system

Abstract

The system according to the embodiment aims to efficiently record and organize daily conversations and events. [Solution] A system according to an embodiment includes a recording unit, an analysis unit, a classification unit, and a generation unit. The recording unit records conversations and images. The analysis unit analyzes the data recorded by the recording unit. The classification unit separates the data analyzed by the analysis unit into categories. The generation unit creates a diary based on the data classified by the classification unit.

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 making it difficult to efficiently record and organize daily conversations and events.

[0005] The system according to the embodiment aims to efficiently record and organize daily conversations and events. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a classification unit, and a generation unit. The recording unit records conversations and images. The analysis unit analyzes the data recorded by the recording unit. The classification unit categorizes the data analyzed by the analysis unit. The generation unit creates a diary based on the data classified by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently record and organize daily conversations and events. [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 diary creation system according to an embodiment of the present invention uses a generation AI to record daily conversations and images, analyze them, and automatically create a diary. In this diary creation system, the generation AI listens to all daily conversations and records them, including camera images. The generation AI then analyzes the recorded data and categorizes them into important conversation content, places visited, people met, and tasks to be completed. Finally, the generation AI automatically creates a diary. This service helps prevent forgetting tasks and avoiding disputes over who said what in conversations. For example, the generation AI listens to all daily conversations and records them, including camera images. The generation AI simultaneously records audio and image data to collect data for later analysis. For example, it records the content of conversations, places visited, and faces of people met. The generation AI then analyzes the recorded data. The generation AI analyzes the audio data to identify important conversation content and analyzes image data to identify places visited and people met. For example, it extracts keywords and phrases deemed "important" from conversations and recognizes specific locations and people from the image data. The analyzed data is then categorized by the AI. For example, it can be classified into categories such as important conversations, places visited, people met, and things to do. This allows users to easily check the information later. Finally, the AI ​​automatically creates a diary. Based on the analyzed data, the AI ​​generates a diary that summarizes the events of the day. For example, it can create a diary that includes important conversations, places visited, people met, and things to do. This service prevents users from forgetting things they need to do or from arguing over what was said in conversations. Data handling is also carefully considered to protect privacy. For example, measures such as data encryption and access restrictions are implemented. This allows the diary creation system to automatically record and organize the user's daily events and provide them as a diary.

[0029] A diary creation system according to an embodiment includes a recording unit, an analysis unit, a classification unit, and a generation unit. The recording unit records conversations and images. The recording unit can, for example, simultaneously record audio data and image data. The recording unit can, for example, collect audio data using a microphone and collect image data using a camera. The recording unit can, for example, save audio data in MP3 or WAV format and save image data in JPEG or PNG format. The analysis unit analyzes the data recorded by the recording unit. The analysis unit can, for example, analyze the audio data to identify important conversation content. The analysis unit can, for example, use keyword extraction technology to extract important keywords and phrases in the conversation. The analysis unit can also, for example, use emotion analysis technology to analyze emotional nuances in the conversation. The analysis unit can, for example, analyze image data to identify places visited and people met. The analysis unit can, for example, use image recognition technology to recognize specific places and people from the image data. The classification unit categorizes the data analyzed by the analysis unit. The classification unit can classify data into categories such as important conversations, places visited, people met, and things that need to be done. The classification unit can create a list of things that need to be done, for example, using task management technology. The generation unit creates a diary based on the data classified by the classification unit. The generation unit can automatically generate the diary, for example, using template-based generation technology. The generation unit can generate diary sentences, for example, using natural language generation technology. As a result, the diary creation system according to the embodiment can record conversations and images and automatically analyze, classify, and create a diary.

[0030] The recording unit can simultaneously record audio data and image data. For example, the recording unit collects audio data using a microphone and collects image data using a camera. For example, the recording unit saves audio data in MP3 or WAV format and saves image data in JPEG or PNG format. For example, the recording unit can use synchronization technology to simultaneously record audio data and image data. For example, the recording unit can add a timestamp to synchronize the audio data and image data. This allows for more detailed information to be collected by simultaneously recording the audio data and image data.

[0031] The analysis unit can analyze the voice data to identify important conversation content. The analysis unit can extract important keywords and phrases in the conversation, for example, using keyword extraction technology. The analysis unit can also analyze the emotional nuances of the conversation, for example, using emotion analysis technology. The analysis unit can convert the voice data into text data, for example, using voice recognition technology. For example, the analysis unit can use voice recognition technology to save the content of the conversation as text data and identify important conversation content based on the text data. In this way, important information can be extracted by analyzing the voice data to identify important conversation content.

[0032] The analysis unit can analyze the image data to identify places visited and people met. The analysis unit can, for example, use image recognition technology to recognize specific places and people from the image data. The analysis unit can, for example, use GPS data to identify places visited. The analysis unit can, for example, use face recognition technology to identify people met. For example, the analysis unit can recognize faces included in the image data and identify the identity of the face. This allows for detailed recording by analyzing the image data to identify places visited and people met.

[0033] The classification unit can classify data into categories such as important conversations, places visited, people met, and things that need to be done. The classification unit can, for example, use task management technology to create a list of things that need to be done. The classification unit can, for example, classify important conversations based on keywords. The classification unit can, for example, classify places visited based on GPS data. The classification unit can, for example, classify people met based on facial recognition technology. Thus, by classifying data into categories, it becomes easier to organize information.

[0034] The generation unit can create a diary based on the analyzed data. The generation unit can automatically generate a diary, for example, using a template-based generation technique. The generation unit can generate diary sentences, for example, using a natural language generation technique. The generation unit can generate a diary that summarizes the events of the day, for example, based on the analyzed data. For example, the generation unit can create a diary that includes important conversations, places visited, people met, things that need to be done, etc. This allows the user to automatically create a diary by creating a diary based on the analyzed data.

[0035] Furthermore, the diary creation system includes a protection unit for privacy protection, encrypting data and restricting access, thereby enabling the diary creation system to safely protect user data.

[0036] When recording, the recording unit can select the optimal recording method by referring to the user's past behavioral history. For example, if the user missed an important conversation in the past, the recording unit can have the generation AI increase the audio sensitivity for recording. For example, if the recording unit places importance on conversations at places the user has visited in the past, the generation AI can prioritize recording at those places. For example, if the user had an important conversation at a specific time period in the past, the recording unit can strengthen recording at that time period. This improves the accuracy of recording by selecting the optimal recording method by referring to the user's past behavioral history.

[0037] The recording unit can determine the priority of recording based on the user's current activity status when recording. For example, when the user is in a meeting, the recording unit can cause the generation AI to prioritize recording of conversations. For example, when the user is traveling, the recording unit can cause the generation AI to prioritize recording of places visited. For example, when the user is taking a break, the recording unit can cause the generation AI to prioritize recording of relaxed conversations. In this way, by determining the priority of recording based on the user's current activity status, important information can be recorded preferentially.

[0038] When recording, the recording unit can prioritize recording highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific location, the recording unit can prioritize recording conversations at that location. For example, when the user is traveling, the recording unit can prioritize recording places the user has visited. For example, when the user is at home, the recording unit can prioritize recording everyday conversations. In this way, by prioritizing recording highly relevant data in consideration of the user's geographical location information, important information can be recorded without missing anything.

[0039] The recording unit can analyze the user's social media activities and record related data during recording. For example, if the user is having an important conversation on social media, the recording unit can prioritize recording that conversation. For example, if the user is sharing a place they visited on social media, the recording unit can prioritize recording that place. For example, if the user is tagging a person they met on social media, the recording unit can prioritize recording a conversation with that person. This allows more information to be collected by analyzing the user's social media activities and recording related data.

[0040] During analysis, the analysis unit can remove background sounds from the audio data to identify important conversation content. For example, the generation AI can filter background sounds during conversation and extract clear audio data. For example, the analysis unit can use noise canceling technology to identify important conversation content. For example, the generation AI can remove background sounds from the audio data and extract the main points of the conversation. As a result, by removing background sounds from the audio data, important conversation content can be more clearly identified.

[0041] During analysis, the analysis unit can remove noise from the image data to identify places visited and people met. For example, the generation AI in the analysis unit can filter noise from the image data and extract clear images. For example, the generation AI in the analysis unit can remove noise from the image data to identify places visited. For example, the generation AI in the analysis unit can remove noise from the image data to identify faces of people met. In this way, by removing noise from the image data, places visited and people met can be identified more accurately.

[0042] During analysis, the analysis unit can identify important conversation content by referring to the user's past conversation history. The analysis unit, for example, allows the generation AI to analyze the user's past conversation history and extract important keywords. The analysis unit, for example, allows the generation AI to identify important conversation content by referring to the past conversation history. The analysis unit, for example, allows the generation AI to extract important information based on the past conversation history. This allows important conversation content to be identified more accurately by referring to the user's past conversation history.

[0043] During analysis, the analysis unit can identify places visited by referring to the user's past visit history. The analysis unit can, for example, have the generation AI analyze the user's past visit history and identify places visited. The analysis unit can, for example, have the generation AI refer to the past visit history and identify places visited. The analysis unit can, for example, have the generation AI refer to the past visit history and identify places visited based on the past visit history. In this way, by referring to the user's past visit history, it is possible to more accurately identify places visited.

[0044] The classification unit can improve the accuracy of classification by taking into account the interrelationships of data during classification. For example, the generation AI can analyze the relationship between the content of conversations and the places visited, and perform highly accurate classification. For example, the generation AI can analyze the relationship between the content of conversations and the people met, and perform highly accurate classification. For example, the generation AI can analyze the relationship between the places visited and the people met, and perform highly accurate classification. In this way, the accuracy of classification is improved by taking into account the interrelationships of data.

[0045] The classification unit can determine the priority of classification based on the importance of data when classifying. For example, the classification unit can prioritize classification of important conversation content by the generation AI. For example, the classification unit can perform classification based on the importance of places visited by the generation AI. For example, the classification unit can perform classification based on the importance of people met by the generation AI. In this way, by determining the priority of classification based on the importance of data, important information can be prioritized.

[0046] The classification unit can determine the priority of classification based on the time of data submission during classification. The classification unit can, for example, allow the generation AI to prioritize classifying the most recent data. The classification unit can, for example, allow the generation AI to refer to past data and prioritize classifying important information. The classification unit can, for example, allow the generation AI to determine the priority of classification based on the time of data submission. In this way, by determining the priority of classification based on the time of data submission, the most recent information can be prioritized.

[0047] The classification unit can adjust the order of classification based on the relevance of the data during classification. For example, the generation AI can adjust the order of classification based on the relevance between the content of the conversation and the places visited. For example, the generation AI can adjust the order of classification based on the relevance between the content of the conversation and the people met. For example, the generation AI can adjust the order of classification based on the relevance between the places visited and the people met. In this way, adjusting the order of classification based on the relevance of the data makes it easier to organize information.

[0048] The generation unit can adjust the level of detail in the diary based on the importance of the data at the time of generation. For example, the generation unit can allow the generation AI to describe important conversation content in detail. For example, the generation unit can adjust the level of detail based on the importance of places visited by the generation AI. For example, the generation unit can adjust the level of detail based on the importance of people met by the generation AI. In this way, important information can be described in detail by adjusting the level of detail in the diary based on the importance of the data.

[0049] The generation unit can apply different generation algorithms depending on the data category at the time of generation. For example, the generation unit can create a dialogue-style diary based on the content of a conversation by the generation AI. For example, the generation unit can create a travelogue-style diary based on places visited by the generation AI. For example, the generation unit can create a person introduction-style diary based on people met by the generation AI. This makes it possible to create a more appropriate diary by applying different generation algorithms depending on the data category.

[0050] At the time of generation, the generation unit can determine the priority of the diary based on the time of data submission. The generation unit, for example, can cause the generation AI to preferentially reflect the latest data in the diary. The generation unit, for example, can cause the generation AI to refer to past data and preferentially reflect important information in the diary. The generation unit, for example, can cause the generation AI to determine the priority of the diary based on the time of data submission. In this way, by determining the priority of the diary based on the time of data submission, the latest information can be preferentially reflected in the diary.

[0051] The generation unit can adjust the order of the diary entries based on the relevance of the data at the time of generation. For example, the generation AI can adjust the order of the diary entries based on the relevance between the content of the conversation and the places visited. For example, the generation AI can adjust the order of the diary entries based on the relevance between the content of the conversation and the people met. For example, the generation AI can adjust the order of the diary entries based on the relevance between the places visited and the people met. This makes it easier to organize information by adjusting the order of the diary entries based on the relevance of the data.

[0052] When protecting data, the protection unit can set optimal access restrictions by referring to the user's past access history. The protection unit can, for example, have the generation AI analyze the user's past access history and set optimal access restrictions. The protection unit can, for example, have the generation AI set strict access restrictions for important data based on the past access history. The protection unit can, for example, have the generation AI refer to the past access history and set appropriate access restrictions. In this way, data protection is strengthened by setting optimal access restrictions by referring to the user's past access history.

[0053] When protecting data, the protection unit can select the optimal protection method by taking into account the user's device information. For example, the generation AI can analyze the user's device information and select the optimal encryption method. For example, the generation AI can select the appropriate protection method based on the device's security level. For example, the generation AI can select the optimal protection method by taking into account the device usage status. This strengthens data protection by selecting the optimal protection method by taking into account the user's device information.

[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] The diary creation system may further include a health management unit that acquires the user's health data and reflects it in the diary. For example, the health management unit may acquire data such as the user's heart rate, number of steps, and sleep time, and add records of the user's health status to the diary. This allows the user to check their daily health status through the diary. The health management unit may also analyze the user's health data and include health advice in the diary. For example, if the user is not getting enough exercise, a message encouraging them to exercise may be added to the diary. Furthermore, the health management unit may adjust the contents of the diary based on the user's health data. For example, if the user is tired, advice to relax may be included in the diary.

[0056] The diary creation system may further include a hobby management unit that customizes the contents of the diary based on the user's hobbies and interests. For example, the hobby management unit may obtain information about the user's favorite movies, music, sports, etc., and add content related to the diary. This allows the user to enjoy a diary that is tailored to their hobbies and interests. The hobby management unit may also include events and news related to the user's hobbies in the diary. For example, information about the release of a new song by a favorite band may be added to the diary. Furthermore, the hobby management unit may customize the layout and design of the diary based on the user's hobbies. For example, a sports-themed design may be applied to a user who likes sports.

[0057] The diary creation system may further include a social media integration unit that analyzes the user's social media activities and reflects them in the diary. For example, the social media integration unit may obtain photos and comments posted by the user and add them to the diary. This allows the user to summarize their social media activities in the diary. The social media integration unit may also include the user's interactions with friends and followers in the diary. For example, conversations with friends and event participation records may be added to the diary. Furthermore, the social media integration unit may also reflect the user's social media trends and topics in the diary. For example, popular hashtags and topics may be included in the diary.

[0058] The diary creation system may further include a behavior history analysis unit that analyzes the user's past behavior history and personalizes the content of the diary. For example, the behavior history analysis unit can add related information to the diary based on places the user has visited or events the user has participated in in the past, allowing the user to look back on past events. The behavior history analysis unit can also analyze the user's past behavior patterns and include future plans and goals in the diary. For example, regular activities and progress toward achieving goals can be added to the diary. Furthermore, the behavior history analysis unit can customize the content of the diary based on the user's past behavior. For example, information related to specific seasons or events can be included in the diary.

[0059] The diary creation system can further include a learning management unit that analyzes the user's learning history and reflects the learning content in the diary. For example, the learning management unit can add the user's learned content and progress to the diary. This allows the user to check the results of their learning through the diary. The learning management unit can also include the next learning content and reference materials in the diary based on the user's learning history. For example, it can add unstudied topics and related reference books to the diary. Furthermore, the learning management unit can customize the content of the diary based on the user's learning style. For example, a user who prefers visual learning can create a diary that makes extensive use of diagrams and graphs.

[0060] The diary creation system may further include a travel management unit that analyzes the user's travel history and reflects the travel notes in the diary. For example, the travel management unit may add the places the user has visited and the activities they have experienced to the diary. This allows the user to look back on their travel memories through the diary. The travel management unit may also include next places to visit and recommended activities in the diary based on the user's travel history. For example, unvisited tourist spots and popular restaurants may be added to the diary. Furthermore, the travel management unit may customize the contents of the diary based on the user's travel style. For example, an adventurous user may be able to create a diary that includes many active activities.

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

[0062] Step 1: The recording unit records the conversation and the image. The recording unit can, for example, simultaneously record audio data and image data. For example, the recording unit collects audio data using a microphone and collects image data using a camera. For example, the recording unit saves the audio data in MP3 or WAV format and the image data in JPEG or PNG format. Step 2: The analysis unit analyzes the data recorded by the recording unit. The analysis unit, for example, analyzes the audio data to identify important conversation content. The analysis unit can, for example, use keyword extraction technology to extract important keywords and phrases in the conversation. The analysis unit can also, for example, use emotion analysis technology to analyze the emotional nuances of the conversation. The analysis unit, for example, analyzes image data to identify places visited and people met. The analysis unit can, for example, use image recognition technology to recognize specific places and people from the image data. Step 3: The classifier categorizes the data analyzed by the analyzer. For example, the classifier can categorize the data into categories such as important conversations, places visited, people met, and things to do. For example, the classifier can use task management techniques to create a list of things to do. Step 4: The generation unit creates a diary based on the data classified by the classification unit. The generation unit can automatically generate the diary using, for example, a template-based generation technique. The generation unit can generate diary sentences using, for example, a natural language generation technique.

[0063] (Example 2) A diary creation system according to an embodiment of the present invention uses a generation AI to record daily conversations and images, analyze them, and automatically create a diary. In this diary creation system, the generation AI listens to all daily conversations and records them, including camera images. The generation AI then analyzes the recorded data and categorizes them into important conversation content, places visited, people met, and tasks to be completed. Finally, the generation AI automatically creates a diary. This service helps prevent forgetting tasks and avoiding disputes over who said what in conversations. For example, the generation AI listens to all daily conversations and records them, including camera images. The generation AI simultaneously records audio and image data to collect data for later analysis. For example, it records the content of conversations, places visited, and faces of people met. The generation AI then analyzes the recorded data. The generation AI analyzes the audio data to identify important conversation content and analyzes image data to identify places visited and people met. For example, it extracts keywords and phrases deemed "important" from conversations and recognizes specific locations and people from the image data. The analyzed data is then categorized by the AI. For example, it can be classified into categories such as important conversations, places visited, people met, and things to do. This allows users to easily check the information later. Finally, the AI ​​automatically creates a diary. Based on the analyzed data, the AI ​​generates a diary that summarizes the events of the day. For example, it can create a diary that includes important conversations, places visited, people met, and things to do. This service prevents users from forgetting things they need to do or from arguing over what was said in conversations. Data handling is also carefully considered to protect privacy. For example, measures such as data encryption and access restrictions are implemented. This allows the diary creation system to automatically record and organize the user's daily events and provide them as a diary.

[0064] A diary creation system according to an embodiment includes a recording unit, an analysis unit, a classification unit, and a generation unit. The recording unit records conversations and images. The recording unit can, for example, simultaneously record audio data and image data. The recording unit can, for example, collect audio data using a microphone and collect image data using a camera. The recording unit can, for example, save audio data in MP3 or WAV format and save image data in JPEG or PNG format. The analysis unit analyzes the data recorded by the recording unit. The analysis unit can, for example, analyze the audio data to identify important conversation content. The analysis unit can, for example, use keyword extraction technology to extract important keywords and phrases in the conversation. The analysis unit can also, for example, use emotion analysis technology to analyze emotional nuances in the conversation. The analysis unit can, for example, analyze image data to identify places visited and people met. The analysis unit can, for example, use image recognition technology to recognize specific places and people from the image data. The classification unit categorizes the data analyzed by the analysis unit. The classification unit can classify data into categories such as important conversations, places visited, people met, and things that need to be done. The classification unit can create a list of things that need to be done, for example, using task management technology. The generation unit creates a diary based on the data classified by the classification unit. The generation unit can automatically generate the diary, for example, using template-based generation technology. The generation unit can generate diary sentences, for example, using natural language generation technology. As a result, the diary creation system according to the embodiment can record conversations and images and automatically analyze, classify, and create a diary.

[0065] The recording unit can simultaneously record audio data and image data. For example, the recording unit collects audio data using a microphone and collects image data using a camera. For example, the recording unit saves audio data in MP3 or WAV format and saves image data in JPEG or PNG format. For example, the recording unit can use synchronization technology to simultaneously record audio data and image data. For example, the recording unit can add a timestamp to synchronize the audio data and image data. This allows for more detailed information to be collected by simultaneously recording the audio data and image data.

[0066] The analysis unit can analyze the voice data to identify important conversation content. The analysis unit can extract important keywords and phrases in the conversation, for example, using keyword extraction technology. The analysis unit can also analyze the emotional nuances of the conversation, for example, using emotion analysis technology. The analysis unit can convert the voice data into text data, for example, using voice recognition technology. For example, the analysis unit can use voice recognition technology to save the content of the conversation as text data and identify important conversation content based on the text data. In this way, important information can be extracted by analyzing the voice data to identify important conversation content.

[0067] The analysis unit can analyze the image data to identify places visited and people met. The analysis unit can, for example, use image recognition technology to recognize specific places and people from the image data. The analysis unit can, for example, use GPS data to identify places visited. The analysis unit can, for example, use face recognition technology to identify people met. For example, the analysis unit can recognize faces included in the image data and identify the identity of the face. This allows for detailed recording by analyzing the image data to identify places visited and people met.

[0068] The classification unit can classify data into categories such as important conversations, places visited, people met, and things that need to be done. The classification unit can, for example, use task management technology to create a list of things that need to be done. The classification unit can, for example, classify important conversations based on keywords. The classification unit can, for example, classify places visited based on GPS data. The classification unit can, for example, classify people met based on facial recognition technology. Thus, by classifying data into categories, it becomes easier to organize information.

[0069] The generation unit can create a diary based on the analyzed data. The generation unit can automatically generate a diary, for example, using a template-based generation technique. The generation unit can generate diary sentences, for example, using a natural language generation technique. The generation unit can generate a diary that summarizes the events of the day, for example, based on the analyzed data. For example, the generation unit can create a diary that includes important conversations, places visited, people met, things that need to be done, etc. This allows the user to automatically create a diary by creating a diary based on the analyzed data.

[0070] Furthermore, the diary creation system includes a protection unit for privacy protection, encrypting data and restricting access, thereby enabling the diary creation system to safely protect user data.

[0071] The recording unit can estimate the user's emotions and adjust the start timing of recording based on the estimated user's emotions. For example, if the user is feeling stressed, the recording unit can cause the generation AI to pause recording of the conversation and resume it when the user is relaxed. For example, if the user is excited, the recording unit can cause the generation AI to immediately start recording the conversation so as not to miss important information. For example, if the user is relaxed, the recording unit can cause the generation AI to periodically record the conversation, maintaining a natural flow of conversation. This allows recording to start at a more appropriate time by adjusting the start timing of recording based on the user's emotions.

[0072] When recording, the recording unit can select the optimal recording method by referring to the user's past behavioral history. For example, if the user missed an important conversation in the past, the recording unit can have the generation AI increase the audio sensitivity for recording. For example, if the recording unit places importance on conversations at places the user has visited in the past, the generation AI can prioritize recording at those places. For example, if the user had an important conversation at a specific time period in the past, the recording unit can strengthen recording at that time period. This improves the accuracy of recording by selecting the optimal recording method by referring to the user's past behavioral history.

[0073] The recording unit can determine the priority of recording based on the user's current activity status when recording. For example, when the user is in a meeting, the recording unit can cause the generation AI to prioritize recording of conversations. For example, when the user is traveling, the recording unit can cause the generation AI to prioritize recording of places visited. For example, when the user is taking a break, the recording unit can cause the generation AI to prioritize recording of relaxed conversations. In this way, by determining the priority of recording based on the user's current activity status, important information can be recorded preferentially.

[0074] The recording unit can estimate the user's emotions and select the type of data to record based on the estimated user's emotions. For example, if the user is feeling stressed, the recording unit can prioritize recording conversations that the generation AI will use to relax. For example, if the user is excited, the recording unit can prioritize recording important conversation content. For example, if the user is relaxed, the recording unit can prioritize recording everyday conversations. This allows more appropriate data to be recorded by selecting the type of data to record based on the user's emotions.

[0075] When recording, the recording unit can prioritize recording highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific location, the recording unit can prioritize recording conversations at that location. For example, when the user is traveling, the recording unit can prioritize recording places the user has visited. For example, when the user is at home, the recording unit can prioritize recording everyday conversations. In this way, by prioritizing recording highly relevant data in consideration of the user's geographical location information, important information can be recorded without missing anything.

[0076] The recording unit can analyze the user's social media activities and record related data during recording. For example, if the user is having an important conversation on social media, the recording unit can prioritize recording that conversation. For example, if the user is sharing a place they visited on social media, the recording unit can prioritize recording that place. For example, if the user is tagging a person they met on social media, the recording unit can prioritize recording a conversation with that person. This allows more information to be collected by analyzing the user's social media activities and recording related data.

[0077] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can have the generation AI perform a detailed analysis and extract important information. For example, if the user is in a hurry, the analysis unit can have the generation AI perform a quick analysis and provide information that focuses on the main points. For example, if the user is excited, the analysis unit can have the generation AI perform an analysis that emphasizes emotional keywords. This allows for more accurate analysis results to be obtained by adjusting the accuracy of the analysis based on the user's emotions.

[0078] During analysis, the analysis unit can remove background sounds from the audio data to identify important conversation content. For example, the generation AI can filter background sounds during conversation and extract clear audio data. For example, the analysis unit can use noise canceling technology to identify important conversation content. For example, the generation AI can remove background sounds from the audio data and extract the main points of the conversation. As a result, by removing background sounds from the audio data, important conversation content can be more clearly identified.

[0079] During analysis, the analysis unit can remove noise from the image data to identify places visited and people met. For example, the generation AI in the analysis unit can filter noise from the image data and extract clear images. For example, the generation AI in the analysis unit can remove noise from the image data to identify places visited. For example, the generation AI in the analysis unit can remove noise from the image data to identify faces of people met. In this way, by removing noise from the image data, places visited and people met can be identified more accurately.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a display method that is simple and highly visible using the generation AI. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to see.

[0081] During analysis, the analysis unit can identify important conversation content by referring to the user's past conversation history. The analysis unit, for example, allows the generation AI to analyze the user's past conversation history and extract important keywords. The analysis unit, for example, allows the generation AI to identify important conversation content by referring to the past conversation history. The analysis unit, for example, allows the generation AI to extract important information based on the past conversation history. This allows important conversation content to be identified more accurately by referring to the user's past conversation history.

[0082] During analysis, the analysis unit can identify places visited by referring to the user's past visit history. The analysis unit can, for example, have the generation AI analyze the user's past visit history and identify places visited. The analysis unit can, for example, have the generation AI refer to the past visit history and identify places visited. The analysis unit can, for example, have the generation AI refer to the past visit history and identify places visited based on the past visit history. In this way, by referring to the user's past visit history, it is possible to more accurately identify places visited.

[0083] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated user emotions. For example, if the user is relaxed, the classification unit can cause the generation AI to apply detailed classification criteria. For example, if the user is in a hurry, the classification unit can cause the generation AI to apply simplified classification criteria. For example, if the user is excited, the classification unit can cause the generation AI to focus on emotional keywords when classifying. This allows for more appropriate classification by adjusting the classification criteria based on the user's emotions.

[0084] The classification unit can improve the accuracy of classification by taking into account the interrelationships of data during classification. For example, the generation AI can analyze the relationship between the content of conversations and the places visited, and perform highly accurate classification. For example, the generation AI can analyze the relationship between the content of conversations and the people met, and perform highly accurate classification. For example, the generation AI can analyze the relationship between the places visited and the people met, and perform highly accurate classification. In this way, the accuracy of classification is improved by taking into account the interrelationships of data.

[0085] The classification unit can determine the priority of classification based on the importance of data when classifying. For example, the classification unit can prioritize classification of important conversation content by the generation AI. For example, the classification unit can perform classification based on the importance of places visited by the generation AI. For example, the classification unit can perform classification based on the importance of people met by the generation AI. In this way, by determining the priority of classification based on the importance of data, important information can be prioritized.

[0086] The classification unit can estimate the user's emotions and adjust the display method of the classification results based on the estimated user emotions. For example, if the user is nervous, the classification unit can provide a display method that is simple and highly visible using the generation AI. For example, if the user is relaxed, the classification unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the classification unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the classification results based on the user's emotions, it is possible to provide a display that is easy for the user to see.

[0087] The classification unit can determine the priority of classification based on the time of data submission during classification. The classification unit can, for example, allow the generation AI to prioritize classifying the most recent data. The classification unit can, for example, allow the generation AI to refer to past data and prioritize classifying important information. The classification unit can, for example, allow the generation AI to determine the priority of classification based on the time of data submission. In this way, by determining the priority of classification based on the time of data submission, the most recent information can be prioritized.

[0088] The classification unit can adjust the order of classification based on the relevance of the data during classification. For example, the generation AI can adjust the order of classification based on the relevance between the content of the conversation and the places visited. For example, the generation AI can adjust the order of classification based on the relevance between the content of the conversation and the people met. For example, the generation AI can adjust the order of classification based on the relevance between the places visited and the people met. In this way, adjusting the order of classification based on the relevance of the data makes it easier to organize information.

[0089] The generation unit can estimate the user's emotions and adjust the way the diary is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can create a detailed diary. For example, if the user is in a hurry, the generation unit can create a concise diary. For example, if the user is excited, the generation AI can create a diary that includes emotional expressions. This allows a more appropriate diary to be created by adjusting the way the diary is expressed based on the user's emotions.

[0090] The generation unit can adjust the level of detail in the diary based on the importance of the data at the time of generation. For example, the generation unit can allow the generation AI to describe important conversation content in detail. For example, the generation unit can adjust the level of detail based on the importance of places visited by the generation AI. For example, the generation unit can adjust the level of detail based on the importance of people met by the generation AI. In this way, important information can be described in detail by adjusting the level of detail in the diary based on the importance of the data.

[0091] The generation unit can apply different generation algorithms depending on the data category at the time of generation. For example, the generation unit can create a dialogue-style diary based on the content of a conversation by the generation AI. For example, the generation unit can create a travelogue-style diary based on places visited by the generation AI. For example, the generation unit can create a person introduction-style diary based on people met by the generation AI. This makes it possible to create a more appropriate diary by applying different generation algorithms depending on the data category.

[0092] The generation unit can estimate the user's emotions and adjust the length of the diary entry based on the estimated user emotions. For example, if the user is relaxed, the generation AI can create a detailed, long diary entry. For example, if the user is in a hurry, the generation unit can create a concise, short diary entry. For example, if the user is excited, the generation AI can create a diary entry that includes emotional expressions. This allows the length of the diary entry to be adjusted based on the user's emotions, making it possible to create a diary entry of a more appropriate length.

[0093] At the time of generation, the generation unit can determine the priority of the diary based on the time of data submission. The generation unit, for example, can cause the generation AI to preferentially reflect the latest data in the diary. The generation unit, for example, can cause the generation AI to refer to past data and preferentially reflect important information in the diary. The generation unit, for example, can cause the generation AI to determine the priority of the diary based on the time of data submission. In this way, by determining the priority of the diary based on the time of data submission, the latest information can be preferentially reflected in the diary.

[0094] The generation unit can adjust the order of the diary entries based on the relevance of the data at the time of generation. For example, the generation AI can adjust the order of the diary entries based on the relevance between the content of the conversation and the places visited. For example, the generation AI can adjust the order of the diary entries based on the relevance between the content of the conversation and the people met. For example, the generation AI can adjust the order of the diary entries based on the relevance between the places visited and the people met. This makes it easier to organize information by adjusting the order of the diary entries based on the relevance of the data.

[0095] The protection unit can estimate the user's emotions and adjust the encryption level of the data based on the estimated user's emotions. For example, if the user is nervous, the protection unit can cause the generation AI to apply a high encryption level. For example, if the user is relaxed, the protection unit can cause the generation AI to apply a standard encryption level. For example, if the user is in a hurry, the protection unit can cause the generation AI to perform encryption quickly. This allows for more appropriate encryption by adjusting the encryption level of the data based on the user's emotions.

[0096] When protecting data, the protection unit can set optimal access restrictions by referring to the user's past access history. The protection unit can, for example, have the generation AI analyze the user's past access history and set optimal access restrictions. The protection unit can, for example, have the generation AI set strict access restrictions for important data based on the past access history. The protection unit can, for example, have the generation AI refer to the past access history and set appropriate access restrictions. In this way, data protection is strengthened by setting optimal access restrictions by referring to the user's past access history.

[0097] The protection unit can estimate the user's emotions and adjust data access restrictions based on the estimated user's emotions. For example, if the user is nervous, the protection unit can cause the generation AI to apply strict access restrictions. For example, if the user is relaxed, the protection unit can cause the generation AI to apply standard access restrictions. For example, if the user is in a hurry, the protection unit can cause the generation AI to quickly set access restrictions. This enables more appropriate access restrictions by adjusting data access restrictions based on the user's emotions.

[0098] When protecting data, the protection unit can select the optimal protection method by taking into account the user's device information. For example, the generation AI can analyze the user's device information and select the optimal encryption method. For example, the generation AI can select the appropriate protection method based on the device's security level. For example, the generation AI can select the optimal protection method by taking into account the device usage status. This strengthens data protection by selecting the optimal protection method by taking into account the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the recording unit, analysis unit, classification unit, generation unit, and protection unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the recording unit collects audio data and image data using the microphone 38B and camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the audio data and image data. For example, the classification unit categorizes the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit automatically generates a diary using the specific processing unit 290 of the data processing device 12. The protection unit is realized, for example, in at least one of the smart device 14 and the data processing device 12 and encrypts data and restricts access. === Hard Collateral 1-2 === Each of the multiple elements including the recording unit, analysis unit, classification unit, generation unit, and protection unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit collects audio data and image data using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the audio data and image data. For example, the classification unit categorizes the data analyzed by the specific processing unit 290 of the data processing device 12. For example, the generation unit automatically generates a diary using the specific processing unit 290 of the data processing device 12. For example, the protection unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12 and performs data encryption and access restrictions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned recording unit, analysis unit, classification unit, generation unit, and protection unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the recording unit collects voice data and image data using the microphone 238 and camera 42 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data and image data. For example, the classification unit classifies the data analyzed by the specific processing unit 290 of the data processing device 12 into categories. For example, the generation unit automatically generates a diary by the specific processing unit 290 of the data processing device 12. The protection unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12, and encrypts data and restricts access. === Hard Collateral 1-4 === Each of the multiple elements including the recording unit, analysis unit, classification unit, generation unit, and protection unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the recording unit collects audio data and image data using the microphone 238 and camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the audio data and image data. For example, the classification unit classifies the data analyzed by the specific processing unit 290 of the data processing device 12 into categories. For example, the generation unit automatically generates a diary using the specific processing unit 290 of the data processing device 12. The protection unit is realized, for example, in at least one of the robot 414 and the data processing device 12, and encrypts data and restricts access to it.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The diary creation system may further include a health management unit that acquires the user's health data and reflects it in the diary. For example, the health management unit may acquire data such as the user's heart rate, number of steps, and sleep time, and add records of the user's health status to the diary. This allows the user to check their daily health status through the diary. The health management unit may also analyze the user's health data and include health advice in the diary. For example, if the user is not getting enough exercise, a message encouraging them to exercise may be added to the diary. Furthermore, the health management unit may adjust the contents of the diary based on the user's health data. For example, if the user is tired, advice to relax may be included in the diary.

[0101] The diary creation system may further include a hobby management unit that customizes the contents of the diary based on the user's hobbies and interests. For example, the hobby management unit may obtain information about the user's favorite movies, music, sports, etc., and add content related to the diary. This allows the user to enjoy a diary that is tailored to their hobbies and interests. The hobby management unit may also include events and news related to the user's hobbies in the diary. For example, information about the release of a new song by a favorite band may be added to the diary. Furthermore, the hobby management unit may customize the layout and design of the diary based on the user's hobbies. For example, a sports-themed design may be applied to a user who likes sports.

[0102] The diary creation system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the content of the diary based on the estimated emotion. For example, if the user feels sad, the emotion adjustment unit may add an encouraging message to the diary, allowing the user to receive emotional support through the diary. The emotion adjustment unit may also include a congratulatory message in the diary if the user is happy. For example, a congratulatory message for a successful project may be added to the diary. The emotion adjustment unit may also adjust the tone and style of the diary based on the user's emotion. For example, if the user is relaxed, the emotion adjustment unit may create a diary with a calm tone.

[0103] The diary creation system may further include a social media integration unit that analyzes the user's social media activities and reflects them in the diary. For example, the social media integration unit may obtain photos and comments posted by the user and add them to the diary. This allows the user to summarize their social media activities in the diary. The social media integration unit may also include the user's interactions with friends and followers in the diary. For example, conversations with friends and event participation records may be added to the diary. Furthermore, the social media integration unit may also reflect the user's social media trends and topics in the diary. For example, popular hashtags and topics may be included in the diary.

[0104] The diary creation system may further include a layout adjustment unit that estimates the user's emotions and adjusts the layout of the diary based on the estimated emotions. For example, if the user is feeling stressed, the layout adjustment unit may apply a simple and calm layout, allowing the user to read the diary in a relaxed mood. Alternatively, if the user is feeling excited, the layout adjustment unit may apply a colorful and lively layout. For example, a design that reflects a festive atmosphere may be applied to the diary. Furthermore, the layout adjustment unit may adjust fonts and colors based on the user's emotions. For example, if the user is feeling relaxed, soft colors and easy-to-read fonts may be used.

[0105] The diary creation system may further include a behavior history analysis unit that analyzes the user's past behavior history and personalizes the content of the diary. For example, the behavior history analysis unit can add related information to the diary based on places the user has visited or events the user has participated in in the past, allowing the user to look back on past events. The behavior history analysis unit can also analyze the user's past behavior patterns and include future plans and goals in the diary. For example, regular activities and progress toward achieving goals can be added to the diary. Furthermore, the behavior history analysis unit can customize the content of the diary based on the user's past behavior. For example, information related to specific seasons or events can be included in the diary.

[0106] The diary creation system may further include a feedback unit that estimates the user's emotions and provides feedback to the diary content based on the estimated emotions. For example, if the user is feeling anxious, the feedback unit may add advice to relax to the diary. This allows the user to receive emotional support through the diary. The feedback unit may also include positive feedback in the diary if the user is happy. For example, a message of praise for an achieved goal may be added to the diary. The feedback unit may also adjust the diary content based on the user's emotions. For example, if the user is feeling stressed, relaxation tips or activities may be included in the diary.

[0107] The diary creation system can further include a learning management unit that analyzes the user's learning history and reflects the learning content in the diary. For example, the learning management unit can add the user's learned content and progress to the diary. This allows the user to check the results of their learning through the diary. The learning management unit can also include the next learning content and reference materials in the diary based on the user's learning history. For example, it can add unstudied topics and related reference books to the diary. Furthermore, the learning management unit can customize the content of the diary based on the user's learning style. For example, a user who prefers visual learning can create a diary that makes extensive use of diagrams and graphs.

[0108] The diary creation system may further include a prediction unit that estimates the user's emotions and predicts the contents of the diary based on the estimated emotions. For example, if the user is feeling stressed, the prediction unit may suggest activities to reduce stress in the diary. This allows the user to receive emotional support through the diary. The prediction unit may also include relaxation methods in the diary if the user is feeling excited. For example, deep breathing and meditation techniques may be added to the diary. Furthermore, the prediction unit may predict future events and plans based on the user's emotions and reflect them in the diary. For example, it may suggest measures to take when stress levels are high.

[0109] The diary creation system may further include a travel management unit that analyzes the user's travel history and reflects the travel notes in the diary. For example, the travel management unit may add the places the user has visited and the activities they have experienced to the diary. This allows the user to look back on their travel memories through the diary. The travel management unit may also include next places to visit and recommended activities in the diary based on the user's travel history. For example, unvisited tourist spots and popular restaurants may be added to the diary. Furthermore, the travel management unit may customize the contents of the diary based on the user's travel style. For example, an adventurous user may be able to create a diary that includes many active activities.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The recording unit records the conversation and the image. The recording unit can, for example, simultaneously record audio data and image data. For example, the recording unit collects audio data using a microphone and collects image data using a camera. For example, the recording unit saves the audio data in MP3 or WAV format and the image data in JPEG or PNG format. Step 2: The analysis unit analyzes the data recorded by the recording unit. The analysis unit, for example, analyzes the audio data to identify important conversation content. The analysis unit can, for example, use keyword extraction technology to extract important keywords and phrases in the conversation. The analysis unit can also, for example, use emotion analysis technology to analyze the emotional nuances of the conversation. The analysis unit, for example, analyzes image data to identify places visited and people met. The analysis unit can, for example, use image recognition technology to recognize specific places and people from the image data. Step 3: The classifier categorizes the data analyzed by the analyzer. For example, the classifier can categorize the data into categories such as important conversations, places visited, people met, and things to do. For example, the classifier can use task management techniques to create a list of things to do. Step 4: The generation unit creates a diary based on the data classified by the classification unit. The generation unit can automatically generate the diary using, for example, a template-based generation technique. The generation unit can generate diary sentences using, for example, a natural language generation technique.

[0112] 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.

[0113] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0159] In the 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.

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

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

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 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 recording unit for recording conversations and images; an analysis unit that analyzes the data recorded by the recording unit; a classification unit that classifies the data analyzed by the analysis unit into categories; a generation unit that creates a diary based on the data classified by the classification unit; Equipped with A system characterized by:

2. The recording unit Records audio and video data simultaneously 2. The system of claim 1.

3. The analysis unit Analyze audio data to identify important conversations 2. The system of claim 1.

4. The analysis unit Analyzing image data to identify places visited and people met 2. The system of claim 1.

5. The classification unit Categorize your data into categories of important conversations, places you've visited, people you've met, and things you need to do.

2. The system of claim 1.

6. The generation unit Create a diary based on the analyzed data 2. The system of claim 1.

7. Equipped with a protection section for privacy protection, Encrypting data and restricting access 2. The system of claim 1.

8. The recording unit Estimate the user's emotion and adjust the start timing of recording based on the estimated user emotion.

2. The system of claim 1.

9. The recording unit When recording, the optimal recording method is selected by referring to the user's past behavior history.

2. The system of claim 1.

Images