System

Abstract

An object of a system according to an embodiment is to enable a user to easily generate a personal history.SOLUTION: A system includes an input collection unit, an analysis unit, a generation unit, and a storage unit. The input collection unit allows the user to answer a question or post a photograph. The analysis unit analyzes the text answer and the photograph collected by the input collection unit. The generation unit generates a personal history on the basis of the content analyzed by the analysis unit. The storage unit stores the personal history generated by the generation unit and the posted photo.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it takes a lot of time and effort for users to manually create their personal histories.

[0005] The system according to the embodiment aims to enable a user to easily create his or her personal history. [Means for solving the problem]

[0006] The system according to the embodiment includes an input collection unit, an analysis unit, a generation unit, and a storage unit. The input collection unit allows users to answer questions and post photos. The analysis unit analyzes the text answers and photos collected by the input collection unit. The generation unit generates a personal history based on the content analyzed by the analysis unit. The storage unit stores the personal history generated by the generation unit and the posted photos. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily create his / her personal history. [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) The automatic autobiography generation system according to an embodiment of the present invention is a system that automatically generates and saves an autobiography by having a user answer questions and post photos. This allows the automatic autobiography generation system to easily create and save an autobiography.

[0029] An automatic autobiography generation system according to an embodiment includes a user input collection unit, an analysis unit, a generation unit, and a storage unit. The user input collection unit allows a user to answer questions and submit photos. For example, a user may provide a text response to a question such as, "Tell us about your childhood memories." The user may also upload childhood photos from their photo albums. The analysis unit analyzes the text responses and photos collected by the user input collection unit. For example, the analysis unit may analyze the content of the photos using an image analysis algorithm. The generation unit may also analyze the content of the user's responses using text analysis technology. The generation unit generates the autobiography based on the content analyzed by the analysis unit. For example, the generation AI may organize the user's responses in chronological order and place the photos in appropriate locations. The generation AI may also generate the autobiography based on prompts containing instructions on what the user wants the generation AI to do. The storage unit stores the autobiography generated by the generation unit and the posted photos. For example, the storage unit may store the generated autobiography and photos in cloud storage. The storage unit may also store the generated autobiography and photos in a database. This allows the automatic autobiography generation system according to an embodiment to easily create and save a personal history.

[0030] The user input collection unit generates related questions based on the user's input, thereby eliciting the user's memories. In the user input collection unit, for example, the generation AI automatically generates related questions based on the content entered by the user. For example, in response to the question "Tell me about your childhood memories," the generation AI generates additional questions such as "Who were your friends at the time?" The generation AI also generates related questions based on photos posted by the user. For example, if a user posts a family photo, the generation AI generates questions such as "Where was this photo taken?" The generation AI also analyzes the user's input and generates probing questions to elicit memories. For example, it generates a question such as "What was the most memorable thing about that event?" This generates related questions to elicit the user's memories, making it possible to create a more detailed autobiography.

[0031] The analysis unit can analyze the user's input in real time and generate feedback and suggestions based on the input. For example, the analysis unit analyzes the text entered by the user in real time, and the generation AI provides appropriate feedback. For example, it displays positive feedback such as, "That event is wonderful!". It also analyzes photos posted by the user in real time, and the generation AI makes related suggestions. For example, it displays a suggestion such as, "Please tell us an episode related to this photo." The generation AI also provides advice in real time based on the user's input. For example, it displays advice such as, "It would be good to add a more specific episode." This makes it possible to support user input by providing feedback and suggestions based on the user's input in real time.

[0032] The user input collection unit allows users to input voice and post videos, and the generation AI can analyze this multimedia data and reflect it in the autobiography. The user input collection unit, for example, allows users to input voice, and the generation AI analyzes the voice data and converts it into text. For example, what the user says is automatically transcribed and reflected in the autobiography. The unit also allows users to post videos, and the generation AI analyzes the video data and extracts important scenes. For example, episodes discussed in the video are converted into text and incorporated into the autobiography. The generation AI also analyzes the data of users who input voice or post videos, and automatically generates related questions. For example, it generates questions such as "Please tell me more about this scene" based on the content of the video. This allows for the incorporation of a wider variety of data by analyzing voice input and video posts and reflecting them in the autobiography.

[0033] The generation unit can organize the user's input content not only chronologically but also by theme or emotion, generating a personal history from multiple perspectives. For example, the generation AI not only organizes the user's input content chronologically, but also classifies it by theme. For example, a personal history is generated for each theme, such as "family," "friends," or "work." The generation unit also organizes the user's input content by emotion, allowing the generation AI to generate a personal history from multiple perspectives. For example, episodes are classified by emotion, such as "joy," "sadness," or "surprise." The generation AI also analyzes the user's input content and organizes it chronologically, by theme, or by emotion. For example, episodes related to a specific theme or emotion are displayed together. This allows the generation of a personal history from multiple perspectives by organizing it not only chronologically but also by theme or emotion.

[0034] The generation unit can automatically add relevant historical background and events based on the user's input, generating a richer autobiography. For example, the generation AI automatically adds relevant historical background based on the user's input. For example, if a user inputs "I was born in the 1960s," social events from that era are incorporated into the autobiography. The generation AI also analyzes the user's input and automatically adds relevant events. For example, if a user inputs "I was in a band in high school," the music scene of that era is reflected in the autobiography. The generation AI also adds relevant historical background and events based on the user's input. For example, if a user inputs "I studied abroad in college," the international situation of that era is incorporated into the autobiography. This allows for the generation of a richer autobiography by adding relevant historical background and events.

[0035] The storage unit stores the generated autobiography and posted photos in cloud storage, preventing data loss. The storage unit stores, for example, the generated autobiography and posted photos in cloud storage. For example, the data is stored using cloud storage such as AWS, Google Cloud, or Azure. The storage unit can also store data in a database. For example, the data is stored using a database such as MySQL or PostgreSQL. In this way, data loss can be prevented by storing the data in cloud storage.

[0036] The storage unit can encrypt user data and strengthen security. For example, the generation AI automatically encrypts user data to strengthen security in the storage unit. For example, photos and text data posted by users are encrypted and stored in cloud storage. The generation AI also analyzes the user's data and selects an appropriate encryption algorithm to protect the data. For example, strong encryption is applied to highly important data. The generation AI also builds a system that encrypts user data and strengthens security. For example, data is encrypted when it is sent and received to prevent unauthorized access by third parties. In this way, security can be strengthened by encrypting the data.

[0037] The storage unit can link user data with other cloud services to achieve centralized data management. For example, the generation AI in the storage unit links user data with other cloud services to achieve centralized management. For example, it links with Google Drive and Dropbox to manage data in an integrated manner. The generation AI also analyzes the user data, and manages the data in collaboration with the appropriate cloud service. For example, photo data is stored in Google Photos and document data in OneDrive. The generation AI also links user data with other cloud services to build a system that achieves centralized management. For example, it manages data that is distributed and stored across multiple cloud services in an integrated manner. This achieves centralized data management, making it easier for users to manage their data.

[0038] The generation unit can analyze the user's usage status and propose the optimal paid plan. For example, the generation AI analyzes the user's usage status and proposes the optimal paid plan. For example, it proposes a plan with appropriate storage capacity and functions based on the user's data usage and access frequency. The generation AI also analyzes the user's usage patterns and proposes the optimal paid plan. For example, it proposes a plan with additional storage capacity for users who frequently post photos. The generation AI also analyzes the user's usage status in real time and dynamically proposes the optimal paid plan. For example, it automatically adjusts the plan according to changes in data usage. This makes it possible to increase user satisfaction by proposing the optimal paid plan based on the user's usage status.

[0039] The generation unit can display personalized advertisements based on user data and generate revenue. In the generation unit, for example, the generation AI analyzes user data and displays personalized advertisements. For example, advertisements for related products are displayed based on the content of photos and text posted by the user. The generation AI also displays personalized advertisements based on user data and generates revenue. For example, advertisements for events and services that the user may be interested in are displayed. Furthermore, a system is constructed in which the generation AI analyzes user data and displays personalized advertisements. For example, advertisements are dynamically adjusted based on the user's interests. This allows revenue to be generated by displaying personalized advertisements.

[0040] The generation unit can suggest additional services that the user may be interested in based on the user's data. For example, the generation AI analyzes the user's data and suggests additional services that the user may be interested in. For example, if the user frequently posts photos, a photo editing service may be suggested. Also, based on the user's data, the generation AI suggests additional services that the user may be interested in. For example, if the user posts many travel memories, a travel album creation service may be suggested. Also, a system is constructed in which the generation AI analyzes the user's data and suggests additional services that the user may be interested in. For example, services may be dynamically suggested based on the user's interests. This makes it possible to increase user satisfaction by suggesting additional services that the user may be interested in.

[0041] The generation unit suggests related products and services based on user data, enabling affiliate revenue to be earned. For example, the generation AI analyzes user data and suggests related products and services. For example, it suggests cameras and accessories related to photos posted by the user. Also, based on user data, the generation AI suggests related products and services and earns affiliate revenue. For example, if a user posts travel memories, it suggests travel-related products and services. Also, a system is built in which the generation AI analyzes user data and suggests related products and services. For example, it dynamically suggests products and services based on the user's interests. This allows affiliate revenue to be earned by suggesting related products and services.

[0042] The generation unit can increase revenue by suggesting personalized gifts and surprise events based on user data. In the generation unit, for example, the generation AI analyzes user data and suggests personalized gifts. For example, it suggests special gifts based on memories posted by the user. Also, the generation AI suggests personalized surprise events based on user data. For example, it suggests events that coincide with the user's birthday or anniversary. Also, the generation AI analyzes user data and builds a system that suggests personalized gifts and surprise events. For example, it dynamically suggests gifts and events based on the user's interests. In this way, it is possible to increase revenue by suggesting personalized gifts and surprise events.

[0043] The user input collection unit can analyze the user's operation history and automatically customize the optimal interface. In the user input collection unit, for example, the generation AI analyzes the user's operation history and automatically customizes the optimal interface. For example, it prioritizes displaying functions that the user uses frequently. In addition, the user's operation patterns are analyzed and the generation AI proposes the optimal interface. For example, it customizes and displays menus that the user uses frequently. In addition, the generation AI analyzes the user's operation history in real time and dynamically customizes the optimal interface. For example, it automatically adjusts the interface according to the user's operations. In this way, operability can be improved by customizing the interface based on the user's operation history.

[0044] The user input collection unit can analyze the user's input content in real time and provide input assistance and automatic correction. In the user input collection unit, for example, a generation AI analyzes the user's input content in real time and provides input assistance. For example, it presents appropriate word candidates for the text entered by the user. The user's input content is also analyzed and the generation AI performs automatic correction. For example, typos and omissions are automatically corrected and the text is converted to correct text. Furthermore, a system is constructed in which the generation AI analyzes the user's input content in real time and provides input assistance and automatic correction. For example, grammatical errors are automatically corrected and the text is converted to easy-to-read text. In this way, the user's input content can be analyzed in real time and input assistance and automatic correction can be provided, thereby improving the accuracy and efficiency of input.

[0045] The user input collection unit can provide feedback on user operations in real time, improving operability. In the user input collection unit, for example, the generation AI provides feedback on user operations in real time. For example, when the user performs an operation, appropriate advice or hints are immediately displayed. The generation AI also analyzes the user's operations and provides feedback in real time. For example, it detects operation errors and suggests the correct operation method. Furthermore, a system can be constructed in which the generation AI provides feedback on user operations in real time, improving operability. For example, it suggests the optimal operation method based on the user's operation history. In this way, operability can be improved by providing feedback on user operations in real time.

[0046] The user input collection unit can provide optimal operation guides and tutorials based on the user's operation history. In the user input collection unit, for example, the generation AI analyzes the user's operation history and provides the optimal operation guide. For example, it displays a guide for functions that the user uses frequently. The generation AI also provides the optimal tutorial based on the user's operation history. For example, it displays a tutorial for functions that the user is using for the first time. The generation AI also analyzes the user's operation history and builds a system that provides optimal operation guides and tutorials. For example, it displays a customized guide according to the user's operation pattern. This allows the user to deepen their understanding of operations by providing the optimal operation guides and tutorials based on the user's operation history.

[0047] The user input collection unit can automatically change the interface layout in response to user operations, improving operability. In the user input collection unit, for example, a generation AI automatically changes the interface layout in response to user operations. For example, functions frequently used by the user are placed in prominent positions. The generation AI also analyzes the user's operation patterns and dynamically adjusts the interface layout. For example, an optimal layout is proposed based on the user's operation history. Furthermore, a system can be constructed in which the generation AI automatically changes the interface layout in response to user operations, improving operability. For example, the menu arrangement is dynamically adjusted in accordance with the user's operations. In this way, operability can be improved by automatically changing the interface layout in response to user operations.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The user input collection unit generates related questions based on the user's input, thereby eliciting the user's memories. For example, the generation AI automatically generates related questions based on the content entered by the user. For example, in response to the question, "Tell me about your childhood memories," it generates additional questions such as, "Who were your friends at the time?" The generation AI also generates related questions based on photos posted by the user. For example, if a user posts a family photo, it generates a question such as, "Where was this photo taken?" The generation AI also analyzes the user's input and generates in-depth questions to elicit memories. For example, it generates a question such as, "What was the most memorable thing about that event?" This allows the user to create a more detailed autobiography by generating related questions to elicit memories.

[0050] The analysis unit can analyze the user's input in real time and generate feedback and suggestions based on the input. For example, the generation AI can analyze the text entered by the user in real time and provide appropriate feedback. For example, it can display positive feedback such as, "That was a wonderful event!". The generation AI can also analyze photos posted by the user in real time and make related suggestions. For example, it can display a suggestion such as, "Please tell us an episode related to this photo." The generation AI can also provide advice in real time based on the user's input. For example, it can display advice such as, "It would be good to add a more specific episode." This makes it possible to support user input by providing feedback and suggestions based on the user's input in real time.

[0051] The user input collection unit allows for voice input and video posting, and the generation AI can analyze this multimedia data and reflect it in the autobiography. For example, the user can input voice, and the generation AI analyzes the voice data and converts it into text. For example, what the user says can be automatically transcribed and reflected in the autobiography. The unit also allows users to post videos, and the generation AI analyzes the video data to extract important scenes. For example, episodes discussed in the video can be converted into text and incorporated into the autobiography. The generation AI also analyzes the data of users who input voice or posted videos, and automatically generates related questions. For example, it generates questions such as "Please tell me more about this scene" based on the content of the video. This allows for the incorporation of a wider variety of data by analyzing voice input and video posting and reflecting it in the autobiography.

[0052] The generation unit can organize the user's input content not only chronologically but also by theme or emotion, generating a personal history from multiple perspectives. For example, the generation AI not only organizes the user's input content chronologically, but also classifies it by theme. For example, it can generate a personal history for each theme, such as "family," "friends," or "work." The generation AI can also organize the user's input content by emotion, generating a personal history from multiple perspectives. For example, it can classify episodes by emotion, such as "joy," "sadness," or "surprise." The generation AI can also analyze the user's input content and organize it chronologically, by theme, or by emotion. For example, it can display episodes related to a specific theme or emotion together. This allows it to generate a personal history from multiple perspectives by organizing it not only chronologically but also by theme or emotion.

[0053] The generation unit can automatically add relevant historical background and events based on the user's input, generating a richer autobiography. For example, the generation AI automatically adds relevant historical background based on the user's input. For example, if a user inputs "I was born in the 1960s," social events from that era will be incorporated into the autobiography. The generation AI also analyzes the user's input and automatically adds relevant events. For example, if a user inputs "I was in a band in high school," the music scene of that era will be reflected in the autobiography. The generation AI also adds relevant historical background and events based on the user's input. For example, if a user inputs "I studied abroad in college," the international situation of that era will be incorporated into the autobiography. This allows for the generation of a richer autobiography by adding relevant historical background and events.

[0054] The storage unit can store the generated autobiography and posted photos in cloud storage to prevent data loss. For example, the generated autobiography and posted photos can be stored in cloud storage. For example, data can be stored using cloud storage such as AWS, Google Cloud, or Azure. The storage unit can also store data in a database. For example, data can be stored using a database such as MySQL or PostgreSQL. In this way, data loss can be prevented by storing it in cloud storage.

[0055] The storage unit can encrypt user data and strengthen security. For example, the generation AI can automatically encrypt user data to strengthen security. For example, photos and text data posted by users can be encrypted and stored in cloud storage. The generation AI can also analyze user data and select an appropriate encryption algorithm to protect the data. For example, strong encryption can be applied to highly important data. The generation AI can also build a system that encrypts user data and strengthens security. For example, data can be encrypted when sent and received to prevent unauthorized access by third parties. In this way, security can be strengthened by encrypting data.

[0056] The storage unit can link user data with other cloud services to achieve centralized data management. For example, the generation AI links user data with other cloud services to achieve centralized management. For example, it links with Google Drive and Dropbox to manage data in an integrated manner. The generation AI also analyzes user data, and links it with the appropriate cloud service to manage the data. For example, photo data is stored in Google Photos and document data in OneDrive. The generation AI also links user data with other cloud services to build a system that achieves centralized management. For example, it manages data that is distributed and stored across multiple cloud services in an integrated manner. This allows for centralized data management, making it easier for users to manage their data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: In the user input collection section, users answer questions and post photos. For example, users can respond to questions such as "Tell us about your childhood memories" by text. Users can also upload childhood photos from their own photo albums. Step 2: The analysis unit analyzes the text responses and photos collected by the user input collection unit. For example, the analysis unit analyzes the content of the photos using an image analysis algorithm. It also analyzes the content of the user responses using text analysis techniques. Step 3: The generator generates the autobiography based on the content analyzed by the analyzer. For example, the generator organizes the user's answers in chronological order and places photos in appropriate positions. The generator also generates the autobiography based on prompts containing instructions on what the user wants the generator to do. Step 4: The storage unit stores the generated autobiography and posted photos. For example, the storage unit stores the generated autobiography and photos in cloud storage. It can also store them in a database.

[0059] (Example 2) The automatic autobiography generation system according to an embodiment of the present invention is a system that automatically generates and saves an autobiography by having a user answer questions and post photos. This allows the automatic autobiography generation system to easily create and save an autobiography.

[0060] An automatic autobiography generation system according to an embodiment includes a user input collection unit, an analysis unit, a generation unit, and a storage unit. The user input collection unit allows a user to answer questions and submit photos. For example, a user may provide a text response to a question such as, "Tell us about your childhood memories." The user may also upload childhood photos from their photo albums. The analysis unit analyzes the text responses and photos collected by the user input collection unit. For example, the analysis unit may analyze the content of the photos using an image analysis algorithm. The generation unit may also analyze the content of the user's responses using text analysis technology. The generation unit generates the autobiography based on the content analyzed by the analysis unit. For example, the generation AI may organize the user's responses in chronological order and place the photos in appropriate locations. The generation AI may also generate the autobiography based on prompts containing instructions on what the user wants the generation AI to do. The storage unit stores the autobiography generated by the generation unit and the posted photos. For example, the storage unit may store the generated autobiography and photos in cloud storage. The storage unit may also store the generated autobiography and photos in a database. This allows the automatic autobiography generation system according to an embodiment to easily create and save a personal history.

[0061] The user input collection unit generates related questions based on the user's input, thereby eliciting the user's memories. In the user input collection unit, for example, the generation AI automatically generates related questions based on the content entered by the user. For example, in response to the question "Tell me about your childhood memories," the generation AI generates additional questions such as "Who were your friends at the time?" The generation AI also generates related questions based on photos posted by the user. For example, if a user posts a family photo, the generation AI generates questions such as "Where was this photo taken?" The generation AI also analyzes the user's input and generates probing questions to elicit memories. For example, it generates a question such as "What was the most memorable thing about that event?" This generates related questions to elicit the user's memories, making it possible to create a more detailed autobiography.

[0062] The analysis unit can analyze the user's input in real time and generate feedback and suggestions based on the input. For example, the analysis unit analyzes the text entered by the user in real time, and the generation AI provides appropriate feedback. For example, it displays positive feedback such as, "That event is wonderful!". It also analyzes photos posted by the user in real time, and the generation AI makes related suggestions. For example, it displays a suggestion such as, "Please tell us an episode related to this photo." The generation AI also provides advice in real time based on the user's input. For example, it displays advice such as, "It would be good to add a more specific episode." This makes it possible to support user input by providing feedback and suggestions based on the user's input in real time.

[0063] The user input collection unit allows users to input voice and post videos, and the generation AI can analyze this multimedia data and reflect it in the autobiography. The user input collection unit, for example, allows users to input voice, and the generation AI analyzes the voice data and converts it into text. For example, what the user says is automatically transcribed and reflected in the autobiography. The unit also allows users to post videos, and the generation AI analyzes the video data and extracts important scenes. For example, episodes discussed in the video are converted into text and incorporated into the autobiography. The generation AI also analyzes the data of users who input voice or post videos, and automatically generates related questions. For example, it generates questions such as "Please tell me more about this scene" based on the content of the video. This allows for the incorporation of a wider variety of data by analyzing voice input and video posts and reflecting them in the autobiography.

[0064] The analysis unit can extract emotional highlights based on the user's input and reflect them in the personal history. For example, the generation AI analyzes the user's input and extracts emotional highlights. For example, it automatically picks out episodes that particularly moved the user and reflects them in the personal history. The generation AI also extracts emotionally significant parts from the user's input and incorporates them into the personal history. For example, it highlights moments that made the user cry or episodes that made the user laugh out loud. The generation AI also performs emotion analysis and extracts emotional highlights from the user's input. For example, it reflects events that the user describes as "unforgettable" in the personal history. In this way, by extracting emotional highlights and reflecting them in the personal history, it is possible to generate a personal history that is more emotionally relatable.

[0065] The generation unit can organize the user's input content not only chronologically but also by theme or emotion, generating a personal history from multiple perspectives. For example, the generation AI not only organizes the user's input content chronologically, but also classifies it by theme. For example, a personal history is generated for each theme, such as "family," "friends," or "work." The generation unit also organizes the user's input content by emotion, allowing the generation AI to generate a personal history from multiple perspectives. For example, episodes are classified by emotion, such as "joy," "sadness," or "surprise." The generation AI also analyzes the user's input content and organizes it chronologically, by theme, or by emotion. For example, episodes related to a specific theme or emotion are displayed together. This allows the generation of a personal history from multiple perspectives by organizing it not only chronologically but also by theme or emotion.

[0066] The generation unit can automatically add relevant historical background and events based on the user's input, generating a richer autobiography. For example, the generation AI automatically adds relevant historical background based on the user's input. For example, if a user inputs "I was born in the 1960s," social events from that era are incorporated into the autobiography. The generation AI also analyzes the user's input and automatically adds relevant events. For example, if a user inputs "I was in a band in high school," the music scene of that era is reflected in the autobiography. The generation AI also adds relevant historical background and events based on the user's input. For example, if a user inputs "I studied abroad in college," the international situation of that era is incorporated into the autobiography. This allows for the generation of a richer autobiography by adding relevant historical background and events.

[0067] The storage unit stores the generated autobiography and posted photos in cloud storage, preventing data loss. The storage unit stores, for example, the generated autobiography and posted photos in cloud storage. For example, the data is stored using cloud storage such as AWS, Google Cloud, or Azure. The storage unit can also store data in a database. For example, the data is stored using a database such as MySQL or PostgreSQL. In this way, data loss can be prevented by storing the data in cloud storage.

[0068] The storage unit can encrypt user data and strengthen security. For example, the generation AI automatically encrypts user data to strengthen security in the storage unit. For example, photos and text data posted by users are encrypted and stored in cloud storage. The generation AI also analyzes the user's data and selects an appropriate encryption algorithm to protect the data. For example, strong encryption is applied to highly important data. The generation AI also builds a system that encrypts user data and strengthens security. For example, data is encrypted when it is sent and received to prevent unauthorized access by third parties. In this way, security can be strengthened by encrypting the data.

[0069] The storage unit can link user data with other cloud services to achieve centralized data management. For example, the generation AI in the storage unit links user data with other cloud services to achieve centralized management. For example, it links with Google Drive and Dropbox to manage data in an integrated manner. The generation AI also analyzes the user data, and manages the data in collaboration with the appropriate cloud service. For example, photo data is stored in Google Photos and document data in OneDrive. The generation AI also links user data with other cloud services to build a system that achieves centralized management. For example, it manages data that is distributed and stored across multiple cloud services in an integrated manner. This achieves centralized data management, making it easier for users to manage their data.

[0070] The storage unit can use the emotion estimation function to identify data that the user considers particularly important and prioritize protecting that data. For example, the storage unit can use the emotion estimation function to identify data that the user considers particularly important, and the generation AI can prioritize protecting that data. For example, text data containing episodes that moved the user can be prioritized as backups. The generation AI can also analyze the user's emotions to identify and protect data that the user considers particularly important. For example, photos in which the user is smiling can be prioritized as encrypted and saved. The emotion estimation function can also be used to identify data that the user considers particularly important, and a system can be constructed in which the generation AI prioritizes protecting that data. For example, data that the user frequently accesses can be prioritized as backups. This can increase data security by prioritizing the protection of data that the user considers particularly important.

[0071] The generation unit can analyze the user's usage status and propose the optimal paid plan. For example, the generation AI analyzes the user's usage status and proposes the optimal paid plan. For example, it proposes a plan with appropriate storage capacity and functions based on the user's data usage and access frequency. The generation AI also analyzes the user's usage patterns and proposes the optimal paid plan. For example, it proposes a plan with additional storage capacity for users who frequently post photos. The generation AI also analyzes the user's usage status in real time and dynamically proposes the optimal paid plan. For example, it automatically adjusts the plan according to changes in data usage. This makes it possible to increase user satisfaction by proposing the optimal paid plan based on the user's usage status.

[0072] The generation unit can display personalized advertisements based on user data and generate revenue. In the generation unit, for example, the generation AI analyzes user data and displays personalized advertisements. For example, advertisements for related products are displayed based on the content of photos and text posted by the user. The generation AI also displays personalized advertisements based on user data and generates revenue. For example, advertisements for events and services that the user may be interested in are displayed. Furthermore, a system is constructed in which the generation AI analyzes user data and displays personalized advertisements. For example, advertisements are dynamically adjusted based on the user's interests. This allows revenue to be generated by displaying personalized advertisements.

[0073] The generation unit can suggest additional services that the user may be interested in based on the user's data. For example, the generation AI analyzes the user's data and suggests additional services that the user may be interested in. For example, if the user frequently posts photos, a photo editing service may be suggested. Also, based on the user's data, the generation AI suggests additional services that the user may be interested in. For example, if the user posts many travel memories, a travel album creation service may be suggested. Also, a system is constructed in which the generation AI analyzes the user's data and suggests additional services that the user may be interested in. For example, services may be dynamically suggested based on the user's interests. This makes it possible to increase user satisfaction by suggesting additional services that the user may be interested in.

[0074] The generation unit suggests related products and services based on user data, enabling affiliate revenue to be earned. For example, the generation AI analyzes user data and suggests related products and services. For example, it suggests cameras and accessories related to photos posted by the user. Also, based on user data, the generation AI suggests related products and services and earns affiliate revenue. For example, if a user posts travel memories, it suggests travel-related products and services. Also, a system is built in which the generation AI analyzes user data and suggests related products and services. For example, it dynamically suggests products and services based on the user's interests. This allows affiliate revenue to be earned by suggesting related products and services.

[0075] The generation unit can increase revenue by suggesting personalized gifts and surprise events based on user data. In the generation unit, for example, the generation AI analyzes user data and suggests personalized gifts. For example, it suggests special gifts based on memories posted by the user. Also, the generation AI suggests personalized surprise events based on user data. For example, it suggests events that coincide with the user's birthday or anniversary. Also, the generation AI analyzes user data and builds a system that suggests personalized gifts and surprise events. For example, it dynamically suggests gifts and events based on the user's interests. In this way, it is possible to increase revenue by suggesting personalized gifts and surprise events.

[0076] The generation unit can use the emotion estimation function to identify moments that particularly moved the user and sell those moments as souvenirs. The generation unit, for example, uses the emotion estimation function to identify moments that particularly moved the user and sell those moments as souvenirs. For example, a photo book is created based on episodes that moved the user. The generation AI also analyzes the user's emotions, and identifies moments that particularly moved the user and suggests souvenirs. For example, a video message is created based on a moment when the user shed tears. The emotion estimation function is also used to identify moments that particularly moved the user and build a system that sells those moments as souvenirs. For example, goods are created based on photos of the user smiling. This allows the user to increase revenue by selling moments that particularly moved them as souvenirs.

[0077] The user input collection unit can analyze the user's operation history and automatically customize the optimal interface. In the user input collection unit, for example, the generation AI analyzes the user's operation history and automatically customizes the optimal interface. For example, it prioritizes displaying functions that the user uses frequently. In addition, the user's operation patterns are analyzed and the generation AI proposes the optimal interface. For example, it customizes and displays menus that the user uses frequently. In addition, the generation AI analyzes the user's operation history in real time and dynamically customizes the optimal interface. For example, it automatically adjusts the interface according to the user's operations. In this way, operability can be improved by customizing the interface based on the user's operation history.

[0078] The user input collection unit can analyze the user's input content in real time and provide input assistance and automatic correction. In the user input collection unit, for example, a generation AI analyzes the user's input content in real time and provides input assistance. For example, it presents appropriate word candidates for the text entered by the user. The user's input content is also analyzed and the generation AI performs automatic correction. For example, typos and omissions are automatically corrected and the text is converted to correct text. Furthermore, a system is constructed in which the generation AI analyzes the user's input content in real time and provides input assistance and automatic correction. For example, grammatical errors are automatically corrected and the text is converted to easy-to-read text. In this way, the user's input content can be analyzed in real time and input assistance and automatic correction can be provided, thereby improving the accuracy and efficiency of input.

[0079] The user input collection unit can provide feedback on user operations in real time, improving operability. In the user input collection unit, for example, the generation AI provides feedback on user operations in real time. For example, when the user performs an operation, appropriate advice or hints are immediately displayed. The generation AI also analyzes the user's operations and provides feedback in real time. For example, it detects operation errors and suggests the correct operation method. Furthermore, a system can be constructed in which the generation AI provides feedback on user operations in real time, improving operability. For example, it suggests the optimal operation method based on the user's operation history. In this way, operability can be improved by providing feedback on user operations in real time.

[0080] The user input collection unit can provide optimal operation guides and tutorials based on the user's operation history. In the user input collection unit, for example, the generation AI analyzes the user's operation history and provides the optimal operation guide. For example, it displays a guide for functions that the user uses frequently. The generation AI also provides the optimal tutorial based on the user's operation history. For example, it displays a tutorial for functions that the user is using for the first time. The generation AI also analyzes the user's operation history and builds a system that provides optimal operation guides and tutorials. For example, it displays a customized guide according to the user's operation pattern. This allows the user to deepen their understanding of operations by providing the optimal operation guides and tutorials based on the user's operation history.

[0081] The user input collection unit can automatically change the interface layout in response to user operations, improving operability. In the user input collection unit, for example, a generation AI automatically changes the interface layout in response to user operations. For example, functions frequently used by the user are placed in prominent positions. The generation AI also analyzes the user's operation patterns and dynamically adjusts the interface layout. For example, an optimal layout is proposed based on the user's operation history. Furthermore, a system can be constructed in which the generation AI automatically changes the interface layout in response to user operations, improving operability. For example, the menu arrangement is dynamically adjusted in accordance with the user's operations. In this way, operability can be improved by automatically changing the interface layout in response to user operations.

[0082] The user input collection unit can use the emotion estimation function to identify stress or frustration felt by the user during operation and improve the interface to resolve it. The user input collection unit, for example, uses the emotion estimation function to identify stress or frustration felt by the user during operation, and the generation AI then improves the interface. For example, if the user is having trouble operating the system, appropriate help is displayed. The generation AI also analyzes the user's emotions, identifies stress or frustration felt by the user during operation, and improves the interface. For example, if the user is irritated, the generation AI makes suggestions to simplify the operation. A system is also constructed that uses the emotion estimation function to identify stress or frustration felt by the user during operation, and the generation AI then improves the interface. For example, the interface layout is adjusted according to the user's emotions. This allows the user's operating experience to be improved by identifying stress or frustration felt by the user during operation and improving the interface to resolve it.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The user input collection unit generates related questions based on the user's input, thereby eliciting the user's memories. For example, the generation AI automatically generates related questions based on the content entered by the user. For example, in response to the question, "Tell me about your childhood memories," it generates additional questions such as, "Who were your friends at the time?" The generation AI also generates related questions based on photos posted by the user. For example, if a user posts a family photo, it generates a question such as, "Where was this photo taken?" The generation AI also analyzes the user's input and generates in-depth questions to elicit memories. For example, it generates a question such as, "What was the most memorable thing about that event?" This allows the user to create a more detailed autobiography by generating related questions to elicit memories.

[0085] The analysis unit can analyze the user's input in real time and generate feedback and suggestions based on the input. For example, the generation AI can analyze the text entered by the user in real time and provide appropriate feedback. For example, it can display positive feedback such as, "That was a wonderful event!". The generation AI can also analyze photos posted by the user in real time and make related suggestions. For example, it can display a suggestion such as, "Please tell us an episode related to this photo." The generation AI can also provide advice in real time based on the user's input. For example, it can display advice such as, "It would be good to add a more specific episode." This makes it possible to support user input by providing feedback and suggestions based on the user's input in real time.

[0086] The user input collection unit allows for voice input and video posting, and the generation AI can analyze this multimedia data and reflect it in the autobiography. For example, the user can input voice, and the generation AI analyzes the voice data and converts it into text. For example, what the user says can be automatically transcribed and reflected in the autobiography. The unit also allows users to post videos, and the generation AI analyzes the video data to extract important scenes. For example, episodes discussed in the video can be converted into text and incorporated into the autobiography. The generation AI also analyzes the data of users who input voice or posted videos, and automatically generates related questions. For example, it generates questions such as "Please tell me more about this scene" based on the content of the video. This allows for the incorporation of a wider variety of data by analyzing voice input and video posting and reflecting it in the autobiography.

[0087] The analysis unit can extract emotional highlights based on the user's input and reflect them in the personal history. For example, the generation AI analyzes the user's input and extracts emotional highlights. For example, it automatically picks out episodes that particularly moved the user and reflects them in the personal history. The generation AI also extracts emotionally significant parts from the user's input and incorporates them into the personal history. For example, it highlights moments that made the user cry or episodes that made them laugh out loud. The generation AI also performs emotion analysis and extracts emotional highlights from the user's input. For example, it reflects events that the user describes as "unforgettable" in the personal history. In this way, by extracting emotional highlights and reflecting them in the personal history, it is possible to generate a personal history that is more emotionally relatable.

[0088] The generation unit can organize the user's input content not only chronologically but also by theme or emotion, generating a personal history from multiple perspectives. For example, the generation AI not only organizes the user's input content chronologically, but also classifies it by theme. For example, it can generate a personal history for each theme, such as "family," "friends," or "work." The generation AI can also organize the user's input content by emotion, generating a personal history from multiple perspectives. For example, it can classify episodes by emotion, such as "joy," "sadness," or "surprise." The generation AI can also analyze the user's input content and organize it chronologically, by theme, or by emotion. For example, it can display episodes related to a specific theme or emotion together. This allows it to generate a personal history from multiple perspectives by organizing it not only chronologically but also by theme or emotion.

[0089] The generation unit can automatically add relevant historical background and events based on the user's input, generating a richer autobiography. For example, the generation AI automatically adds relevant historical background based on the user's input. For example, if a user inputs "I was born in the 1960s," social events from that era will be incorporated into the autobiography. The generation AI also analyzes the user's input and automatically adds relevant events. For example, if a user inputs "I was in a band in high school," the music scene of that era will be reflected in the autobiography. The generation AI also adds relevant historical background and events based on the user's input. For example, if a user inputs "I studied abroad in college," the international situation of that era will be incorporated into the autobiography. This allows for the generation of a richer autobiography by adding relevant historical background and events.

[0090] The storage unit can store the generated autobiography and posted photos in cloud storage to prevent data loss. For example, the generated autobiography and posted photos can be stored in cloud storage. For example, data can be stored using cloud storage such as AWS, Google Cloud, or Azure. The storage unit can also store data in a database. For example, data can be stored using a database such as MySQL or PostgreSQL. In this way, data loss can be prevented by storing it in cloud storage.

[0091] The storage unit can encrypt user data and strengthen security. For example, the generation AI can automatically encrypt user data to strengthen security. For example, photos and text data posted by users can be encrypted and stored in cloud storage. The generation AI can also analyze user data and select an appropriate encryption algorithm to protect the data. For example, strong encryption can be applied to highly important data. The generation AI can also build a system that encrypts user data and strengthens security. For example, data can be encrypted when sent and received to prevent unauthorized access by third parties. In this way, security can be strengthened by encrypting data.

[0092] The storage unit can link user data with other cloud services to achieve centralized data management. For example, the generation AI links user data with other cloud services to achieve centralized management. For example, it links with Google Drive and Dropbox to manage data in an integrated manner. The generation AI also analyzes user data, and links it with the appropriate cloud service to manage the data. For example, photo data is stored in Google Photos and document data in OneDrive. The generation AI also links user data with other cloud services to build a system that achieves centralized management. For example, it manages data that is distributed and stored across multiple cloud services in an integrated manner. This allows for centralized data management, making it easier for users to manage their data.

[0093] The storage unit can use the emotion estimation function to identify data that the user considers particularly important and prioritize protecting that data. For example, the emotion estimation function can be used to identify data that the user considers particularly important, and the generation AI can prioritize protecting that data. For example, text data containing episodes that moved the user can be prioritized as backups. The generation AI can also analyze the user's emotions to identify and protect data that the user considers particularly important. For example, photos in which the user is smiling can be prioritized as encrypted and saved. The emotion estimation function can also be used to identify data that the user considers particularly important, and a system can be constructed in which the generation AI prioritizes protecting that data. For example, data that the user frequently accesses can be prioritized as backups. This can increase data security by prioritizing the protection of data that the user considers particularly important.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: In the user input collection section, users answer questions and post photos. For example, users can respond to questions such as "Tell us about your childhood memories" by text. Users can also upload childhood photos from their own photo albums. Step 2: The analysis unit analyzes the text responses and photos collected by the user input collection unit. For example, the analysis unit analyzes the content of the photos using an image analysis algorithm. It also analyzes the content of the user responses using text analysis techniques. Step 3: The generator generates the autobiography based on the content analyzed by the analyzer. For example, the generator organizes the user's answers in chronological order and places photos in appropriate positions. The generator also generates the autobiography based on prompts containing instructions on what the user wants the generator to do. Step 4: The storage unit stores the generated autobiography and posted photos. For example, the storage unit stores the generated autobiography and photos in cloud storage. It can also store them in a database.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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. [Explanation of symbols]

[0163] 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 user input collection unit where users answer questions and submit photos; an analysis unit that analyzes the text responses and photos collected by the user input collection unit; a generation unit that generates an autobiography based on the content analyzed by the analysis unit; a storage unit for storing the personal history generated by the generation unit and the posted photos; A system characterized by:

2. The user input collection unit: Generate relevant questions based on user input to trigger user memory 2. The system of claim 1.

3. The analysis unit Analyzes user input in real time and generates feedback and suggestions based on the input 2. The system of claim 1.

4. The user input collection unit: Users will be able to input voice and post videos, and the generative AI will analyze this multimedia data and reflect it in their personal history.

2. The system of claim 1.

5. The analysis unit Extract emotional highlights based on user input and reflect them in the autobiography 2. The system of claim 1.

Images