System

Abstract

An object of the system according to the embodiment is to prevent contents from remaining in the system even if a user talks about a secret or gossip.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a deletion unit. The reception unit receives an input of a user. The analysis unit analyzes the content received by the reception unit. The generation unit generates a reply based on the content analyzed by the analysis unit. The providing unit provides the reply generated by the generating unit to the user. The deletion unit deletes the content processed by the reception unit, the analysis unit, the generation unit, and the provision unit after a certain period of time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when a user shares a secret or complains, there is a risk that the content of that information will remain in the system, posing a problem in terms of privacy protection.

[0005] The system according to the embodiment aims to prevent users from revealing secrets or complaints in the system. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a deletion unit. The reception unit receives user input. The analysis unit analyzes the content received by the reception unit. The generation unit generates a reply based on the content analyzed by the analysis unit. The provision unit provides the reply generated by the generation unit to the user. The deletion unit deletes the content processed by the reception unit, analysis unit, generation unit, and provision unit after a certain period of time. [Effects of the Invention]

[0007] The system according to the embodiment can prevent users from revealing secrets or complaints in the system. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes a chat generation AI when a user wants to feel better by sharing secrets or complaints with someone. In this system, a user inputs a secret or complaint, and the AI ​​analyzes the content and generates and provides a response that helps the user feel better. The generated response is provided to the user, and the spoken content is automatically deleted after a certain period of time. For example, a user inputs a secret or complaint to the chat generation AI. For example, the user may input content such as "stress at work." This input is sent to the chat generation AI. The chat generation AI then analyzes the input content and generates a response that helps the user feel better. For example, the AI ​​generates a response such as "That must have been tough. But you're doing your best." This response is displayed to the user. Furthermore, the spoken content is processed so that it does not remain in the chat generation AI. Specifically, the content input by the user and the generated response are automatically deleted after a certain period of time. This protects the user's privacy. This system allows the user to feel safe talking about secrets or complaints and feel better. This allows the system to efficiently accept users' secrets and complaints, analyze them, generate and provide replies, and delete them after a certain period of time, thereby protecting users' privacy and helping them feel at ease.

[0029] A secret or complaint processing system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, a providing unit, and a deleting unit. The receiving unit receives user input. The user input includes, but is not limited to, secrets and complaints. The receiving unit receives, for example, secrets and complaints input in text format. The receiving unit can also receive voice input. For example, a user can input secrets and complaints by voice using a microphone. The receiving unit can also receive image input. For example, a user can express secrets and complaints using an image. The analyzing unit analyzes the content received by the receiving unit. The analyzing unit analyzes the user's input content using, for example, a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. For example, the generation AI analyzes the user's input content and extracts information for generating a reply based on the content. The generation unit generates a reply based on the content analyzed by the analysis unit. The generation unit generates a reply that makes the user feel relieved using, for example, the generation AI. For example, the generation AI generates a reply such as, "That must have been difficult. But you're doing your best." The providing unit provides the reply generated by the generation unit to the user. The providing unit, for example, displays the generated reply to the user in text format. The providing unit can also provide the reply in audio format. For example, the generated reply is provided to the user as audio using speech synthesis technology. The deleting unit deletes the content processed by the receiving unit, analysis unit, generation unit, and providing unit after a certain period of time. For example, the deleting unit automatically deletes content entered by the user and generated replies after a certain period of time. For example, the deleting unit deletes the content entered by the user and generated replies after 24 hours. As a result, the secret and complaint processing system according to the embodiment efficiently accepts and analyzes the user's secrets and complaints, generates and provides replies, and deletes them after a certain period of time, thereby protecting the user's privacy and helping them feel refreshed.

[0030] The reception unit can analyze the user's past input history and select an appropriate input method. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze trends in the content the user has entered in the past and customize the optimal input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0031] The reception unit can filter secrets and complaints based on the user's current situation and areas of interest when the secrets and complaints are input. For example, when the user is at work, the reception unit can prioritize receiving work-related secrets and complaints. Furthermore, when the user is having problems at home, the reception unit can prioritize receiving home-related secrets and complaints. Furthermore, the reception unit can filter related secrets and complaints based on the user's areas of interest and prompt the user to input appropriate secrets and complaints. By filtering based on the user's current situation and areas of interest, more appropriate secrets and complaints can be received. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's current situation data and area of ​​interest data to the generation AI and cause the generation AI to perform filtering.

[0032] The reception unit can select an appropriate input means according to the user's input method when inputting a secret or complaint. For example, if the user desires voice input, the reception unit can preferentially accept voice input. Furthermore, if the user desires text input, the reception unit can also preferentially accept text input. Furthermore, if the user wants to express a secret or complaint using an image, the reception unit can support image input. In this way, by selecting the optimal input means according to the user's input method, the user can input a secret or complaint in a manner that is easy for the user to use. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0033] When inputting secrets or complaints, the reception unit can prioritize inputting highly relevant content by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize inputting secrets or complaints related to that location. Furthermore, when the user is traveling, the reception unit can prioritize inputting secrets or complaints related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting secrets or complaints related to the home. In this way, highly relevant content can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant content.

[0034] The reception unit can analyze the user's social media activity and input related content when the user inputs a secret or a complaint. For example, the reception unit inputs related secrets or complaints based on the content posted by the user on social media. The reception unit can also input related secrets or complaints by referring to the activity of the user's friends on social media. The reception unit can also input related secrets or complaints based on the user's check-in information on social media. In this way, the user's social media activity can be analyzed to appropriately accept related content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related content.

[0035] The reception unit can customize the input method by reflecting the user's past feedback when inputting a secret or a complaint. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. The reception unit can also customize the optimal input method based on the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the optimal input method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the secret or complaint. For example, the analysis unit performs a detailed analysis on a secret or complaint that is highly important. The analysis unit can also perform a concise analysis on a secret or complaint that is less important. The analysis unit can also adjust the depth of the analysis according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the secret or complaint, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user input data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the secret or complaint. For example, the analysis unit can apply a work-related analysis algorithm to work-related secrets or complaints. The analysis unit can also apply a home-related analysis algorithm to home-related secrets or complaints. The analysis unit can also apply a relationship-related analysis algorithm to home-related secrets or complaints. In this way, by applying different analysis algorithms depending on the category of the secret or complaint, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user input data to the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the secret or complaint. For example, the analysis unit prioritizes analysis of the most recently submitted secret or complaint. The analysis unit can also postpone secrets or complaints that were submitted earlier. The analysis unit can also adjust the priority of analysis according to the time of submission. In this way, by determining the priority of analysis based on the time of submission of the secret or complaint, it is possible to perform the analysis in an appropriate order. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's submission time data into the generation AI and have the generation AI determine the priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the secrets and complaints. For example, the analysis unit prioritizes analysis of highly relevant secrets and complaints. The analysis unit can also postpone analysis of less relevant secrets and complaints. The analysis unit can also adjust the order of analysis according to the relevance. In this way, by adjusting the order of analysis based on the relevance of the secrets and complaints, highly relevant content can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user input data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can express the analysis results in simpler terms. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0042] When generating a reply, the generation unit can adjust the level of detail of the reply based on the importance of the secret or complaint. For example, the generation unit generates a detailed reply for a secret or complaint of high importance. The generation unit can also generate a concise reply for a secret or complaint of low importance. The generation unit can also adjust the level of detail of the reply according to the importance. In this way, an appropriate reply can be provided by adjusting the level of detail of the reply based on the importance of the secret or complaint. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to adjust the level of detail of the reply based on the importance.

[0043] When generating a reply, the generation unit can apply different reply generation algorithms depending on the category of the secret or complaint. For example, the generation unit applies a work-related reply generation algorithm to work-related secrets or complaints. The generation unit can also apply a home-related reply generation algorithm to home-related secrets or complaints. The generation unit can also apply a relationship-related reply generation algorithm to home-related secrets or complaints. In this way, by applying different reply generation algorithms depending on the category of the secret or complaint, a more appropriate reply can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to apply a reply generation algorithm depending on the category.

[0044] When generating a reply, the generation unit can improve the accuracy of the reply by referring to the user's past reply results. The generation unit can improve the accuracy of the current reply, for example, based on the user's past reply results. The generation unit can also adjust the reply generation algorithm by referring to the user's past feedback. The generation unit can also analyze the user's past reply results and suggest the optimal reply method. In this way, the accuracy of the reply can be improved by referring to the user's past reply results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's past reply result data into the generation AI and cause the generation AI to improve the accuracy of the reply.

[0045] When generating replies, the generation unit can determine the priority of replies based on the time when the secret or complaint was submitted. For example, the generation unit can prioritize generating replies to recently submitted secrets or complaints. The generation unit can also postpone secrets or complaints that were submitted earlier. The generation unit can also adjust the priority of replies according to the time of submission. In this way, by determining the priority of replies based on the time when the secret or complaint was submitted, replies can be provided in an appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the priority.

[0046] When generating replies, the generation unit can adjust the order of replies based on the relevance of the secrets or complaints. For example, the generation unit prioritizes generating replies to highly relevant secrets or complaints. The generation unit can also postpone less relevant secrets or complaints. The generation unit can also adjust the order of replies according to the relevance. In this way, by adjusting the order of replies based on the relevance of the secrets or complaints, it is possible to prioritize highly relevant content in replies. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to adjust the order of replies based on the relevance.

[0047] When generating a reply, the generation unit can adjust the use of technical terminology in the reply according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can express the reply in simpler terms. Furthermore, the generation unit can adjust the use of technical terminology in the reply according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the reply according to the user's level of expertise, a reply that is easy for the user to understand can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When providing a reply, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide display methods that the user has preferred in the past. The providing unit can also suggest the optimal display method from the user's past operation history. The providing unit can also analyze the user's past operation history and customize the optimal display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0049] When providing a reply, the providing unit can customize the display content according to the user's current task. For example, if the user is at work, the providing unit can prioritize displaying work-related replies. Furthermore, if the user is having problems at home, the providing unit can also prioritize displaying home-related replies. Furthermore, the providing unit can customize optimal display content according to the user's current task. In this way, by customizing the display content according to the user's current task, a more appropriate display can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generation AI and cause the generation AI to customize the display content.

[0050] When providing a reply, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0051] When providing a reply, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0052] When providing a reply, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the reply based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide a reply in a specific language when the user selects that language. This makes it possible to provide a more appropriate display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute multilingual display content.

[0053] When providing a reply, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0054] When deleting, the deletion unit can select the optimal deletion method by referring to the user's past deletion history. For example, the deletion unit can preferentially provide deletion methods that the user has previously preferred. The deletion unit can also suggest the optimal deletion method based on the user's past deletion history. The deletion unit can also analyze the user's past deletion history and customize the optimal deletion method. In this way, the optimal deletion method can be provided by referring to the user's past deletion history. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input the user's past deletion history data into the generation AI and have the generation AI select the optimal deletion method.

[0055] The deletion unit can customize the deletion method based on the user's current situation at the time of deletion. For example, if the user is at work, the deletion unit performs deletion quickly. The deletion unit can also adjust the timing of deletion if the user is having problems at home. The deletion unit can also customize the optimal deletion method according to the user's current situation. This allows for more appropriate deletion by customizing the deletion method based on the user's current situation. Some or all of the above-described processing in the deletion unit may be performed using, or without, AI, for example. For example, the deletion unit can input the user's current situation data into the generation AI and cause the generation AI to customize the deletion method.

[0056] The deletion unit can select the optimal deletion method by taking into account the user's geographical location information when deleting content. For example, when the user is in a specific location, the deletion unit can prioritize deleting content related to that location. Furthermore, when the user is traveling, the deletion unit can prioritize deleting content related to the travel destination. Furthermore, when the user is at home, the deletion unit can prioritize deleting content related to the home. This makes it possible to provide an optimal deletion method by taking into account the user's geographical location information. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal deletion method.

[0057] At the time of deletion, the deletion unit can analyze the user's social media activity and suggest a deletion method. The deletion unit, for example, deletes related content based on the content posted by the user on social media. The deletion unit can also delete related content by referring to the activity of the user's friends on social media. The deletion unit can also delete related content based on the user's check-in information on social media. In this way, by analyzing the user's social media activity, related content can be appropriately deleted. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a deletion method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can automatically classify the category of the content that the user wants to talk about based on the content input by the user. For example, if the user inputs a complaint about work, the reception unit classifies the content into the work category. Also, if the user inputs a secret about home life, the reception unit can classify the content into the home category. Furthermore, if the user inputs a complaint about human relationships, the reception unit can classify the content into the human relationships category. In this way, the reception unit can appropriately classify the content input by the user, allowing the analysis unit and generation unit to process the content efficiently.

[0060] The analysis unit can retrieve additional information from a related external database based on the user's input. For example, if the user inputs a complaint about work, the analysis unit can retrieve related industry news and statistical data. If the user inputs a secret about family, the analysis unit can retrieve advice about related family issues. Furthermore, if the user inputs a complaint about interpersonal relationships, the analysis unit can retrieve related psychological advice. This allows the analysis unit to provide additional information related to the user's input and generate a more comprehensive response.

[0061] The generation unit can adjust the tone of the reply based on the user's input. For example, if the user inputs something that makes them sad, the generation unit can generate a reply in a gentle tone. If the user inputs something that makes them angry, the generation unit can generate a reply in a calm tone. Furthermore, if the user inputs something that makes them happy, the generation unit can generate a reply in a bright tone. This allows the generation unit to generate a reply in a tone that matches the user's input, providing a more empathetic response.

[0062] The providing unit can adjust the display format of the reply based on the user's input. For example, if the user requests detailed information, the providing unit can display the detailed reply in text format. If the user requests concise information, the providing unit can display the reply in bullet point format. Furthermore, if the user prefers visual information, the providing unit can display the reply using graphs or diagrams. This allows the providing unit to provide the reply in the optimal display format according to the user's input.

[0063] The deletion unit can determine the priority of deletion based on the content input by the user. For example, if the user inputs content that places great importance on privacy, the deletion unit will delete that content as a top priority. Also, if the user inputs general complaints, the deletion unit can postpone deleting that content. Furthermore, if the user wants to delete content input based on temporary emotions, the deletion unit can quickly delete that content. In this way, the deletion unit can delete content in priority according to the content input by the user, thereby protecting privacy.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives user input. User input includes, but is not limited to, secrets and complaints. The reception unit can receive text, voice input, and image input. For example, the user can use a microphone to input secrets and complaints by voice. The user can also express secrets and complaints using images. Step 2: The analysis unit analyzes the content received by the reception unit. The analysis unit uses a generation AI to analyze the user's input and extract information to generate a reply based on that content. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The generator generates a response based on the content analyzed by the analyzer. Using AI, the generator generates a response that makes the user feel better. For example, it generates a response such as, "That must have been difficult, but you're doing your best." Step 4: The providing unit provides the response generated by the generating unit to the user. The providing unit displays the generated response to the user in text format. The providing unit can also provide the response to the user as voice using speech synthesis technology. Step 5: The deletion unit deletes the content processed by the reception unit, analysis unit, generation unit, and provision unit after a certain period of time. The deletion unit automatically deletes the content entered by the user and the generated reply after a certain period of time has passed. For example, the content entered by the user and the generated reply are deleted after 24 hours.

[0066] (Example 2) A system according to an embodiment of the present invention utilizes a chat generation AI when a user wants to feel better by sharing secrets or complaints with someone. In this system, a user inputs a secret or complaint, and the AI ​​analyzes the content and generates and provides a response that helps the user feel better. The generated response is provided to the user, and the spoken content is automatically deleted after a certain period of time. For example, a user inputs a secret or complaint to the chat generation AI. For example, the user may input content such as "stress at work." This input is sent to the chat generation AI. The chat generation AI then analyzes the input content and generates a response that helps the user feel better. For example, the AI ​​generates a response such as "That must have been tough. But you're doing your best." This response is displayed to the user. Furthermore, the spoken content is processed so that it does not remain in the chat generation AI. Specifically, the content input by the user and the generated response are automatically deleted after a certain period of time. This protects the user's privacy. This system allows the user to feel safe talking about secrets or complaints and feel better. This allows the system to efficiently accept users' secrets and complaints, analyze them, generate and provide replies, and delete them after a certain period of time, thereby protecting users' privacy and helping them feel at ease.

[0067] A secret or complaint processing system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, a providing unit, and a deleting unit. The receiving unit receives user input. The user input includes, but is not limited to, secrets and complaints. The receiving unit receives, for example, secrets and complaints input in text format. The receiving unit can also receive voice input. For example, a user can input secrets and complaints by voice using a microphone. The receiving unit can also receive image input. For example, a user can express secrets and complaints using an image. The analyzing unit analyzes the content received by the receiving unit. The analyzing unit analyzes the user's input content using, for example, a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. For example, the generation AI analyzes the user's input content and extracts information for generating a reply based on the content. The generation unit generates a reply based on the content analyzed by the analysis unit. The generation unit generates a reply that makes the user feel relieved using, for example, the generation AI. For example, the generation AI generates a reply such as, "That must have been difficult. But you're doing your best." The providing unit provides the reply generated by the generation unit to the user. The providing unit, for example, displays the generated reply to the user in text format. The providing unit can also provide the reply in audio format. For example, the generated reply is provided to the user as audio using speech synthesis technology. The deleting unit deletes the content processed by the receiving unit, analysis unit, generation unit, and providing unit after a certain period of time. For example, the deleting unit automatically deletes content entered by the user and generated replies after a certain period of time. For example, the deleting unit deletes the content entered by the user and generated replies after 24 hours. As a result, the secret and complaint processing system according to the embodiment efficiently accepts and analyzes the user's secrets and complaints, generates and provides replies, and deletes them after a certain period of time, thereby protecting the user's privacy and helping them feel refreshed.

[0068] The reception unit can estimate the user's emotions and adjust the timing of inputting secrets or complaints based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can immediately accept the input, providing an environment where the user can speak immediately. Furthermore, when the user is relaxed, the reception unit can slightly delay the timing of the input, allowing the user to speak more slowly. Furthermore, when the user is in a hurry, the reception unit can speed up the timing of the input, allowing the user to speak more quickly. By adjusting the input timing according to the user's emotions, it is possible to accept secrets or complaints at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0069] The reception unit can analyze the user's past input history and select an appropriate input method. For example, the reception unit can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze trends in the content the user has entered in the past and customize the optimal input method. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0070] The reception unit can filter secrets and complaints based on the user's current situation and areas of interest when the secrets and complaints are input. For example, when the user is at work, the reception unit can prioritize receiving work-related secrets and complaints. Furthermore, when the user is having problems at home, the reception unit can prioritize receiving home-related secrets and complaints. Furthermore, the reception unit can filter related secrets and complaints based on the user's areas of interest and prompt the user to input appropriate secrets and complaints. By filtering based on the user's current situation and areas of interest, more appropriate secrets and complaints can be received. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's current situation data and area of ​​interest data to the generation AI and cause the generation AI to perform filtering.

[0071] The reception unit can select an appropriate input means according to the user's input method when inputting a secret or complaint. For example, if the user desires voice input, the reception unit can preferentially accept voice input. Furthermore, if the user desires text input, the reception unit can also preferentially accept text input. Furthermore, if the user wants to express a secret or complaint using an image, the reception unit can support image input. In this way, by selecting the optimal input means according to the user's input method, the user can input a secret or complaint in a manner that is easy for the user to use. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0072] The reception unit can estimate the user's emotions and determine the priority of secrets and complaints to be input based on the estimated user emotions. For example, if the user is feeling very stressed, the reception unit can give top priority to receiving those secrets and complaints. Furthermore, if the user wants to share a light complaint, the reception unit can postpone that content. Furthermore, if the user is emotionally unstable, the reception unit can prioritize receiving those secrets and complaints. Thus, by determining the priority of secrets and complaints according to the user's emotions, important content can be received preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0073] When inputting secrets or complaints, the reception unit can prioritize inputting highly relevant content by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize inputting secrets or complaints related to that location. Furthermore, when the user is traveling, the reception unit can prioritize inputting secrets or complaints related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting secrets or complaints related to the home. In this way, highly relevant content can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize inputting highly relevant content.

[0074] The reception unit can analyze the user's social media activity and input related content when the user inputs a secret or a complaint. For example, the reception unit inputs related secrets or complaints based on the content posted by the user on social media. The reception unit can also input related secrets or complaints by referring to the activity of the user's friends on social media. The reception unit can also input related secrets or complaints based on the user's check-in information on social media. In this way, the user's social media activity can be analyzed to appropriately accept related content. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to input related content.

[0075] The reception unit can customize the input method by reflecting the user's past feedback when inputting a secret or a complaint. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. The reception unit can also customize the optimal input method based on the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the optimal input method.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can express the analysis results in gentle language. The analysis unit can also provide detailed analysis results if the user is relaxed. The analysis unit can also provide concise analysis results if the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the secret or complaint. For example, the analysis unit performs a detailed analysis on a secret or complaint that is highly important. The analysis unit can also perform a concise analysis on a secret or complaint that is less important. The analysis unit can also adjust the depth of the analysis according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the secret or complaint, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user input data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the secret or complaint. For example, the analysis unit can apply a work-related analysis algorithm to work-related secrets or complaints. The analysis unit can also apply a home-related analysis algorithm to home-related secrets or complaints. The analysis unit can also apply a relationship-related analysis algorithm to home-related secrets or complaints. In this way, by applying different analysis algorithms depending on the category of the secret or complaint, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user input data to the generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a short analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise and to-the-point analysis result when the user is stressed. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the secret or complaint. For example, the analysis unit prioritizes analysis of the most recently submitted secret or complaint. The analysis unit can also postpone secrets or complaints that were submitted earlier. The analysis unit can also adjust the priority of analysis according to the time of submission. In this way, by determining the priority of analysis based on the time of submission of the secret or complaint, it is possible to perform the analysis in an appropriate order. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's submission time data into the generation AI and have the generation AI determine the priority.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the secrets and complaints. For example, the analysis unit prioritizes analysis of highly relevant secrets and complaints. The analysis unit can also postpone analysis of less relevant secrets and complaints. The analysis unit can also adjust the order of analysis according to the relevance. In this way, by adjusting the order of analysis based on the relevance of the secrets and complaints, highly relevant content can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user input data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0083] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can express the analysis results in simpler terms. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0084] The generation unit can estimate the user's emotions and adjust the way a reply is expressed based on the estimated user's emotions. For example, if the user is stressed, the generation unit can generate a reply using gentle language. The generation unit can also generate a detailed reply if the user is relaxed. The generation unit can also generate a concise reply if the user is in a hurry. This allows for a more appropriate reply to be provided by adjusting the way a reply is expressed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0085] When generating a reply, the generation unit can adjust the level of detail of the reply based on the importance of the secret or complaint. For example, the generation unit generates a detailed reply for a secret or complaint of high importance. The generation unit can also generate a concise reply for a secret or complaint of low importance. The generation unit can also adjust the level of detail of the reply according to the importance. In this way, an appropriate reply can be provided by adjusting the level of detail of the reply based on the importance of the secret or complaint. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to adjust the level of detail of the reply based on the importance.

[0086] When generating a reply, the generation unit can apply different reply generation algorithms depending on the category of the secret or complaint. For example, the generation unit applies a work-related reply generation algorithm to work-related secrets or complaints. The generation unit can also apply a home-related reply generation algorithm to home-related secrets or complaints. The generation unit can also apply a relationship-related reply generation algorithm to home-related secrets or complaints. In this way, by applying different reply generation algorithms depending on the category of the secret or complaint, a more appropriate reply can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to apply a reply generation algorithm depending on the category.

[0087] When generating a reply, the generation unit can improve the accuracy of the reply by referring to the user's past reply results. The generation unit can improve the accuracy of the current reply, for example, based on the user's past reply results. The generation unit can also adjust the reply generation algorithm by referring to the user's past feedback. The generation unit can also analyze the user's past reply results and suggest the optimal reply method. In this way, the accuracy of the reply can be improved by referring to the user's past reply results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's past reply result data into the generation AI and cause the generation AI to improve the accuracy of the reply.

[0088] The generation unit can estimate the user's emotions and adjust the length of the reply based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point reply. The generation unit can also generate a longer reply with detailed explanations if the user is relaxed. The generation unit can also generate a concise, to-the-point reply if the user is stressed. This allows for adjusting the length of the reply according to the user's emotions, thereby providing a more appropriate reply. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0089] When generating replies, the generation unit can determine the priority of replies based on the time when the secret or complaint was submitted. For example, the generation unit can prioritize generating replies to recently submitted secrets or complaints. The generation unit can also postpone secrets or complaints that were submitted earlier. The generation unit can also adjust the priority of replies according to the time of submission. In this way, by determining the priority of replies based on the time when the secret or complaint was submitted, replies can be provided in an appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the priority.

[0090] When generating replies, the generation unit can adjust the order of replies based on the relevance of the secrets or complaints. For example, the generation unit prioritizes generating replies to highly relevant secrets or complaints. The generation unit can also postpone less relevant secrets or complaints. The generation unit can also adjust the order of replies according to the relevance. In this way, by adjusting the order of replies based on the relevance of the secrets or complaints, it is possible to prioritize highly relevant content in replies. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data to the generation AI and cause the generation AI to adjust the order of replies based on the relevance.

[0091] When generating a reply, the generation unit can adjust the use of technical terminology in the reply according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can express the reply in simpler terms. Furthermore, the generation unit can adjust the use of technical terminology in the reply according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the reply according to the user's level of expertise, a reply that is easy for the user to understand can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0092] The providing unit can estimate the user's emotions and adjust the display method of the reply based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can display the reply in a gentle color. If the user is relaxed, the providing unit can also display a detailed reply. If the user is in a hurry, the providing unit can also display a concise reply. This allows for a more appropriate display by adjusting the display method of the reply according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] When providing a reply, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially provide display methods that the user has preferred in the past. The providing unit can also suggest the optimal display method from the user's past operation history. The providing unit can also analyze the user's past operation history and customize the optimal display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0094] When providing a reply, the providing unit can customize the display content according to the user's current task. For example, if the user is at work, the providing unit can prioritize displaying work-related replies. Furthermore, if the user is having problems at home, the providing unit can also prioritize displaying home-related replies. Furthermore, the providing unit can customize optimal display content according to the user's current task. In this way, by customizing the display content according to the user's current task, a more appropriate display can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generation AI and cause the generation AI to customize the display content.

[0095] When providing a reply, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0096] The providing unit can estimate the user's emotions and adjust the reply operation procedure based on the estimated user's emotions. For example, the providing unit simplifies the operation procedure when the user is stressed. The providing unit can also provide detailed operation procedures when the user is relaxed. The providing unit can also provide procedures that allow the user to operate quickly when the user is in a hurry. This allows the reply operation procedure to be adjusted according to the user's emotions, thereby providing more appropriate operation procedures. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0097] When providing a reply, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0098] When providing a reply, the providing unit can make the display content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the reply based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide a reply in a specific language when the user selects that language. This makes it possible to provide a more appropriate display by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute multilingual display content.

[0099] When providing a reply, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0100] The deletion unit can estimate the user's emotions and adjust the timing of deletion based on the estimated user emotions. For example, if the user is feeling stressed, the deletion unit can immediately delete the content. Furthermore, if the user is relaxed, the deletion unit can slightly delay the timing of deletion. Furthermore, if the user is in a hurry, the deletion unit can quickly delete the content. By adjusting the timing of deletion according to the user's emotions, deletion can be performed at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described 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 facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0101] When deleting, the deletion unit can select the optimal deletion method by referring to the user's past deletion history. For example, the deletion unit can preferentially provide deletion methods that the user has previously preferred. The deletion unit can also suggest the optimal deletion method based on the user's past deletion history. The deletion unit can also analyze the user's past deletion history and customize the optimal deletion method. In this way, the optimal deletion method can be provided by referring to the user's past deletion history. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input the user's past deletion history data into the generation AI and have the generation AI select the optimal deletion method.

[0102] The deletion unit can customize the deletion method based on the user's current situation at the time of deletion. For example, if the user is at work, the deletion unit performs deletion quickly. The deletion unit can also adjust the timing of deletion if the user is having problems at home. The deletion unit can also customize the optimal deletion method according to the user's current situation. This allows for more appropriate deletion by customizing the deletion method based on the user's current situation. Some or all of the above-described processing in the deletion unit may be performed using, or without, AI, for example. For example, the deletion unit can input the user's current situation data into the generation AI and cause the generation AI to customize the deletion method.

[0103] The deletion unit can estimate the user's emotions and determine a deletion priority based on the estimated user emotions. For example, if the user is feeling very stressed, the deletion unit can delete that content as a top priority. The deletion unit can also postpone content that the user is complaining about. The deletion unit can also prioritize deletion of content that the user is emotionally unstable. By determining the deletion priority according to the user's emotions, important content can be deleted preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the deletion unit can be performed using, for example, an AI, or without an AI. For example, the deletion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0104] The deletion unit can select the optimal deletion method by taking into account the user's geographical location information when deleting content. For example, when the user is in a specific location, the deletion unit can prioritize deleting content related to that location. Furthermore, when the user is traveling, the deletion unit can prioritize deleting content related to the travel destination. Furthermore, when the user is at home, the deletion unit can prioritize deleting content related to the home. This makes it possible to provide an optimal deletion method by taking into account the user's geographical location information. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal deletion method.

[0105] At the time of deletion, the deletion unit can analyze the user's social media activity and suggest a deletion method. The deletion unit, for example, deletes related content based on the content posted by the user on social media. The deletion unit can also delete related content by referring to the activity of the user's friends on social media. The deletion unit can also delete related content based on the user's check-in information on social media. In this way, by analyzing the user's social media activity, related content can be appropriately deleted. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a deletion method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and deletion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input content using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analyzed content. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated reply to the user. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes the user's input content and the generated reply after a certain period of time. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and deletion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input content using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated reply to the user by voice. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes the user's input content and the generated reply after a certain period of time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and deletion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a user's voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input content using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the generated reply to the user by voice. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes the user's input content and the generated reply after a certain period of time. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and deletion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a user's voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input content using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a reply based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated reply to the user by voice. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and deletes the user's input content and the generated reply after a certain period of time.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can automatically classify the category of the content that the user wants to talk about based on the content input by the user. For example, if the user inputs a complaint about work, the reception unit classifies the content into the work category. Also, if the user inputs a secret about home life, the reception unit can classify the content into the home category. Furthermore, if the user inputs a complaint about human relationships, the reception unit can classify the content into the human relationships category. In this way, the reception unit can appropriately classify the content input by the user, allowing the analysis unit and generation unit to process the content efficiently.

[0108] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide feedback such as "Please relax and speak." If the user is relaxed, the reception unit can also provide feedback such as "Keep it up." Furthermore, if the user is in a hurry, the reception unit can provide feedback such as "It's okay to keep it short." In this way, by providing feedback according to the user's emotions, it is possible to enable the user to input more comfortably.

[0109] The analysis unit can retrieve additional information from a related external database based on the user's input. For example, if the user inputs a complaint about work, the analysis unit can retrieve related industry news and statistical data. If the user inputs a secret about family, the analysis unit can retrieve advice about related family issues. Furthermore, if the user inputs a complaint about interpersonal relationships, the analysis unit can retrieve related psychological advice. This allows the analysis unit to provide additional information related to the user's input and generate a more comprehensive response.

[0110] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is feeling very stressed, the analysis unit can perform a detailed analysis and provide specific advice. If the user is relaxed, the analysis unit can perform a concise analysis and provide advice that focuses on the main points. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide advice that can be understood in a short amount of time. In this way, by adjusting the depth of analysis according to the user's emotions, more appropriate analysis results can be provided.

[0111] The generation unit can adjust the tone of the reply based on the user's input. For example, if the user inputs something that makes them sad, the generation unit can generate a reply in a gentle tone. If the user inputs something that makes them angry, the generation unit can generate a reply in a calm tone. Furthermore, if the user inputs something that makes them happy, the generation unit can generate a reply in a bright tone. This allows the generation unit to generate a reply in a tone that matches the user's input, providing a more empathetic response.

[0112] The generation unit can estimate the user's emotions and customize the content of the reply based on the estimated user's emotions. For example, if the user is feeling very stressed, the generation unit can provide specific advice for reducing stress. Also, if the user is relaxed, the generation unit can provide advice for maintaining relaxation. Furthermore, if the user is in a hurry, the generation unit can provide advice that can be implemented in a short time. This allows for more appropriate support by providing a customized reply according to the user's emotions.

[0113] The providing unit can adjust the display format of the reply based on the user's input. For example, if the user requests detailed information, the providing unit can display the detailed reply in text format. If the user requests concise information, the providing unit can display the reply in bullet point format. Furthermore, if the user prefers visual information, the providing unit can display the reply using graphs or diagrams. This allows the providing unit to provide the reply in the optimal display format according to the user's input.

[0114] The providing unit can estimate the user's emotions and adjust the reply display speed based on the estimated user's emotions. For example, if the user is feeling very stressed, the providing unit can display the reply slowly so that the user can read it calmly. Also, if the user is relaxed, the providing unit can display the reply at a normal speed. Furthermore, if the user is in a hurry, the providing unit can display the reply quickly so that the user can check it in a short time. In this way, by adjusting the display speed according to the user's emotions, a more appropriate display can be provided.

[0115] The deletion unit can determine the priority of deletion based on the content input by the user. For example, if the user inputs content that places great importance on privacy, the deletion unit will delete that content as a top priority. Also, if the user inputs general complaints, the deletion unit can postpone deleting that content. Furthermore, if the user wants to delete content input based on temporary emotions, the deletion unit can quickly delete that content. In this way, the deletion unit can delete content in priority according to the content input by the user, thereby protecting privacy.

[0116] The deletion unit can estimate the user's emotions and customize the deletion method based on the estimated user's emotions. For example, if the user is feeling very stressed, the deletion unit can delete the content immediately, allowing the user to feel at ease. If the user is relaxed, the deletion unit can also delay the timing of deletion a little. Furthermore, if the user is in a hurry, the deletion can be performed quickly. In this way, by customizing the deletion method according to the user's emotions, more appropriate deletion can be performed.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives user input. User input includes, but is not limited to, secrets and complaints. The reception unit can receive text, voice input, and image input. For example, the user can use a microphone to input secrets and complaints by voice. The user can also express secrets and complaints using images. Step 2: The analysis unit analyzes the content received by the reception unit. The analysis unit uses a generation AI to analyze the user's input and extract information to generate a reply based on that content. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The generator generates a response based on the content analyzed by the analyzer. Using AI, the generator generates a response that makes the user feel better. For example, it generates a response such as, "That must have been difficult, but you're doing your best." Step 4: The providing unit provides the response generated by the generating unit to the user. The providing unit displays the generated response to the user in text format. The providing unit can also provide the response to the user as voice using speech synthesis technology. Step 5: The deletion unit deletes the content processed by the reception unit, analysis unit, generation unit, and provision unit after a certain period of time. The deletion unit automatically deletes the content entered by the user and the generated reply after a certain period of time has passed. For example, the content entered by the user and the generated reply are deleted after 24 hours.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] 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.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the content received by the reception unit; a generation unit that generates a reply based on the content analyzed by the analysis unit; a providing unit that provides the reply generated by the generating unit to a user; a deletion unit that deletes the content processed by the reception unit, analysis unit, generation unit, and provision unit after a certain period of time. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of inputting secrets or complaints based on the estimated user emotions 2. The system of claim 1.

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

4. The reception unit When entering secrets or complaints, filtering is performed based on the user's current situation and areas of interest.

2. The system of claim 1.

5. The reception unit When entering secrets or complaints, select the appropriate input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the secrets and complaints to be entered based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering secrets or complaints, the app takes into account the user's geographic location and prioritizes the most relevant information.

2. The system of claim 1.

8. The reception unit When you enter a secret or complaint, it analyzes your social media activity and enters relevant content.

2. The system of claim 1.

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