithm that generates a response that eases the user's emotions is used. Also, if the user is happy, an algorithm that generates a positive response can be used. Furthermore, if the user is stressed, an algorithm that generates a response that relaxes the user can be used. This allows the analysis algorithm to be adjusted based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0107] The storage unit can preferentially store highly relevant data in consideration of the geographical location information of the user. For example, when the user is traveling, conversations and photos at the travel destination are preferentially stored. In addition, when the user is at home, the storage unit can also preferentially store daily conversations and activities. Furthermore, when the user is participating in a specific event, the storage unit can also preferentially store data related to the event. This makes it possible to preferentially store highly relevant data in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the storage of highly relevant data.

[0108] The providing unit can estimate the user's emotion and adjust the way of expressing the reply based on the estimated emotion of the user. For example, if the user is sad, the reply can be provided in gentle language. Also, if the user is happy, the reply can be provided in cheerful language. Furthermore, if the user is stressed, the reply can be provided in relaxing language. This allows the way of expressing the reply to be adjusted based on the emotion of the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing the reply.

[0109] The protection unit can estimate the user's emotion and adjust the level of privacy protection based on the estimated user's emotion. For example, if the user feels anxious, the level of privacy protection is increased. In addition, the protection unit can also set the level of privacy protection to normal if the user is relaxed. Furthermore, the protection unit can also strengthen the level of privacy protection if the user is stressed. This makes it possible to adjust the level of privacy protection based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input the user's emotion data to the generation AI and have the generation AI adjust the level of privacy protection.

[0110] The reproduction unit can estimate the user's emotion and adjust the reproduction method of the voice and image based on the estimated user's emotion. For example, when the user is sad, the reproduction unit reproduces the user with a gentle voice and image. Moreover, when the user is happy, the reproduction unit can reproduce the user with a bright voice and image. Furthermore, when the user is stressed, the reproduction unit can reproduce the user with a voice and image that relaxes the user. This makes it possible to adjust the reproduction method of the voice and image based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reproduction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the reproduction method of the voice and image.

[0111] At the time of saving, the storage unit can analyze the user's social media activity and save specific data. For example, the storage unit can save photos and videos shared by the user on social media. The storage unit can also analyze the user's interactions on social media and save important conversations. Furthermore, the storage unit can save related data based on the user's social media activity history. This makes it possible to analyze the user's social media activity and save related data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's social media activity data to the generation AI and cause the generation AI to save the related data.

[0112] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past communication history. For example, the accuracy of the reply can be improved by referring to phrases and wording used by the user in the past. In addition, when the user frequently interacts with a specific person, the analysis unit can also generate a reply by referring to past interactions with the person. Furthermore, when the user frequently talks about a specific topic, the analysis unit can also generate a reply by referring to past interactions related to the topic. This can improve the accuracy of the analysis by referring to the user's past communication history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past communication history data to the generation AI to improve the accuracy of the analysis in the generation AI.

[0113] The providing unit can provide an appropriate response in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit can provide a response including information related to the area. In addition, when the user is traveling, the providing unit can also provide a response including information related to the travel destination. Furthermore, when the user frequently visits a specific area, the providing unit can also provide a response including information related to the area. This makes it possible to provide an optimal response in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide an appropriate response.

[0114] The protection unit can select an appropriate protection method by referring to the user's past data usage history during privacy protection. For example, if the user has frequently used specific data in the past, the protection unit can increase the protection level of the data. In addition, if the user has hardly used specific data in the past, the protection unit can set the protection level of the data to normal. Furthermore, the protection unit can analyze the user's past data usage history and select an optimal protection method. This makes it possible to select an optimal protection method by referring to the user's past data usage history. Part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI or may be performed without using AI. For example, the protection unit can input the user's past data usage history data to the generation AI and have the generation AI select an appropriate protection method.

[0115] The deletion unit can select an appropriate deletion method in consideration of the geographical location information of the user when deleting data. For example, when the user is in a specific area, the deletion unit selects the deletion method based on the data deletion regulations of the area. In addition, when the user is traveling, the deletion unit can also select the deletion method based on the data deletion regulations of the travel destination. Furthermore, when the user frequently visits a specific area, the deletion unit can also select the deletion method based on the data deletion regulations of the area. This makes it possible to select the optimal deletion method in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit inputs the geographical location information of the user to the generation AI and causes the generation AI to select an appropriate deletion method.

[0116] The process flow of the second embodiment will be briefly described below.

[0117] Step 1: The storage unit stores the user's text information, video information, and call information. The user's text information includes chat history from emails and messenger apps, the video information includes recordings of video calls, and the call information includes recordings of voice calls. The storage unit automatically stores this data and uses it to learn the user's communication patterns, language choices, tone of voice, etc. Step 2: The analysis unit analyzes the data stored by the storage unit and generates a response. The analysis unit learns the user's frequently used phrases and reactions to specific topics, and generates a response based on this. The generation AI is used to infer what kind of response the user would make if they were deceased. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and it learns the user's communication patterns and generates an appropriate response. Step 3: The provision unit provides the response generated by the analysis unit. The provision unit provides the AI-generated response in chats or calls on the messenger app. When the user's friends or family start a chat on the messenger app, the AI ​​responds as if the deceased were speaking. The provision unit also reproduces the voice and image of the deceased in voice and video calls, providing an experience as if one is talking to the deceased.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0120] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements including the above-mentioned storage unit, analysis unit, provision unit, protection unit, deletion unit, and reproduction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit is realized by the storage 50 of the smart device 14 or the storage 32 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart device 14. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The deletion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reproduction unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0129] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements including the above-mentioned storage unit, analysis unit, provision unit, protection unit, deletion unit, and reproduction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit is realized by the storage 50 of the smart glasses 214 or the storage 32 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The deletion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The reproduction unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements including the above-mentioned storage unit, analysis unit, provision unit, protection unit, deletion unit, and reproduction unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the storage unit is realized by the storage 50 of the headset type terminal 314 or the storage 32 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The deletion unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The reproduction unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] Each of the multiple elements including the above-mentioned storage unit, analysis unit, provision unit, protection unit, deletion unit, and reproduction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit is realized by the storage 50 of the robot 414 or the storage 32 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the robot 414. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The deletion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The reproduction unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0172] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0176] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0178] 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.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0181] 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.

[0182] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0183] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0187] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0189] (Appendix 1) A storage unit for storing text information, video information, and call information of a user; an analysis unit that analyzes the data stored by the storage unit and generates a response; a providing unit that provides a response generated by the analysis unit; A system characterized by: (Appendix 2) Equipped with a protection section that protects the privacy of stored data 2. The system of claim 1, (Appendix 3) Equipped with a deletion section that allows users to request the deletion of data 2. The system of claim 1, (Appendix 4) Equipped with a reproduction section that reproduces the user's voice and image using AI 2. The system of claim 1, (Appendix 5) The storage unit includes: Infer user emotions and adjust the type of data to be stored based on the inferred user emotions 2. The system of claim 1, (Appendix 6) The storage unit includes: When saving, the system analyzes the user's communication patterns in real time and saves certain data preferentially. 2. The system of claim 1, (Appendix 7) The storage unit includes: When storing data, adjust the frequency of data storage based on the user's lifestyle and activity history. 2. The system of claim 1, (Appendix 8) The storage unit includes: Infer user emotions and prioritize data to be stored based on the inferred user emotions 2. The system of claim 1, (Appendix 9) The storage unit includes: When saving, it takes into account the user's geographic location to prioritize saving relevant data. 2. The system of claim 1, (Appendix 10) The storage unit includes: At the time of storage, we analyze your social media activity and store certain data. 2. The system of claim 1, (Appendix 11) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is When analyzing, the accuracy of the analysis is improved by referring to the user's past communication history. 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, it learns how users respond to specific topics and improves the accuracy of its responses. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the geographical distribution of users is taken into account. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 17) The providing unit is Infer the user's emotions and adjust the way the reply is expressed based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The providing unit is When providing a response, the system references the user's past communication history to provide an appropriate response. 2. The system of claim 1, (Appendix 19) The providing unit is As it is served, it learns how users respond to specific topics and improves the accuracy of its responses. 2. The system of claim 1, (Appendix 20) The providing unit is Infer user sentiment and prioritize replies based on the inferred sentiment 2. The system of claim 1, (Appendix 21) The providing unit is When providing a response, it takes into account the user's geographic location to provide an appropriate response. 2. The system of claim 1, (Appendix 22) The providing unit is When provided, analyze your social media activity and provide you with a relevant response 2. The system of claim 1, (Appendix 23) The protective part is Estimating user emotions and adjusting the level of privacy protection based on the estimated user emotions 3. The system of claim 2, (Appendix 24) The protective part is When protecting privacy, select the appropriate protection method by referring to the user's past data usage history. 3. The system of claim 2, (Appendix 25) The protective part is Estimating user emotions and determining privacy protection priorities based on the estimated user emotions 3. The system of claim 2, (Appendix 26) The protective part is When protecting privacy, select an appropriate protection method taking into account the user's geographic location information 3. The system of claim 2, (Appendix 27) The deletion unit is Inferring user emotions and adjusting the timing of data deletion based on the estimated user emotions 4. The system of claim 3. (Appendix 28) The deletion unit is When deleting data, the appropriate deletion method is selected by referring to the user's past data usage history. 4. The system of claim 3. (Appendix 29) The deletion unit is Inferring user sentiment and prioritizing data deletion based on the inferred user sentiment 4. The system of claim 3. (Appendix 30) The deletion unit is When deleting data, choose the appropriate deletion method taking into account the user's geographic location information 4. The system of claim 3. (Appendix 31) The reproducing portion comprises: Estimate the user's emotions and adjust the way voices and images are reproduced based on the estimated user emotions. 5. The system of claim 4. (Appendix 32) The reproducing portion comprises: When reproducing, select the appropriate reproduction method by referring to the user's past communication history 5. The system of claim 4. (Appendix 33) The reproducing portion comprises: During replay, the system learns the user's reactions to specific topics and improves the accuracy of the replay. 5. The system of claim 4. (Appendix 34) The reproducing portion comprises: Estimate the user's emotions and determine the priority of reproduction based on the estimated user's emotions. 5. The system of claim 4. (Appendix 35) The reproducing portion comprises: When reproducing, the appropriate reproduction method is selected taking into account the user's geographic location information. 5. The system of claim 4. (Appendix 36) The reproducing portion comprises: During replay, analyze the user's social media activity and provide relevant replay methods 5. The system of claim 4. [Explanation of symbols]

[0190] 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 storage unit for storing text information, video information, and call information of a user; an analysis unit that analyzes the data stored by the storage unit and generates a response; a providing unit that provides a response generated by the analysis unit; A system characterized by:

2. Equipped with a protection section that protects the privacy of stored data 2. The system of claim 1 .

3. Equipped with a deletion section that allows users to request the deletion of data 2. The system of claim 1 .

4. The AI ​​will be equipped with a reproduction section that reproduces the user's voice and image.

2. The system of claim 1 .

5. The storage unit includes: Infer user emotions and adjust the type of data to be stored based on the inferred user emotions 2. The system of claim 1 .

6. The storage unit includes: When saving, the system analyzes the user's communication patterns in real time and saves certain data preferentially.

2. The system of claim 1 .

7. The storage unit includes: When storing data, adjust the frequency of data storage based on the user's lifestyle and activity history.

2. The system of claim 1 .

8. The storage unit includes: Infer user emotions and prioritize data to be stored based on the inferred user emotions 2. The system of claim 1 .

9. The storage unit includes: When saving, it takes into account the user's geographic location to prioritize saving relevant data.

2. The system of claim 1 .

10. The storage unit includes: At the time of storage, we analyze your social media activity and store certain data.

2. The system of claim 1 .

11. The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1 .

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