System

Abstract

An object of a system according to an embodiment is to generate a dialogue by utilizing a user's memory, reduce a sense of loneliness, and promote activation of the brain.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a photograph or a memory from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a dialogue on the basis of the information analyzed by the analysis unit. The providing unit provides the user with the dialogue generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not providing sufficient and effective support to lonely elderly people and people who find it difficult to enjoy their memories on their own.

[0005] The system according to the embodiment aims to generate dialogue by utilizing the user's memories, thereby reducing feelings of loneliness and promoting brain activation. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos or memories from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a dialogue based on the information analyzed by the analysis unit. The provision unit provides the dialogue generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment utilizes the user's memories to generate dialogue, thereby reducing feelings of loneliness and promoting brain activation. [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 uses a generation AI to stimulate the brains of lonely elderly people, people who take photos of food at mealtimes, and people who take memorable photos on walks and trips, thereby maintaining their vitality and contributing to society. In this system, a user interacts with a generation AI, which generates and provides dialogues based on the user's photos and memories. This allows the user to reminisce about happy times and promotes brain activation. For example, for lonely elderly people, the generation AI generates dialogues based on old memorable photos. For example, when a user shows an old family photo, the generation AI begins a conversation about family memories based on the photo. Next, for people who take photos of food at mealtimes, the generation AI generates dialogues based on the food photos. For example, when a user shows a photo of a dish they took, the generation AI provides topics related to the dish. Furthermore, for people who take memorable photos on walks and trips, the generation AI generates dialogues based on the memorable photos. For example, when a user shows a photo they took at a travel destination, the generation AI provides topics related to the destination. This allows the user to reminisce about happy times and promotes brain activation. Furthermore, this system maintains their vitality and allows them to contribute to society. This allows the system to generate and provide dialogue based on the user's photos and memories, stimulating the user's brain and maintaining their vitality to contribute to society.

[0029] A dialogue generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos or memories from a user. The photos or memories from the user include, but are not limited to, digital photos, paper photos, and spoken memories. The reception unit receives, for example, digital photos through an online form. The reception unit can also scan paper photos and convert them into digital data. The reception unit can also receive spoken memories from the user as voice input. The analysis unit analyzes the information received by the reception unit. The analysis can be performed using, for example, image analysis, text analysis, or emotion analysis, but is not limited to, these examples. For example, the analysis unit can analyze the content of photos using image analysis technology. The analysis unit can also analyze spoken memories using text analysis technology. The analysis unit can also analyze the user's emotions using emotion analysis technology. The generation unit generates a dialogue based on the information analyzed by the analysis unit. The dialogue generation can be performed based on, for example, natural language generation technology or a dialogue scenario creation method, but is not limited to these examples. For example, the generation unit generates a dialogue based on the user's photos and memories using natural language generation technology. The generation unit can also generate a dialogue using a dialogue scenario creation method. Furthermore, the generation unit can also generate a dialogue using a generation AI. The provision unit provides the dialogue generated by the generation unit to the user. The provision can be performed, for example, in audio, text, or an interactive dialogue format, but is not limited to these examples. For example, the provision unit provides the dialogue generated by speech synthesis technology in audio format. The provision unit can also display the dialogue generated in text format on the user's device. Furthermore, the provision unit can provide the dialogue generated in an interactive dialogue format. Thus, the dialogue generation system according to the embodiment generates and provides a dialogue based on the user's photos and memories, thereby stimulating the user's brain and maintaining their vitality to contribute to society.

[0030] The reception unit can analyze the user's past photo and memory submission history and select an appropriate reception method. For example, the reception unit can analyze the types of photos the user frequently submitted in the past and prioritize accepting similar photos. The reception unit can also analyze the content of memories the user previously submitted and prioritize accepting related memories. Furthermore, the reception unit can prioritize accepting photos and memories submitted during a specific time period based on the user's submission history. This allows photos and memories to be accepted in the optimal manner based on the user's past submission history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0031] The reception unit may filter photos and memories based on the user's current living situation or areas of interest when receiving the photos and memories. For example, the reception unit may preferentially receive photos and memories related to areas of interest in the user's current living situation. The reception unit may also filter and receive related photos and memories based on the user's areas of interest. Furthermore, the reception unit may also receive photos and memories at an appropriate time depending on the user's living situation. This allows appropriate photos and memories to be received depending on the user's current living situation and areas of interest. 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 may input the user's living situation data into a generation AI and have the generation AI perform filtering.

[0032] When accepting photos or memories, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user describes the photos or memories using voice, the acceptance unit can prioritize accepting voice input. Furthermore, when the user describes the photos or memories using text, the acceptance unit can also prioritize accepting text input. Furthermore, when the user submits the photos or memories using images, the acceptance unit can also prioritize accepting image input. This makes it possible to accept photos and memories using the optimal means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into a generation AI and have the generation AI select the optimal acceptance means.

[0033] When receiving photos and memories, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, the reception unit can prioritize receiving photos taken by the user in locations close to the user's current location. The reception unit can also prioritize receiving photos taken by the user at travel destinations. Furthermore, the reception unit can prioritize receiving related memories based on the user's geographical location information. This makes it possible to prioritize receiving highly relevant photos and memories based on the user's geographical location information. Some or all of the above-described processing by 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 a generation AI and cause the generation AI to select highly relevant information.

[0034] The reception unit can analyze the user's social media activity and receive related information when receiving photos and memories. For example, the reception unit can prioritize receiving photos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related memories. Furthermore, the reception unit can also receive related photos and memories by referring to the activity of the user's friends on social media. This makes it possible to receive related photos and memories based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related information.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving photos and memories. For example, the reception unit may preferentially accept types of photos that the user has previously preferred. The reception unit can also adjust the reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception method by referring to the content of photos and memories that the user has previously submitted. This makes it possible to receive photos and memories in an optimal manner based on 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 feedback data into the generation AI and have the generation AI customize the reception method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photos and memories. For example, the analysis unit performs a detailed analysis on photos of important memories. The analysis unit can also perform a simplified analysis on general photos. Furthermore, the analysis unit can also perform a detailed analysis on photos that the user particularly likes. This allows analysis to be performed with an appropriate level of detail depending on the importance of the photos and memories. 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 importance data of the user's photos and memories to the generation AI and cause the generation AI to adjust the level of detail.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the photo or memory. For example, the analysis unit can apply an algorithm that analyzes family relationships to family photos. The analysis unit can also apply an algorithm that analyzes travel destination information to travel photos. The analysis unit can also apply an algorithm that analyzes food types and recipes to food photos. This makes it possible to apply an appropriate analysis algorithm depending on the category of the photo or memory. 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 category data of photos and memories into the generation AI and cause the generation AI to apply the analysis algorithm.

[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 can improve the accuracy of the analysis by referring to, for example, analysis results that the user preferred in the past. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. 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 cause the generation AI to 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 photos and memories. For example, the analysis unit prioritizes analysis of recently submitted photos and memories. The analysis unit can also prioritize analysis of photos and memories related to a specific event. Furthermore, the analysis unit can prioritize analysis of photos and memories that the user particularly likes. This allows analysis to be prioritized based on the time of submission of photos and memories. 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 data on the time of submission of photos and memories 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 photos and memories. For example, the analysis unit prioritizes analysis of highly relevant photos and memories. The analysis unit can also prioritize analysis of photos and memories that the user particularly likes. Furthermore, the analysis unit can also prioritize analysis of highly relevant photos and memories by referring to the user's past submission history. This allows analysis to be performed in an appropriate order based on the relevance of photos and memories. 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 relevance data of photos and memories into the generation AI and cause the generation AI to adjust the order of analysis.

[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 can provide the analysis result using technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result in simple language. Furthermore, the analysis unit can select appropriate terminology according to the user's level of expertise and provide the analysis result. This allows the analysis result to be provided using appropriate terminology 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 cause the generation AI to adjust the use of technical terminology.

[0042] When generating a dialogue, the generation unit can adjust the level of detail of the dialogue based on the importance of the photo or memory. For example, the generation unit generates detailed dialogue for photos of important memories. The generation unit can also generate simple dialogue for general photos. Furthermore, the generation unit can also generate detailed dialogue for photos that the user particularly likes. This makes it possible to generate dialogue with an appropriate level of detail depending on the importance of the photo or memory. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input importance data of photos and memories into the generation AI and cause the generation AI to adjust the level of detail.

[0043] When generating a dialogue, the generation unit can apply different dialogue generation algorithms depending on the category of the photo or memory. For example, the generation unit applies an algorithm that generates dialogue themed around family relationships to family photos. The generation unit can also apply an algorithm that generates dialogue themed around travel destination information to travel photos. The generation unit can also apply an algorithm that generates dialogue themed around food types and recipes to food photos. This makes it possible to apply an appropriate dialogue generation algorithm depending on the category of the photo or memory. 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 category data of photos and memories into the generation AI and cause the generation AI to apply the dialogue generation algorithm.

[0044] When generating a dialogue, the generation unit can improve the accuracy of the dialogue by referring to the user's past dialogue results. The generation unit can improve the accuracy of the dialogue by referring to, for example, dialogue content that the user has preferred in the past. The generation unit can also adjust the dialogue generation algorithm based on the user's past dialogue results. Furthermore, the generation unit can also improve the accuracy of the dialogue by referring to the user's past feedback. In this way, the accuracy of the dialogue can be improved by referring to the user's past dialogue results. 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 past dialogue result data into the generation AI and cause the generation AI to improve the accuracy of the dialogue.

[0045] When generating a dialogue, the generation unit can determine the priority of the dialogue based on the time when the photos and memories were submitted. The generation unit generates a dialogue based on, for example, recently submitted photos and memories. The generation unit can also generate a dialogue based on photos and memories related to a specific event. Furthermore, the generation unit can generate a dialogue based on photos and memories that the user particularly likes. This allows dialogues to be generated preferentially based on the time when the photos and memories were submitted. Some or all of the above-described 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 data on the time when the photos and memories were submitted into the generation AI and have the generation AI determine the priority.

[0046] When generating a dialogue, the generation unit can adjust the order of the dialogues based on the relevance of the photos and memories. The generation unit, for example, generates dialogues based on highly relevant photos and memories. The generation unit can also generate dialogues based on photos and memories that the user particularly likes. Furthermore, the generation unit can generate dialogues based on highly relevant photos and memories by referring to the user's past submission history. This makes it possible to generate dialogues in an appropriate order based on the relevance of the photos and memories. Some or all of the above-described 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 relevance data of photos and memories into the generation AI and cause the generation AI to adjust the order of the dialogues.

[0047] When generating a dialogue, the generation unit can adjust the use of technical terminology in the dialogue according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates the dialogue using technical terminology. Also, if the user does not have technical expertise, the generation unit can generate the dialogue using simple language. Furthermore, the generation unit can generate the dialogue by selecting appropriate terminology according to the user's level of expertise. This makes it possible to generate a dialogue using appropriate terminology according to the user's level of expertise. Some or all of the above-described 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 dialogue, the providing unit can select an optimal delivery method by referring to the user's past dialogue history. The providing unit provides the dialogue, for example, by referring to delivery methods that the user has preferred in the past. The providing unit can also adjust the delivery method based on the user's past dialogue history. Furthermore, the providing unit can also select an optimal delivery method by referring to the user's past feedback. This makes it possible to provide a dialogue in an optimal method based on the user's past dialogue history. 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 past dialogue history data into the generation AI and cause the generation AI to select an optimal delivery method.

[0049] The providing unit can customize the content to be provided based on the user's current living situation when providing the dialogue. For example, the providing unit provides dialogue related to areas in which the user is interested in the user's current living situation. The providing unit can also provide the dialogue at an appropriate time depending on the user's living situation. Furthermore, the providing unit can customize the content of the dialogue taking into account the user's current living situation. This makes it possible to provide dialogue with appropriate content depending on the user's current living situation. Some or all of the above-described 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 living situation data into a generation AI and cause the generation AI to customize the content to be provided.

[0050] The providing unit can improve the method of providing dialogue by reflecting user feedback. For example, the providing unit improves the method of providing dialogue based on feedback from the user regarding dialogues previously provided by the user. The providing unit can also adjust the content of the dialogue by referring to the user's feedback. Furthermore, the providing unit can customize the method of providing dialogue based on the user's past feedback. This makes it possible to provide dialogue in an optimal manner based on the user's feedback. Some or all of the above-described 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 user feedback data into the generating AI and cause the generating AI to improve the method of providing dialogue.

[0051] When providing a dialogue, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, the providing unit can provide a dialogue based on a photo taken by the user in a location close to the user's current location. The providing unit can also provide a dialogue based on a photo taken by the user at a travel destination. Furthermore, the providing unit can also provide a related dialogue based on the user's geographical location information. This makes it possible to provide a dialogue in the optimal method based on the user's geographical location information. Some or all of the above-described 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 geographical location information data to the generation AI and cause the generation AI to select the optimal delivery method.

[0052] When providing dialogue, the providing unit can analyze the user's social media activity and suggest content to be provided. The providing unit can provide dialogue based on, for example, photos shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related dialogue. Furthermore, the providing unit can also provide related dialogue by referring to the activity of the user's friends on social media. This makes it possible to provide dialogue with optimal content based on the user's social media activity. Some or all of the above-mentioned 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 data into a generation AI and cause the generation AI to suggest content to be provided.

[0053] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a dialogue. The providing unit can improve the delivery method, for example, based on feedback from the user regarding a dialogue previously provided. The providing unit can also adjust the content of the dialogue by referring to the user's feedback. Furthermore, the providing unit can customize the delivery method based on the user's past feedback. This makes it possible to provide a dialogue in an optimal manner based on the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to customize the delivery method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photos and memories. For example, a detailed analysis can be performed for photos of important memories. A simplified analysis can also be performed for general photos. Furthermore, a detailed analysis can be performed for photos that the user particularly likes. This allows the analysis to be performed with an appropriate level of detail depending on the importance of the photos and memories. 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 importance data of the user's photos and memories into the generation AI and have the generation AI adjust the level of detail.

[0056] When receiving photos and memories, the reception unit can filter them based on the user's current living situation or areas of interest. For example, the reception unit can prioritize receiving photos and memories related to areas of interest in the user's current living situation. Related photos and memories can also be filtered and received based on the user's areas of interest. Furthermore, photos and memories can be received at an appropriate time depending on the user's living situation. This allows appropriate photos and memories to be received depending on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's living situation data into a generation AI and have the generation AI perform filtering.

[0057] When generating a dialogue, the generation unit can apply different dialogue generation algorithms depending on the category of the photo or memory. For example, an algorithm that generates dialogue themed on family relationships can be applied to family photos. Also, an algorithm that generates dialogue themed on travel destination information can be applied to travel photos. Furthermore, an algorithm that generates dialogue themed on food types and recipes can be applied to food photos. This makes it possible to apply an appropriate dialogue generation algorithm depending on the category of the photo or memory. 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 category data of photos and memories into the generation AI and cause the generation AI to apply the dialogue generation algorithm.

[0058] When providing a dialogue, the providing unit can select the optimal delivery method by referring to the user's past dialogue history. For example, the providing unit provides the dialogue by referring to delivery methods that the user has previously preferred. The delivery method can also be adjusted based on the user's past dialogue history. Furthermore, the optimal delivery method can be selected by referring to the user's past feedback. This makes it possible to provide the dialogue in the optimal method based on the user's past dialogue history. 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 past dialogue history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0059] 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 results can be provided using technical terminology. Alternatively, if the user does not have technical expertise, the analysis results can be provided in simple language. Furthermore, the analysis results can be provided by selecting appropriate terminology according to the user's level of expertise. This makes it possible to provide analysis results using appropriate terminology 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.

[0060] The reception unit can analyze the user's social media activity and receive related information when receiving photos and memories. For example, it can prioritize receiving photos shared by the user on social media. It can also analyze the content of the user's social media posts and receive related memories. It can also receive related photos and memories by referring to the activities of the user's friends on social media. This makes it possible to receive related photos and memories based on the user's social media activity. Some or all of the above-described processing by 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 social media data into a generation AI and have the generation AI select related information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts photos or memories from the user. The photos or memories from the user can include digital photos, paper photos, and verbal memories. The reception unit can accept digital photos through an online form, scan paper photos and convert them into digital data, or accept verbal memories from the user as voice input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as image analysis, text analysis, and emotion analysis. For example, the analysis unit can use image analysis technology to analyze the content of photos and text analysis technology to analyze spoken memories. Furthermore, emotion analysis technology can be used to analyze the user's emotions. Step 3: The generation unit generates a dialogue based on the information analyzed by the analysis unit. The dialogue is generated based on natural language generation technology and a dialogue scenario creation method. For example, the generation unit can use natural language generation technology to generate a dialogue based on the user's photos and memories, or can generate a dialogue using a dialogue scenario creation method. Furthermore, the dialogue can also be generated using a generation AI. Step 4: The providing unit provides the dialogue generated by the generating unit to the user. The dialogue may be provided by voice, text, or in an interactive dialogue format. For example, the providing unit may provide the dialogue generated by voice using speech synthesis technology, or may display the dialogue generated in text format on the user's device. Furthermore, the dialogue generated may be provided in an interactive dialogue format.

[0063] (Example 2) A system according to an embodiment of the present invention uses a generation AI to stimulate the brains of lonely elderly people, people who take photos of food at mealtimes, and people who take memorable photos on walks and trips, thereby maintaining their vitality and contributing to society. In this system, a user interacts with a generation AI, which generates and provides dialogues based on the user's photos and memories. This allows the user to reminisce about happy times and promotes brain activation. For example, for lonely elderly people, the generation AI generates dialogues based on old memorable photos. For example, when a user shows an old family photo, the generation AI begins a conversation about family memories based on the photo. Next, for people who take photos of food at mealtimes, the generation AI generates dialogues based on the food photos. For example, when a user shows a photo of a dish they took, the generation AI provides topics related to the dish. Furthermore, for people who take memorable photos on walks and trips, the generation AI generates dialogues based on the memorable photos. For example, when a user shows a photo they took at a travel destination, the generation AI provides topics related to the destination. This allows the user to reminisce about happy times and promotes brain activation. Furthermore, this system maintains their vitality and allows them to contribute to society. This allows the system to generate and provide dialogue based on the user's photos and memories, stimulating the user's brain and maintaining their vitality to contribute to society.

[0064] A dialogue generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos or memories from a user. The photos or memories from the user include, but are not limited to, digital photos, paper photos, and spoken memories. The reception unit receives, for example, digital photos through an online form. The reception unit can also scan paper photos and convert them into digital data. The reception unit can also receive spoken memories from the user as voice input. The analysis unit analyzes the information received by the reception unit. The analysis can be performed using, for example, image analysis, text analysis, or emotion analysis, but is not limited to, these examples. For example, the analysis unit can analyze the content of photos using image analysis technology. The analysis unit can also analyze spoken memories using text analysis technology. The analysis unit can also analyze the user's emotions using emotion analysis technology. The generation unit generates a dialogue based on the information analyzed by the analysis unit. The dialogue generation can be performed based on, for example, natural language generation technology or a dialogue scenario creation method, but is not limited to these examples. For example, the generation unit generates a dialogue based on the user's photos and memories using natural language generation technology. The generation unit can also generate a dialogue using a dialogue scenario creation method. Furthermore, the generation unit can also generate a dialogue using a generation AI. The provision unit provides the dialogue generated by the generation unit to the user. The provision can be performed, for example, in audio, text, or an interactive dialogue format, but is not limited to these examples. For example, the provision unit provides the dialogue generated by speech synthesis technology in audio format. The provision unit can also display the dialogue generated in text format on the user's device. Furthermore, the provision unit can provide the dialogue generated in an interactive dialogue format. Thus, the dialogue generation system according to the embodiment generates and provides a dialogue based on the user's photos and memories, thereby stimulating the user's brain and maintaining their vitality to contribute to society.

[0065] The reception unit can estimate the user's emotions and adjust the timing of receiving photos and memories based on the estimated user emotions. For example, if the user is sad, the reception unit can estimate the user's emotions using an emotion engine and temporarily delay the reception of photos and memories. Alternatively, if the user is happy, the reception unit can estimate the user's emotions using an emotion engine and immediately accept photos and memories. Furthermore, if the user is stressed, the reception unit can estimate the user's emotions using an emotion engine and accept photos and memories when the user is relaxed. This allows photos and memories to be accepted at an appropriate time 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 reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0066] The reception unit can analyze the user's past photo and memory submission history and select an appropriate reception method. For example, the reception unit can analyze the types of photos the user frequently submitted in the past and prioritize accepting similar photos. The reception unit can also analyze the content of memories the user previously submitted and prioritize accepting related memories. Furthermore, the reception unit can prioritize accepting photos and memories submitted during a specific time period based on the user's submission history. This allows photos and memories to be accepted in the optimal manner based on the user's past submission history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0067] The reception unit may filter photos and memories based on the user's current living situation or areas of interest when receiving the photos and memories. For example, the reception unit may preferentially receive photos and memories related to areas of interest in the user's current living situation. The reception unit may also filter and receive related photos and memories based on the user's areas of interest. Furthermore, the reception unit may also receive photos and memories at an appropriate time depending on the user's living situation. This allows appropriate photos and memories to be received depending on the user's current living situation and areas of interest. 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 may input the user's living situation data into a generation AI and have the generation AI perform filtering.

[0068] When accepting photos or memories, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user describes the photos or memories using voice, the acceptance unit can prioritize accepting voice input. Furthermore, when the user describes the photos or memories using text, the acceptance unit can also prioritize accepting text input. Furthermore, when the user submits the photos or memories using images, the acceptance unit can also prioritize accepting image input. This makes it possible to accept photos and memories using the optimal means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into a generation AI and have the generation AI select the optimal acceptance means.

[0069] The reception unit can estimate the user's emotions and determine the priority of photos and memories to be received based on the estimated user emotions. For example, if the user is sad, the reception unit can estimate the user's emotions using an emotion engine and prioritize receiving photos of happy memories. Furthermore, if the user is happy, the reception unit can estimate the user's emotions using an emotion engine and prioritize receiving recent photos. Furthermore, if the user is stressed, the reception unit can estimate the user's emotions using an emotion engine and prioritize receiving photos of relaxing memories. This allows the photos and memories to be prioritized based on the user's emotions. 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 reception unit can be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0070] When receiving photos and memories, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, the reception unit can prioritize receiving photos taken by the user in locations close to the user's current location. The reception unit can also prioritize receiving photos taken by the user at travel destinations. Furthermore, the reception unit can prioritize receiving related memories based on the user's geographical location information. This makes it possible to prioritize receiving highly relevant photos and memories based on the user's geographical location information. Some or all of the above-described processing by 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 a generation AI and cause the generation AI to select highly relevant information.

[0071] The reception unit can analyze the user's social media activity and receive related information when receiving photos and memories. For example, the reception unit can prioritize receiving photos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related memories. Furthermore, the reception unit can also receive related photos and memories by referring to the activity of the user's friends on social media. This makes it possible to receive related photos and memories based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI select related information.

[0072] The reception unit can customize the reception method by reflecting the user's past feedback when receiving photos and memories. For example, the reception unit may preferentially accept types of photos that the user has previously preferred. The reception unit can also adjust the reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception method by referring to the content of photos and memories that the user has previously submitted. This makes it possible to receive photos and memories in an optimal manner based on 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 feedback data into the generation AI and have the generation AI customize the reception method.

[0073] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, if the user is sad, the analysis unit can estimate the user's emotions using an emotion engine and provide the analysis result in a gentle expression. Furthermore, if the user is happy, the analysis unit can estimate the user's emotions using an emotion engine and provide the analysis result in a cheerful expression. Furthermore, if the user is stressed, the analysis unit can estimate the user's emotions using an emotion engine and provide the analysis result in a relaxing expression. This allows the analysis result to be provided in an appropriate expression method depending on 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 AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photos and memories. For example, the analysis unit performs a detailed analysis on photos of important memories. The analysis unit can also perform a simplified analysis on general photos. Furthermore, the analysis unit can also perform a detailed analysis on photos that the user particularly likes. This allows analysis to be performed with an appropriate level of detail depending on the importance of the photos and memories. 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 importance data of the user's photos and memories to the generation AI and cause the generation AI to adjust the level of detail.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the photo or memory. For example, the analysis unit can apply an algorithm that analyzes family relationships to family photos. The analysis unit can also apply an algorithm that analyzes travel destination information to travel photos. The analysis unit can also apply an algorithm that analyzes food types and recipes to food photos. This makes it possible to apply an appropriate analysis algorithm depending on the category of the photo or memory. 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 category data of photos and memories into the generation AI and cause the generation AI to apply the analysis algorithm.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user preferred in the past. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. 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 cause the generation AI to improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can estimate the user's emotions using an emotion engine and provide a short analysis result. Furthermore, if the user is relaxed, the analysis unit can estimate the user's emotions using an emotion engine and provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can estimate the user's emotions using an emotion engine and provide a concise analysis result. This allows the analysis result to be provided at an appropriate length depending on 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 AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of photos and memories. For example, the analysis unit prioritizes analysis of recently submitted photos and memories. The analysis unit can also prioritize analysis of photos and memories related to a specific event. Furthermore, the analysis unit can prioritize analysis of photos and memories that the user particularly likes. This allows analysis to be prioritized based on the time of submission of photos and memories. 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 data on the time of submission of photos and memories into the generation AI and have the generation AI determine the priority.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of photos and memories. For example, the analysis unit prioritizes analysis of highly relevant photos and memories. The analysis unit can also prioritize analysis of photos and memories that the user particularly likes. Furthermore, the analysis unit can also prioritize analysis of highly relevant photos and memories by referring to the user's past submission history. This allows analysis to be performed in an appropriate order based on the relevance of photos and memories. 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 relevance data of photos and memories into the generation AI and cause the generation AI to adjust the order of analysis.

[0080] 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 can provide the analysis result using technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result in simple language. Furthermore, the analysis unit can select appropriate terminology according to the user's level of expertise and provide the analysis result. This allows the analysis result to be provided using appropriate terminology 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 cause the generation AI to adjust the use of technical terminology.

[0081] The generation unit can estimate the user's emotion and adjust the dialogue expression method based on the estimated user emotion. For example, if the user is sad, the generation unit can estimate the emotion using an emotion engine and generate dialogue with a gentle expression. Furthermore, if the user is happy, the generation unit can estimate the emotion using an emotion engine and generate dialogue with a cheerful expression. Furthermore, if the user is stressed, the generation unit can estimate the emotion using an emotion engine and generate dialogue with a relaxing expression. This allows dialogue to be generated with an appropriate expression method depending on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, with 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, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method.

[0082] When generating a dialogue, the generation unit can adjust the level of detail of the dialogue based on the importance of the photo or memory. For example, the generation unit generates detailed dialogue for photos of important memories. The generation unit can also generate simple dialogue for general photos. Furthermore, the generation unit can also generate detailed dialogue for photos that the user particularly likes. This makes it possible to generate dialogue with an appropriate level of detail depending on the importance of the photo or memory. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input importance data of photos and memories into the generation AI and cause the generation AI to adjust the level of detail.

[0083] When generating a dialogue, the generation unit can apply different dialogue generation algorithms depending on the category of the photo or memory. For example, the generation unit applies an algorithm that generates dialogue themed around family relationships to family photos. The generation unit can also apply an algorithm that generates dialogue themed around travel destination information to travel photos. The generation unit can also apply an algorithm that generates dialogue themed around food types and recipes to food photos. This makes it possible to apply an appropriate dialogue generation algorithm depending on the category of the photo or memory. 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 category data of photos and memories into the generation AI and cause the generation AI to apply the dialogue generation algorithm.

[0084] When generating a dialogue, the generation unit can improve the accuracy of the dialogue by referring to the user's past dialogue results. The generation unit can improve the accuracy of the dialogue by referring to, for example, dialogue content that the user has preferred in the past. The generation unit can also adjust the dialogue generation algorithm based on the user's past dialogue results. Furthermore, the generation unit can also improve the accuracy of the dialogue by referring to the user's past feedback. In this way, the accuracy of the dialogue can be improved by referring to the user's past dialogue results. 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 past dialogue result data into the generation AI and cause the generation AI to improve the accuracy of the dialogue.

[0085] The generation unit can estimate the user's emotion and adjust the length of the dialogue based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can estimate the emotion using an emotion engine and generate a short dialogue. Furthermore, if the user is relaxed, the generation unit can estimate the emotion using an emotion engine and generate a detailed dialogue. Furthermore, if the user is stressed, the generation unit can estimate the emotion using an emotion engine and generate a concise dialogue. This allows a dialogue of an appropriate length to be generated according to the user's emotion. 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 generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the dialogue.

[0086] When generating a dialogue, the generation unit can determine the priority of the dialogue based on the time when the photos and memories were submitted. The generation unit generates a dialogue based on, for example, recently submitted photos and memories. The generation unit can also generate a dialogue based on photos and memories related to a specific event. Furthermore, the generation unit can generate a dialogue based on photos and memories that the user particularly likes. This allows dialogues to be generated preferentially based on the time when the photos and memories were submitted. Some or all of the above-described 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 data on the time when the photos and memories were submitted into the generation AI and have the generation AI determine the priority.

[0087] When generating a dialogue, the generation unit can adjust the order of the dialogues based on the relevance of the photos and memories. The generation unit, for example, generates dialogues based on highly relevant photos and memories. The generation unit can also generate dialogues based on photos and memories that the user particularly likes. Furthermore, the generation unit can generate dialogues based on highly relevant photos and memories by referring to the user's past submission history. This makes it possible to generate dialogues in an appropriate order based on the relevance of the photos and memories. Some or all of the above-described 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 relevance data of photos and memories into the generation AI and cause the generation AI to adjust the order of the dialogues.

[0088] When generating a dialogue, the generation unit can adjust the use of technical terminology in the dialogue according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates the dialogue using technical terminology. Also, if the user does not have technical expertise, the generation unit can generate the dialogue using simple language. Furthermore, the generation unit can generate the dialogue by selecting appropriate terminology according to the user's level of expertise. This makes it possible to generate a dialogue using appropriate terminology according to the user's level of expertise. Some or all of the above-described 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.

[0089] The providing unit can estimate the user's emotions and adjust the dialogue provision method based on the estimated user emotions. For example, if the user is sad, the providing unit can estimate the user's emotions using an emotion engine and provide dialogue in a gentle voice. Furthermore, if the user is happy, the providing unit can estimate the user's emotions using an emotion engine and provide dialogue in a cheerful voice. Furthermore, if the user is stressed, the providing unit can estimate the user's emotions using an emotion engine and provide dialogue in a relaxing voice. This allows dialogue to be provided in an appropriate manner depending on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using 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 providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the delivery method.

[0090] When providing a dialogue, the providing unit can select an optimal delivery method by referring to the user's past dialogue history. The providing unit provides the dialogue, for example, by referring to delivery methods that the user has preferred in the past. The providing unit can also adjust the delivery method based on the user's past dialogue history. Furthermore, the providing unit can also select an optimal delivery method by referring to the user's past feedback. This makes it possible to provide a dialogue in an optimal method based on the user's past dialogue history. 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 past dialogue history data into the generation AI and cause the generation AI to select an optimal delivery method.

[0091] The providing unit can customize the content to be provided based on the user's current living situation when providing the dialogue. For example, the providing unit provides dialogue related to areas in which the user is interested in the user's current living situation. The providing unit can also provide the dialogue at an appropriate time depending on the user's living situation. Furthermore, the providing unit can customize the content of the dialogue taking into account the user's current living situation. This makes it possible to provide dialogue with appropriate content depending on the user's current living situation. Some or all of the above-described 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 living situation data into a generation AI and cause the generation AI to customize the content to be provided.

[0092] The providing unit can improve the method of providing dialogue by reflecting user feedback. For example, the providing unit improves the method of providing dialogue based on feedback from the user regarding dialogues previously provided by the user. The providing unit can also adjust the content of the dialogue by referring to the user's feedback. Furthermore, the providing unit can customize the method of providing dialogue based on the user's past feedback. This makes it possible to provide dialogue in an optimal manner based on the user's feedback. Some or all of the above-described 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 user feedback data into the generating AI and cause the generating AI to improve the method of providing dialogue.

[0093] The providing unit can estimate the user's emotions and determine the priority of dialogue provision based on the estimated user emotions. For example, if the user is sad, the providing unit can estimate the user's emotions using an emotion engine and prioritize providing happy dialogues. Furthermore, if the user is happy, the providing unit can estimate the user's emotions using an emotion engine and prioritize providing recent dialogues. Furthermore, if the user is stressed, the providing unit can estimate the user's emotions using an emotion engine and prioritize providing relaxing dialogues. This allows dialogues to be provided preferentially 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, 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 providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the provision priority.

[0094] When providing a dialogue, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, the providing unit can provide a dialogue based on a photo taken by the user in a location close to the user's current location. The providing unit can also provide a dialogue based on a photo taken by the user at a travel destination. Furthermore, the providing unit can also provide a related dialogue based on the user's geographical location information. This makes it possible to provide a dialogue in the optimal method based on the user's geographical location information. Some or all of the above-described 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 geographical location information data to the generation AI and cause the generation AI to select the optimal delivery method.

[0095] When providing dialogue, the providing unit can analyze the user's social media activity and suggest content to be provided. The providing unit can provide dialogue based on, for example, photos shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related dialogue. Furthermore, the providing unit can also provide related dialogue by referring to the activity of the user's friends on social media. This makes it possible to provide dialogue with optimal content based on the user's social media activity. Some or all of the above-mentioned 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 data into a generation AI and cause the generation AI to suggest content to be provided.

[0096] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a dialogue. The providing unit can improve the delivery method, for example, based on feedback from the user regarding a dialogue previously provided. The providing unit can also adjust the content of the dialogue by referring to the user's feedback. Furthermore, the providing unit can customize the delivery method based on the user's past feedback. This makes it possible to provide a dialogue in an optimal manner based on the user's past feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 photos and memories from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a dialogue based on the analyzed information. For example, the provision unit is realized by the output device 40 of the smart device 14 and provides the generated dialogue to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 photos and memories from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a dialogue based on the analyzed information. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated dialogue to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 photos and memories from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a dialogue based on the analyzed information. For example, the provision unit is realized by the speaker 240 of the headset type terminal 314 and provides the generated dialogue to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 photos and memories from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a dialogue based on the analyzed information. For example, the provision unit is realized by the speaker 240 of the robot 414 and provides the generated dialogue to the user.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is sad, the emotion engine can estimate the user's emotions and prioritize analysis of photos of happy memories. Alternatively, if the user is happy, the emotion engine can estimate the user's emotions and prioritize analysis of recent photos. Furthermore, if the user is stressed, the emotion engine can estimate the user's emotions and prioritize analysis of photos of relaxing memories. This allows analysis to be prioritized 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, 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities.

[0099] The providing unit can estimate the user's emotions and adjust the dialogue provision method based on the estimated user emotions. For example, if the user is sad, the emotion engine can estimate the user's emotions and provide dialogue in a gentle voice. Alternatively, if the user is happy, the emotion engine can estimate the user's emotions and provide dialogue in a cheerful voice. Furthermore, if the user is stressed, the emotion engine can estimate the user's emotions and provide dialogue in a relaxing voice. This allows dialogue to be provided in an appropriate manner depending on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, with 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 providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the delivery method.

[0100] The generation unit can estimate the user's emotions and adjust the dialogue expression method based on the estimated user emotions. For example, if the user is sad, the emotion engine can estimate the user's emotions and generate dialogue with a gentle expression. Also, if the user is happy, the emotion engine can estimate the user's emotions and generate dialogue with a cheerful expression. Furthermore, if the user is stressed, the emotion engine can estimate the user's emotions and generate dialogue with a relaxing expression. This allows dialogue to be generated with an appropriate expression method depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with 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 emotion data into the generation AI and have the generation AI adjust the expression method.

[0101] The reception unit can estimate the user's emotions and determine the priority of photos and memories to be received based on the estimated user emotions. For example, if the user is sad, the emotion engine can estimate the user's emotions and prioritize receiving photos of happy memories. Alternatively, if the user is happy, the emotion engine can estimate the user's emotions and prioritize receiving recent photos. Furthermore, if the user is stressed, the emotion engine can estimate the user's emotions and prioritize receiving photos of relaxing memories. This allows the photos and memories to be prioritized based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0102] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photos and memories. For example, a detailed analysis can be performed for photos of important memories. A simplified analysis can also be performed for general photos. Furthermore, a detailed analysis can be performed for photos that the user particularly likes. This allows the analysis to be performed with an appropriate level of detail depending on the importance of the photos and memories. 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 importance data of the user's photos and memories into the generation AI and have the generation AI adjust the level of detail.

[0103] When receiving photos and memories, the reception unit can filter them based on the user's current living situation or areas of interest. For example, the reception unit can prioritize receiving photos and memories related to areas of interest in the user's current living situation. Related photos and memories can also be filtered and received based on the user's areas of interest. Furthermore, photos and memories can be received at an appropriate time depending on the user's living situation. This allows appropriate photos and memories to be received depending on the user's current living situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's living situation data into a generation AI and have the generation AI perform filtering.

[0104] When generating a dialogue, the generation unit can apply different dialogue generation algorithms depending on the category of the photo or memory. For example, an algorithm that generates dialogue themed on family relationships can be applied to family photos. Also, an algorithm that generates dialogue themed on travel destination information can be applied to travel photos. Furthermore, an algorithm that generates dialogue themed on food types and recipes can be applied to food photos. This makes it possible to apply an appropriate dialogue generation algorithm depending on the category of the photo or memory. 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 category data of photos and memories into the generation AI and cause the generation AI to apply the dialogue generation algorithm.

[0105] When providing a dialogue, the providing unit can select the optimal delivery method by referring to the user's past dialogue history. For example, the providing unit provides the dialogue by referring to delivery methods that the user has previously preferred. The delivery method can also be adjusted based on the user's past dialogue history. Furthermore, the optimal delivery method can be selected by referring to the user's past feedback. This makes it possible to provide the dialogue in the optimal method based on the user's past dialogue history. 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 past dialogue history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0106] 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 results can be provided using technical terminology. Alternatively, if the user does not have technical expertise, the analysis results can be provided in simple language. Furthermore, the analysis results can be provided by selecting appropriate terminology according to the user's level of expertise. This makes it possible to provide analysis results using appropriate terminology 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.

[0107] The reception unit can analyze the user's social media activity and receive related information when receiving photos and memories. For example, it can prioritize receiving photos shared by the user on social media. It can also analyze the content of the user's social media posts and receive related memories. It can also receive related photos and memories by referring to the activities of the user's friends on social media. This makes it possible to receive related photos and memories based on the user's social media activity. Some or all of the above-described processing by 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 social media data into a generation AI and have the generation AI select related information.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit accepts photos or memories from the user. The photos or memories from the user can include digital photos, paper photos, and verbal memories. The reception unit can accept digital photos through an online form, scan paper photos and convert them into digital data, or accept verbal memories from the user as voice input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as image analysis, text analysis, and emotion analysis. For example, the analysis unit can use image analysis technology to analyze the content of photos and text analysis technology to analyze spoken memories. Furthermore, emotion analysis technology can be used to analyze the user's emotions. Step 3: The generation unit generates a dialogue based on the information analyzed by the analysis unit. The dialogue is generated based on natural language generation technology and a dialogue scenario creation method. For example, the generation unit can use natural language generation technology to generate a dialogue based on the user's photos and memories, or can generate a dialogue using a dialogue scenario creation method. Furthermore, the dialogue can also be generated using a generation AI. Step 4: The providing unit provides the dialogue generated by the generating unit to the user. The dialogue may be provided by voice, text, or in an interactive dialogue format. For example, the providing unit may provide the dialogue generated by voice using speech synthesis technology, or may display the dialogue generated in text format on the user's device. Furthermore, the dialogue generated may be provided in an interactive dialogue format.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving photos or memories from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a dialogue based on the information analyzed by the analysis unit; a providing unit that provides the dialogue generated by the generating unit to a user; Equipped with A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of accepting photos and memories based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past photo and memory submission history and select the appropriate reception method.

2. The system of claim 1.

4. The reception unit Filtering photos and memories based on the user's current life situation or interests when accepting them 2. The system of claim 1.

5. The reception unit When accepting photos and memories, select the appropriate acceptance 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 photos and memories to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When collecting photos and memories, prioritize relevant information based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When accepting photos and memories, analyze users' social media activity and accept related information.

2. The system of claim 1.

Images