System

Abstract

An object of a system according to an embodiment is to transmit a message for a user or a family member in the future at an appropriate timing.SOLUTION: A system includes a letter receiving unit, a message storage unit, a transmission unit, and a recommendation unit. The letter receiving unit receives a letter or a message written by the user. The message storage unit stores the letter or message received by the letter receiving unit. The transmission unit transmits the letter or message stored by the message storage unit on a designated future date. The recommendation unit recommends a timing and content for writing a future letter addressed to the user or a family member.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to send messages to your future self or family at the appropriate time.

[0005] The system according to the embodiment aims to send messages to one's future self and family at appropriate times. [Means for solving the problem]

[0006] The system according to the embodiment includes a letter receiving unit, a message storage unit, a sending unit, and a recommendation unit. The letter receiving unit receives letters and messages written by a user. The message storage unit stores the letters and messages received by the letter receiving unit. The sending unit sends the letters and messages stored by the message storage unit on a specified future date. The recommendation unit recommends the timing and content of letters to write to oneself or family in the future. [Effects of the Invention]

[0007] The system according to the embodiment can transmit messages to one's future self or family at appropriate timing. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 AI ​​time capsule service according to an embodiment of the present invention is a system that automatically receives letters and messages written by users, sends them on a specified future date, and delivers messages from past selves and deceased loved ones to present users. This allows users to cherish their memories of the deceased while sending messages to the future.

[0029] The AI ​​time capsule service according to the embodiment includes a letter receiving unit, a message storage unit, a transmission unit, and a recommendation unit. The letter receiving unit receives letters and messages written by users. For example, the letter receiving unit digitizes and receives handwritten letters using scanning technology. The letter receiving unit can also directly receive letters and messages submitted in digital format. The letter receiving unit can also read printed letters using OCR technology. For example, the letter receiving unit scans handwritten letters with a high-resolution scanner and converts them into text information using OCR technology. Digital letters submitted in a specific file format can also be directly received. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The message storage unit stores letters and messages received by the letter receiving unit. For example, the message storage unit stores letters and messages in a database. The message storage unit can also encrypt and store letters and messages. For example, the message storage unit stores letters and messages using AES encryption technology and decrypts and sends them only at a specified date and time. The transmission unit sends letters and messages stored by the message storage unit at a specified future date. For example, the sending unit automatically sends a letter or message on a future date specified by the user. The sending unit can also send a letter or message according to a sending method desired by the user (e.g., email, a messenger app, social media, etc.). For example, the sending unit sends a letter or message by specifying an email address. The recommendation unit recommends the timing and content of a letter to write to your future self or family. For example, the recommendation unit analyzes the user's past messages and behavioral history and makes a recommendation such as, "Now is the best time to write a letter to your future self." The recommendation unit also provides specific advice on the content of the letter, such as, "It would be good to write something that reflects on memories with your family." For example, the recommendation unit analyzes the user's behavioral history and suggests the timing to write the letter based on a specific event (e.g., birthday, anniversary, etc.). This allows the AI ​​time capsule service according to the embodiment to send a message to the future while cherishing memories with the deceased.For example, you can convey precious memories with family and friends to the future, and receive messages from your past self or deceased loved ones, allowing you to feel a bond that transcends time. Furthermore, the generative AI's recommendation function can provide advice on the timing and content of letters, allowing you to send more effective messages.

[0030] The sending unit can analyze the contents of the letter and send it at the most touching moment for the recipient. For example, the generation AI analyzes the contents of the letter and identifies touching moments, such as the recipient's birthday or anniversary. For example, if the letter contains the words "Happy Birthday," it will be sent on that birthday. This allows the content of the letter to be analyzed and sent at the most touching moment, impressing the recipient.

[0031] The sending unit allows the generation AI to automatically attach relevant images and audio messages based on the content of the letter. For example, the sending unit uses the generation AI to analyze the content of the letter and automatically search for and attach relevant images. For example, if the letter says "Memories of a family trip," it will attach photos from that trip. This provides the recipient with a more moving experience by attaching relevant images and audio messages based on the content of the letter.

[0032] The recommendation unit allows the generation AI to automatically generate additional messages and advice for future recipients based on the contents of the letter. For example, the recommendation unit allows the generation AI to analyze the contents of the letter and automatically generate additional messages for future recipients. For example, if the letter says "do your best," specific advice will be added. This automatically generates additional messages and advice based on the contents of the letter, thereby strengthening support for the recipient.

[0033] The recommendation unit can translate the contents of the letter into multiple languages, making it possible to accommodate recipients from different cultural backgrounds. For example, the generation AI can automatically translate the contents of the letter into multiple languages, making it possible to accommodate recipients from different cultural backgrounds. For example, it can translate into English, French, Chinese, etc. This makes it possible to accommodate recipients from different cultural backgrounds by translating the contents of the letter into multiple languages.

[0034] The message storage unit can create a video montage for the generation AI to reminisce about the deceased's memories based on the message of the deceased. For example, the message storage unit allows the generation AI to analyze the message of the deceased and create a video montage for reminiscing about the deceased. For example, the message storage unit generates a video by combining photos and videos of the deceased. This creates a video montage for reminiscing about the deceased, which moves the recipient.

[0035] The message storage unit uses a generation AI to create a digital album compiling highlights from the deceased's life based on the message of the deceased. For example, the message storage unit uses a generation AI to analyze the message of the deceased and create a digital album compiling highlights from the deceased's life. For example, an album is generated by combining photos and messages of the deceased. This creates a digital album compiling highlights from the deceased's life, which moves the recipient.

[0036] The message storage unit can store the deceased's message in multiple formats, allowing the recipient to select. For example, the message storage unit uses a generation AI to analyze the deceased's message and store it in multiple formats (text, audio, video). For example, the message of the deceased is stored as text, and also as audio and video. This allows the recipient to select by storing the deceased's message in multiple formats.

[0037] The recommendation unit can predict the user's life events and recommend the corresponding content of the letter. For example, the generation AI predicts the user's life events and recommends the corresponding content of the letter. For example, if the user is planning to get married, the recommendation unit will suggest the content of the letter related to the wedding. This makes it possible to create an effective message for the user by predicting the user's life events and recommending the corresponding content of the letter.

[0038] The recommendation unit can analyze messages from the user's family and friends and recommend letter content based on that. For example, the recommendation unit uses a generation AI to analyze messages from the user's family and friends and recommend letter content based on that. For example, it can suggest a letter of thanks to family members based on their messages. This allows the system to create an effective message for the user by analyzing messages from the user's family and friends and recommending letter content based on that.

[0039] The recommendation unit can analyze the user's hobbies and interests and customize the content of the letter based on that. For example, the recommendation unit uses a generation AI to analyze the user's hobbies and interests and customize the content of the letter based on that. For example, if the user is interested in music, it will suggest letter content related to music. This allows the system to create an effective message for the user by analyzing the user's hobbies and interests and customizing the content of the letter based on that.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The AI ​​time capsule service can also include a generation unit that predicts the user's life events and automatically generates the content of the letter based on those events. For example, if the user plans to get married, the generation unit can automatically generate the content of a letter about the wedding. If the user plans to have children, the generation unit can automatically generate a message for the children. If the user plans to retire, the generation unit can automatically generate the content of a letter about retirement. This makes it possible to provide the user with the optimal letter content according to their life events.

[0042] The AI ​​time capsule service can also include a customization unit that analyzes the user's hobbies and interests and customizes the content of the letter based on those analysis results. For example, if the user is interested in music, the customization unit can suggest music-related letter content. If the user is interested in sports, the customization unit can suggest sports-related letter content. If the user is interested in travel, the customization unit can suggest travel-related letter content. This makes it possible to provide optimal letter content tailored to the user's hobbies and interests.

[0043] The AI ​​time capsule service can also include a recommendation unit that analyzes messages from the user's family and friends and recommends letter content based on the results. For example, the recommendation unit can suggest thank-you letters to family members based on messages from family members. Also, the recommendation unit can suggest thank-you letters to friends based on messages from friends. Furthermore, the recommendation unit can suggest thank-you letters to colleagues based on messages from colleagues. This makes it possible to provide optimal letter content based on the messages from the user's family and friends.

[0044] The AI ​​time capsule service can also be equipped with a past message analysis unit that analyzes a user's past messages and automatically generates the content of a letter based on them. For example, it can analyze the content of letters written by a user in the past and automatically generate the content of a new letter based on the past messages. It can also analyze messages sent by a user in the past and automatically generate the content of a new letter based on the past messages. It can also analyze messages received by a user in the past and automatically generate the content of a new letter based on the past messages. This makes it possible to provide the optimal content of a letter based on the user's past messages.

[0045] The AI ​​time capsule service can also include a generation unit that predicts the user's life events and automatically generates the content of the letter based on those events. For example, if the user plans to get married, the generation unit can automatically generate the content of a letter about the wedding. If the user plans to have children, the generation unit can automatically generate a message for the children. If the user plans to retire, the generation unit can automatically generate the content of a letter about retirement. This makes it possible to provide the user with the optimal letter content according to their life events.

[0046] The AI ​​time capsule service can also include a customization unit that analyzes the user's hobbies and interests and customizes the content of the letter based on those analysis results. For example, if the user is interested in music, the customization unit can suggest music-related letter content. If the user is interested in sports, the customization unit can suggest sports-related letter content. If the user is interested in travel, the customization unit can suggest travel-related letter content. This makes it possible to provide optimal letter content tailored to the user's hobbies and interests.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The letter receiving unit receives letters and messages written by users. For example, handwritten letters are digitized and received using scanning technology. Letters and messages submitted in digital format can also be directly received. The letter receiving unit can also read printed letters using OCR technology. For example, a handwritten letter is scanned with a high-resolution scanner and converted into text information using OCR technology. Digital letters submitted in a specific file format can be directly received. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The message storage unit stores the letters and messages received by the letter receiving unit. For example, it stores the letters and messages in a database. The message storage unit can also encrypt and store the letters and messages. For example, it can store them using AES encryption technology and decrypt and send them only at a specified date and time. Step 3: The sending unit sends the letter or message stored by the message storage unit at a specified future date. For example, the sending unit automatically sends the letter or message at a future date specified by the user. The sending unit can also send the letter or message according to the sending method desired by the user (e.g., email, messenger app, SNS, etc.). For example, the sending unit sends the letter or message by specifying an email address. Step 4: The recommendation unit recommends the timing and content of a letter to your future self or family. For example, it analyzes the user's past messages and behavioral history and makes a recommendation such as, "Now is the best time to write a letter to your future self." It also provides specific advice on the content of the letter, such as, "It would be good to write something that reflects on memories with your family." For example, it analyzes the user's behavioral history and suggests the best time to write a letter based on a specific event (e.g., birthday, anniversary, etc.).

[0049] (Example 2) The AI ​​time capsule service according to an embodiment of the present invention is a system that automatically receives letters and messages written by users, sends them on a specified future date, and delivers messages from past selves and deceased loved ones to present users. This allows users to cherish their memories of the deceased while sending messages to the future.

[0050] The AI ​​time capsule service according to the embodiment includes a letter receiving unit, a message storage unit, a transmission unit, and a recommendation unit. The letter receiving unit receives letters and messages written by users. For example, the letter receiving unit digitizes and receives handwritten letters using scanning technology. The letter receiving unit can also directly receive letters and messages submitted in digital format. The letter receiving unit can also read printed letters using OCR technology. For example, the letter receiving unit scans handwritten letters with a high-resolution scanner and converts them into text information using OCR technology. Digital letters submitted in a specific file format can also be directly received. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The message storage unit stores letters and messages received by the letter receiving unit. For example, the message storage unit stores letters and messages in a database. The message storage unit can also encrypt and store letters and messages. For example, the message storage unit stores letters and messages using AES encryption technology and decrypts and sends them only at a specified date and time. The transmission unit sends letters and messages stored by the message storage unit at a specified future date. For example, the sending unit automatically sends a letter or message on a future date specified by the user. The sending unit can also send a letter or message according to a sending method desired by the user (e.g., email, a messenger app, social media, etc.). For example, the sending unit sends a letter or message by specifying an email address. The recommendation unit recommends the timing and content of a letter to write to your future self or family. For example, the recommendation unit analyzes the user's past messages and behavioral history and makes a recommendation such as, "Now is the best time to write a letter to your future self." The recommendation unit also provides specific advice on the content of the letter, such as, "It would be good to write something that reflects on memories with your family." For example, the recommendation unit analyzes the user's behavioral history and suggests the timing to write the letter based on a specific event (e.g., birthday, anniversary, etc.). This allows the AI ​​time capsule service according to the embodiment to send a message to the future while cherishing memories with the deceased.For example, you can convey precious memories with family and friends to the future, and receive messages from your past self or deceased loved ones, allowing you to feel a bond that transcends time. Furthermore, the generative AI's recommendation function can provide advice on the timing and content of letters, allowing you to send more effective messages.

[0051] The sending unit can analyze the contents of the letter and send it at the most touching moment for the recipient. For example, the generation AI analyzes the contents of the letter and identifies touching moments, such as the recipient's birthday or anniversary. For example, if the letter contains the words "Happy Birthday," it will be sent on that birthday. This allows the content of the letter to be analyzed and sent at the most touching moment, impressing the recipient.

[0052] The sending unit allows the generation AI to automatically attach relevant images and audio messages based on the content of the letter. For example, the sending unit uses the generation AI to analyze the content of the letter and automatically search for and attach relevant images. For example, if the letter says "Memories of a family trip," it will attach photos from that trip. This provides the recipient with a more moving experience by attaching relevant images and audio messages based on the content of the letter.

[0053] The sending unit can use the emotion estimation function to analyze the emotional tone of the letter and adjust the timing of sending to make the recipient most moved. For example, the sending unit uses a generation AI to analyze the emotional tone of the letter and predict the timing when the recipient will be most moved. For example, if the content of the letter includes a moving episode, it will be sent on a date related to that episode. In this way, the recipient is moved by analyzing the emotional tone of the letter using the emotion estimation function and sending it at the timing when it will be most moved.

[0054] The recommendation unit allows the generation AI to automatically generate additional messages and advice for future recipients based on the contents of the letter. For example, the recommendation unit allows the generation AI to analyze the contents of the letter and automatically generate additional messages for future recipients. For example, if the letter says "do your best," specific advice will be added. This automatically generates additional messages and advice based on the contents of the letter, thereby strengthening support for the recipient.

[0055] The recommendation unit can translate the contents of the letter into multiple languages, making it possible to accommodate recipients from different cultural backgrounds. For example, the generation AI can automatically translate the contents of the letter into multiple languages, making it possible to accommodate recipients from different cultural backgrounds. For example, it can translate into English, French, Chinese, etc. This makes it possible to accommodate recipients from different cultural backgrounds by translating the contents of the letter into multiple languages.

[0056] The message storage unit analyzes the deceased's message, and the generation AI can reproduce the deceased's voice and send it as an audio message. For example, the message storage unit generates an audio message using voice synthesis technology to reproduce the deceased's voice. For example, the reproduction is based on audio data from the deceased's life. This allows the deceased's voice to be reproduced and sent as an audio message, which moves the recipient.

[0057] The message storage unit can create a video montage for the generation AI to reminisce about the deceased's memories based on the message of the deceased. For example, the message storage unit allows the generation AI to analyze the message of the deceased and create a video montage for reminiscing about the deceased. For example, the message storage unit generates a video by combining photos and videos of the deceased. This creates a video montage for reminiscing about the deceased, which moves the recipient.

[0058] The message storage unit can use the emotion estimation function to analyze the emotional tone of the deceased's message and edit the message to be most moving to the recipient. For example, the message storage unit analyzes the deceased's message using the generation AI, identifies the emotional tone using the emotion estimation function, and edits the message to be most moving to the recipient. For example, it emphasizes the moving parts. In this way, the emotional tone of the deceased's message can be analyzed and edited to be most moving, thereby moving the recipient.

[0059] The message storage unit uses a generation AI to create a digital album compiling highlights from the deceased's life based on the message of the deceased. For example, the message storage unit uses a generation AI to analyze the message of the deceased and create a digital album compiling highlights from the deceased's life. For example, an album is generated by combining photos and messages of the deceased. This creates a digital album compiling highlights from the deceased's life, which moves the recipient.

[0060] The message storage unit can store the deceased's message in multiple formats, allowing the recipient to select. For example, the message storage unit uses a generation AI to analyze the deceased's message and store it in multiple formats (text, audio, video). For example, the message of the deceased is stored as text, and also as audio and video. This allows the recipient to select by storing the deceased's message in multiple formats.

[0061] The message storage unit can use the emotion estimation function to analyze the recipient's emotional response to the deceased's message and provide the message in the optimal format. For example, the message storage unit uses a generation AI to analyze the deceased's message, and then uses the emotion estimation function to analyze the recipient's emotional response and provide the message in the optimal format. For example, it provides a moving message in audio format. This allows the recipient to be moved by analyzing the recipient's emotional response to the deceased's message and providing the message in the optimal format.

[0062] The recommendation unit can predict the user's life events and recommend the corresponding content of the letter. For example, the generation AI predicts the user's life events and recommends the corresponding content of the letter. For example, if the user is planning to get married, the recommendation unit will suggest the content of the letter related to the wedding. This makes it possible to create an effective message for the user by predicting the user's life events and recommending the corresponding content of the letter.

[0063] The recommendation unit uses the emotion estimation function to analyze the user's current emotional state and suggest the optimal letter content based on that. For example, the recommendation unit uses a generation AI to analyze the user's current emotional state and suggest the optimal letter content based on that. For example, if the user is moved, it will suggest an emotional letter content. This allows the system to create an effective message for the user by analyzing the user's current emotional state and suggesting the optimal letter content based on that.

[0064] The recommendation unit can analyze messages from the user's family and friends and recommend letter content based on that. For example, the recommendation unit uses a generation AI to analyze messages from the user's family and friends and recommend letter content based on that. For example, it can suggest a letter of thanks to family members based on their messages. This allows the system to create an effective message for the user by analyzing messages from the user's family and friends and recommending letter content based on that.

[0065] The recommendation unit can analyze the user's hobbies and interests and customize the content of the letter based on that. For example, the recommendation unit uses a generation AI to analyze the user's hobbies and interests and customize the content of the letter based on that. For example, if the user is interested in music, it will suggest letter content related to music. This allows the system to create an effective message for the user by analyzing the user's hobbies and interests and customizing the content of the letter based on that.

[0066] The recommendation unit can use the emotion estimation function to recommend letter content in real time according to the user's emotional state. For example, the recommendation unit uses a generation AI to analyze the user's emotional state in real time and recommend letter content based on that. For example, if the user is emotional, the recommendation unit will suggest letter content that is inspiring. This allows the creation of effective messages for users by recommending letter content in real time according to the user's emotional state.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The AI ​​time capsule service can also include an editing department that estimates the user's emotions and edits the contents of the letter based on the estimated emotions. For example, if the user is sad, the editing department can change the contents of the letter to a message of encouragement. If the user is happy, the editing department can change the contents of the letter to a message of congratulations. Furthermore, if the user is moved, the editing department can change the contents of the letter to a touching episode. This makes it possible to provide the optimal letter content according to the user's emotions.

[0069] The AI ​​time capsule service can also include a generation unit that predicts the user's life events and automatically generates the content of the letter based on those events. For example, if the user plans to get married, the generation unit can automatically generate the content of a letter about the wedding. If the user plans to have children, the generation unit can automatically generate a message for the children. If the user plans to retire, the generation unit can automatically generate the content of a letter about retirement. This makes it possible to provide the user with the optimal letter content according to their life events.

[0070] The AI ​​time capsule service can also include a customization unit that analyzes the user's hobbies and interests and customizes the content of the letter based on those analysis results. For example, if the user is interested in music, the customization unit can suggest music-related letter content. If the user is interested in sports, the customization unit can suggest sports-related letter content. If the user is interested in travel, the customization unit can suggest travel-related letter content. This makes it possible to provide optimal letter content tailored to the user's hobbies and interests.

[0071] The AI ​​time capsule service may further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of sending letters based on the estimated emotions. For example, if the user is emotional, the timing adjustment unit can adjust the timing of sending an emotional letter. If the user is sad, the timing adjustment unit can adjust the timing of sending an encouraging letter. If the user is happy, the timing adjustment unit can adjust the timing of sending a congratulatory letter. This makes it possible to provide the optimal timing for sending letters according to the user's emotions.

[0072] The AI ​​time capsule service can also include a recommendation unit that analyzes messages from the user's family and friends and recommends letter content based on the results. For example, the recommendation unit can suggest thank-you letters to family members based on messages from family members. Also, the recommendation unit can suggest thank-you letters to friends based on messages from friends. Furthermore, the recommendation unit can suggest thank-you letters to colleagues based on messages from colleagues. This makes it possible to provide optimal letter content based on the messages from the user's family and friends.

[0073] The AI ​​time capsule service can also include a real-time recommendation unit that estimates the user's emotions and recommends letter content in real time based on the estimated emotions. For example, if the user is moved, the real-time recommendation unit can suggest the content of an inspiring letter. If the user is sad, the real-time recommendation unit can suggest the content of an encouraging letter. If the user is happy, the real-time recommendation unit can suggest the content of a congratulatory letter. This makes it possible to provide the optimal letter content in real time according to the user's emotions.

[0074] The AI ​​time capsule service can also be equipped with a past message analysis unit that analyzes a user's past messages and automatically generates the content of a letter based on them. For example, it can analyze the content of letters written by a user in the past and automatically generate the content of a new letter based on the past messages. It can also analyze messages sent by a user in the past and automatically generate the content of a new letter based on the past messages. It can also analyze messages received by a user in the past and automatically generate the content of a new letter based on the past messages. This makes it possible to provide the optimal content of a letter based on the user's past messages.

[0075] The AI ​​time capsule service can also include an editing department that estimates the user's emotions and edits the contents of the letter based on the estimated emotions. For example, if the user is sad, the editing department can change the contents of the letter to a message of encouragement. If the user is happy, the editing department can change the contents of the letter to a message of congratulations. Furthermore, if the user is moved, the editing department can change the contents of the letter to a touching episode. This makes it possible to provide the optimal letter content according to the user's emotions.

[0076] The AI ​​time capsule service can also include a generation unit that predicts the user's life events and automatically generates the content of the letter based on those events. For example, if the user plans to get married, the generation unit can automatically generate the content of a letter about the wedding. If the user plans to have children, the generation unit can automatically generate a message for the children. If the user plans to retire, the generation unit can automatically generate the content of a letter about retirement. This makes it possible to provide the user with the optimal letter content according to their life events.

[0077] The AI ​​time capsule service can also include a customization unit that analyzes the user's hobbies and interests and customizes the content of the letter based on those analysis results. For example, if the user is interested in music, the customization unit can suggest music-related letter content. If the user is interested in sports, the customization unit can suggest sports-related letter content. If the user is interested in travel, the customization unit can suggest travel-related letter content. This makes it possible to provide optimal letter content tailored to the user's hobbies and interests.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The letter receiving unit receives letters and messages written by users. For example, handwritten letters are digitized and received using scanning technology. Letters and messages submitted in digital format can also be directly received. The letter receiving unit can also read printed letters using OCR technology. For example, a handwritten letter is scanned with a high-resolution scanner and converted into text information using OCR technology. Digital letters submitted in a specific file format can be directly received. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The message storage unit stores the letters and messages received by the letter receiving unit. For example, it stores the letters and messages in a database. The message storage unit can also encrypt and store the letters and messages. For example, it can store them using AES encryption technology and decrypt and send them only at a specified date and time. Step 3: The sending unit sends the letter or message stored by the message storage unit at a specified future date. For example, the sending unit automatically sends the letter or message at a future date specified by the user. The sending unit can also send the letter or message according to the sending method desired by the user (e.g., email, messenger app, SNS, etc.). For example, the sending unit sends the letter or message by specifying an email address. Step 4: The recommendation unit recommends the timing and content of a letter to your future self or family. For example, it analyzes the user's past messages and behavioral history and makes a recommendation such as, "Now is the best time to write a letter to your future self." It also provides specific advice on the content of the letter, such as, "It would be good to write something that reflects on memories with your family." For example, it analyzes the user's behavioral history and suggests the best time to write a letter based on a specific event (e.g., birthday, anniversary, etc.).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 letter receiving unit for receiving letters and messages written by users; a message storage unit for storing letters and messages received by the letter receiving unit; a sending unit for sending the letter or message stored by the message storage unit at a specified future date; It also has a recommendation section that recommends the timing and content of letters to write to your future self or family. A system characterized by:

2. The transmission unit Based on the content of the letter, the AI ​​automatically attaches relevant images and audio messages.

2. The system of claim 1.

3. The recommendation unit Based on the contents of the letter, the generative AI automatically generates additional messages and advice for future recipients.

2. The system of claim 1.

4. The message storage unit Based on the deceased's message, generative AI creates a video montage to reminisce about the deceased.

2. The system of claim 1.

5. The recommendation unit Using emotion estimation functionality, the current emotional state of the user is analyzed and the optimal content of the letter is suggested based on the analysis.

2. The system of claim 1.

6. The transmission unit Using emotion estimation, the emotional tone of the letter is analyzed and the timing of sending it is adjusted to best impress the recipient.

2. The system of claim 1.

7. The message storage unit Use emotion estimation to analyze the emotional tone of the deceased's message and edit it to best appeal to the recipient.

2. The system of claim 1.

8. The recommendation unit Using an emotion estimation function, the content of the letter is recommended in real time according to the emotional state of the user.

2. The system of claim 1.

Images