System

Abstract

An object of the system according to the embodiment is to inscribe memories and remorse with the deceased in cosmic space and feel a connection with the cosmic space.SOLUTION: A system according to an embodiment includes a message reception unit, a data transmission unit, a data broadcasting unit, and a signal tracking unit. The message receiving unit receives a message, an image, and a voice from a user. The data transmission section transmits the message, the image and the voice received by the message reception section to the small satellite. The data broadcasting unit broadcasts the message, the image, and the sound transmitted by the data transmitting unit in outer space. The signal tracker tracks signals emitted by the small satellites.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has no means of imprinting memories and regrets of deceased loved ones in space, making it difficult to feel connected to space.

[0005] The system of the embodiment aims to imprint memories and regrets of the deceased in outer space and to feel a connection with the universe. [Means for solving the problem]

[0006] The system according to the embodiment includes a message receiving unit, a data transmitting unit, a data broadcasting unit, and a signal tracking unit. The message receiving unit receives messages, images, and audio from users. The data transmitting unit transmits the messages, images, and audio received by the message receiving unit to the small satellite. The data broadcasting unit broadcasts the messages, images, and audio transmitted by the data transmitting unit in space. The signal tracking unit tracks signals emitted by the small satellite. [Effects of the Invention]

[0007] The system according to the embodiment allows users to inscribe memories and regrets of the deceased in outer space, allowing them to feel a connection with the universe. [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 GalacticMemories Satellite system according to an embodiment of the present invention is a system in which messages, images, and audio received from users are transmitted into space by a generation AI installed on a small satellite and broadcast in outer space. In this way, the GalacticMemories Satellite system imprints thoughts about the deceased and wishes for the future in outer space, and users can feel a connection with space by tracking the signal from Earth.

[0029] The GalacticMemories Satellite system according to the embodiment includes a message receiving unit, a data transmitting unit, a data broadcasting unit, and a signal tracking unit. The message receiving unit receives messages, images, and audio from users. For example, if a user sends a message such as "Thank you, Mom. I'll never forget your smile," the generation AI analyzes the message and converts it into an appropriate format. The message receiving unit also receives image and audio data sent by the user, which are then converted into an appropriate format by the generation AI. The data transmitting unit transmits the received messages, images, and audio to a small satellite. For example, the generation AI transmits the received data to a small satellite and prepares it for broadcast in space. The data broadcasting unit broadcasts the received messages, images, and audio in space. For example, by broadcasting a message sent by a user in space, thoughts of the deceased will remain forever in the infinite expanse of space. The signal tracking unit tracks the signals emitted by the small satellite. For example, a user can use a dedicated application to check the satellite's location and feel a connection to space. As a result, the GalacticMemories Satellite system of the embodiment broadcasts users' messages into space and allows them to feel a connection with their deceased loved ones by tracking the signal.

[0030] The message receiving unit can analyze the user's past message history and suggest the optimal message format and content. For example, the message receiving unit uses a generation AI to analyze the user's past message history and suggest the optimal message format and content. For example, it suggests a new message based on the tone and style of messages sent in the past. This makes it possible to provide a message that is in line with the user's intentions by suggesting the optimal message format and content based on the past message history.

[0031] The message receiving unit can analyze the user's handwritten message, digitize it, and send it. For example, the message receiving unit uses a generation AI to analyze the user's handwritten message, digitize it, and send it. For example, it recognizes handwritten characters, converts them into digital text, and sends it. In this way, by digitizing and sending the user's handwritten message, it can be sent in digital format while maintaining the warmth of handwriting.

[0032] The data sending unit can analyze the content of the message and automatically determine the optimal sending timing. For example, the generation AI analyzes the content of the message and automatically determines the optimal sending timing. For example, important messages can be sent immediately, and general messages can be sent later. This allows for effective message sending by analyzing the content of the message and automatically determining the optimal sending timing.

[0033] The data transmission unit can evaluate the importance of messages and prioritize them for transmission. For example, the generation AI evaluates the importance of messages and prioritizes them for transmission. For example, emotionally important messages are sent with priority. By evaluating the importance of messages and prioritizing their transmission, important messages can be sent with priority.

[0034] The data transmission unit translates the contents of the message into multiple languages, making it possible to accommodate international users. For example, the generation AI translates the contents of the message into multiple languages, making it possible to accommodate international users. For example, it translates into multiple languages ​​such as English, Japanese, and French. This allows the contents of the message to be translated into multiple languages, making it possible to provide services to a wide range of users.

[0035] The data sending unit can analyze the content of the message and automatically add related images and audio. For example, the generation AI in the data sending unit analyzes the content of the message and automatically adds related images and audio. For example, an image expressing gratitude can be added to a thank-you message. This allows the content of the message to be analyzed and related images and audio automatically added, making it possible to provide a richer message.

[0036] The data broadcasting unit can automatically adjust the frequency of message broadcasts and broadcast them at the optimal timing. For example, a small satellite can automatically adjust the frequency of message broadcasts and broadcast them at the optimal timing. For example, important messages can be broadcast frequently, and general messages can be broadcast at intervals. This allows the frequency of message broadcasts to be automatically adjusted and broadcast at the optimal timing, making it possible to broadcast messages effectively.

[0037] The data broadcasting unit can analyze the contents of messages and optimize the broadcast order. For example, a small satellite can analyze the contents of messages and optimize the broadcast order. For example, it can broadcast emotionally important messages with priority. This allows for effective message broadcasting by analyzing the contents of messages and optimizing the broadcast order.

[0038] The data broadcasting unit can analyze the content of a message and broadcast it by linking it to related constellations and celestial bodies. For example, a small satellite can analyze the content of a message and broadcast it by linking it to related constellations and celestial bodies. For example, a message of gratitude can be broadcast by linking it to a specific constellation. This makes it possible to broadcast a more moving message by analyzing the content of the message and broadcasting it by linking it to related constellations and celestial bodies.

[0039] The data broadcasting unit can analyze the content of the message and add visual effects during broadcast. For example, a small satellite can analyze the content of the message and add visual effects during broadcast. For example, an effect expressing gratitude can be added to a message of gratitude. In this way, by analyzing the content of the message and adding visual effects during broadcast, it becomes possible to broadcast a more moving message.

[0040] The signal tracking unit can analyze signal strength and location information and suggest the optimal tracking method to the user. For example, the signal tracking unit uses a generation AI to analyze signal strength and location information and suggest the optimal tracking method to the user. For example, it can identify locations with strong signals and guide the user to those locations. This enables effective signal tracking by analyzing signal strength and location information and suggesting the optimal tracking method to the user.

[0041] The signal tracking unit can analyze the signal history and make predictions based on past tracking data. For example, the signal tracking unit uses a generation AI to analyze the signal history and make predictions based on past tracking data. For example, it predicts future signal strength based on fluctuations in signal strength in the past. This enables effective signal tracking by analyzing the signal history and making predictions based on past tracking data.

[0042] The signal tracking unit can analyze the location information of the signal and suggest the optimal observation point to the user. For example, the generation AI analyzes the location information of the signal and suggests the optimal observation point to the user. For example, it identifies a location where the signal is strong and guides the user to that location. This enables effective signal tracking by analyzing the location information of the signal and suggesting the optimal observation point to the user.

[0043] The signal tracking unit can analyze the signal strength and suggest the optimal receiving device to the user. For example, the signal tracking unit uses a generation AI to analyze the signal strength and suggest the optimal receiving device to the user. For example, if the signal is weak, a more sensitive receiving device will be suggested. This enables effective signal tracking by analyzing the signal strength and suggesting the optimal receiving device to the user.

[0044] The system monitors the status of the satellite in real time and can automatically make corrections if an abnormality is detected. For example, the system uses a generation AI to monitor the status of the satellite in real time and automatically make corrections if an abnormality is detected. For example, if a communication failure occurs, it will automatically attempt to reconnect. This allows the system to monitor the status of the satellite in real time and automatically make corrections if an abnormality is detected, enabling stable satellite operation.

[0045] The system can periodically back up satellite data to prevent data loss. For example, the generating AI can periodically back up satellite data to prevent data loss. For example, the data can be stored in the cloud on a daily basis. This ensures data safety by periodically backing up satellite data to prevent data loss.

[0046] The system can automatically optimize satellite maintenance schedules and achieve efficient operations. For example, generative AI can automatically optimize satellite maintenance schedules and achieve efficient operations. For example, it can carry out regular maintenance at the optimal timing. This automatically optimizes satellite maintenance schedules and achieves efficient operations, enabling stable satellite operations.

[0047] The system can analyze satellite data and suggest the optimal data update method to the user. For example, the system uses a generative AI to analyze satellite data and suggest the optimal data update method to the user. For example, important data is updated immediately. This allows for efficient data updates by analyzing satellite data and suggesting the optimal data update method to the user.

[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] When receiving a user's message, the message receiving unit can automatically select relevant music based on the content of the message and send it along with the message. For example, music that expresses gratitude can be selected for a message of gratitude, and uplifting music can be selected for a message of encouragement. By automatically selecting music according to the content of the message, it is possible to provide a more moving message.

[0050] The message receiving unit can analyze the user's past message history and suggest the optimal message format and content. For example, it can suggest new messages based on the tone and style of messages sent in the past. It can also suggest related messages to users who frequently use specific keywords or phrases. This allows it to provide messages that are in line with the user's intentions by suggesting the optimal message format and content based on the user's past message history.

[0051] The message receiving unit can analyze a user's handwritten message, digitize it, and send it. For example, it can recognize handwritten characters and convert them into digital text before sending it. It can also analyze handwritten illustrations and drawings and send them in digital format. This allows a user's handwritten message to be digitized and sent in digital format while preserving the warmth of handwriting.

[0052] The data transmission unit can analyze the content of a message and automatically determine the optimal transmission timing. For example, important messages can be sent immediately and general messages can be sent later. Messages can also be sent to coincide with specific events or anniversaries. This allows for effective message transmission by analyzing the content of the message and automatically determining the optimal transmission timing.

[0053] The data transmission unit can evaluate the importance of messages and prioritize them for transmission. For example, emotionally important messages can be sent with priority. Also, urgent messages can be sent immediately, and general messages can be sent later. In this way, by evaluating the importance of messages and prioritizing their transmission, important messages can be sent with priority.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The message receiver receives messages, images, and audio from the user. For example, if the user sends a message such as "Thank you, Mom. I'll never forget your smile," the generation AI analyzes this message and converts it into an appropriate format. The message receiver also receives image and audio data sent by the user, which are then converted into an appropriate format by the generation AI. Step 2: The data transmitter transmits the received messages, images, and audio to a small satellite. For example, the generation AI transmits the received data to a small satellite and prepares it for broadcast in space. Step 3: The data broadcasting unit broadcasts the received message, image, and sound into space. For example, a message sent by a user can be broadcast into space, allowing the memory of the deceased to remain forever in the infinite expanse of space. Step 4: The signal tracking unit tracks the signals emitted by the small satellites. For example, users can use a dedicated application to check the satellite's position and feel connected to space.

[0056] (Example 2) The GalacticMemories Satellite system according to an embodiment of the present invention is a system in which messages, images, and audio received from users are transmitted into space by a generation AI installed on a small satellite and broadcast in outer space. In this way, the GalacticMemories Satellite system imprints thoughts about the deceased and wishes for the future in outer space, and users can feel a connection with space by tracking the signal from Earth.

[0057] The GalacticMemories Satellite system according to the embodiment includes a message receiving unit, a data transmitting unit, a data broadcasting unit, and a signal tracking unit. The message receiving unit receives messages, images, and audio from users. For example, if a user sends a message such as "Thank you, Mom. I'll never forget your smile," the generation AI analyzes the message and converts it into an appropriate format. The message receiving unit also receives image and audio data sent by the user, which are then converted into an appropriate format by the generation AI. The data transmitting unit transmits the received messages, images, and audio to a small satellite. For example, the generation AI transmits the received data to a small satellite and prepares it for broadcast in space. The data broadcasting unit broadcasts the received messages, images, and audio in space. For example, by broadcasting a message sent by a user in space, thoughts of the deceased will remain forever in the infinite expanse of space. The signal tracking unit tracks the signals emitted by the small satellite. For example, a user can use a dedicated application to check the satellite's location and feel a connection to space. As a result, the GalacticMemories Satellite system of the embodiment broadcasts users' messages into space and allows them to feel a connection with their deceased loved ones by tracking the signal.

[0058] The message receiving unit can analyze the user's emotions in real time and automatically generate a message format that corresponds to the emotion. For example, the message receiving unit uses a generation AI to analyze the user's emotions in real time and automatically generate a message format that corresponds to the emotion. For example, if the user is feeling sad, the generation AI generates a message in a gentle tone that matches that emotion. This allows the system to provide a message that is in tune with the user's emotions by automatically generating a message format that corresponds to the user's emotions.

[0059] The message receiving unit can analyze the user's past message history and suggest the optimal message format and content. For example, the message receiving unit uses a generation AI to analyze the user's past message history and suggest the optimal message format and content. For example, it suggests a new message based on the tone and style of messages sent in the past. This makes it possible to provide a message that is in line with the user's intentions by suggesting the optimal message format and content based on the past message history.

[0060] The message receiving unit can automatically adjust the tone and style of the message based on the user's emotions. For example, the message receiving unit uses an emotion estimation function to automatically adjust the tone and style of the message based on the user's emotions. For example, if the user is feeling sad, the generation AI generates a message with a gentle tone that matches that emotion. This allows the message tone and style to be automatically adjusted based on the user's emotions, making it possible to provide a message that is in tune with the user's emotions.

[0061] The message receiving unit can analyze the user's tone of voice and facial expression and generate a voice message that corresponds to the emotion. For example, the message receiving unit uses a generation AI to analyze the user's tone of voice and generate a voice message that corresponds to the emotion. For example, if the user is feeling sad, a voice message with a gentle tone that reflects that emotion is generated. In this way, by analyzing the user's tone of voice and facial expression and generating a voice message that corresponds to the emotion, a voice message that is in tune with the emotion can be provided.

[0062] The message receiving unit can analyze the user's handwritten message, digitize it, and send it. For example, the message receiving unit uses a generation AI to analyze the user's handwritten message, digitize it, and send it. For example, it recognizes handwritten characters, converts them into digital text, and sends it. In this way, by digitizing and sending the user's handwritten message, it can be sent in digital format while maintaining the warmth of handwriting.

[0063] The message receiving unit can analyze the emotions of the user when entering a message in real time and make suggestions to elicit positive emotions. For example, the message receiving unit uses an emotion estimation function to analyze the emotions of the user when entering a message in real time and make suggestions to elicit positive emotions. For example, if the user is feeling sad, an encouraging message is suggested. In this way, the emotions of the user when entering a message can be analyzed in real time and suggestions to elicit positive emotions can be made, thereby providing a message that is in line with the user's emotions.

[0064] The data sending unit can analyze the content of the message and automatically determine the optimal sending timing. For example, the generation AI analyzes the content of the message and automatically determines the optimal sending timing. For example, important messages can be sent immediately, and general messages can be sent later. This allows for effective message sending by analyzing the content of the message and automatically determining the optimal sending timing.

[0065] The data transmission unit can evaluate the importance of messages and prioritize them for transmission. For example, the generation AI evaluates the importance of messages and prioritizes them for transmission. For example, emotionally important messages are sent with priority. By evaluating the importance of messages and prioritizing their transmission, important messages can be sent with priority.

[0066] The data transmission unit can use the emotion estimation function to preferentially transmit emotionally important messages. The data transmission unit, for example, uses the emotion estimation function to preferentially transmit emotionally important messages. For example, the data transmission unit preferentially transmits messages expressing the user's gratitude. In this way, by using the emotion estimation function to preferentially transmit emotionally important messages, it becomes possible to transmit messages that are in line with the user's emotions.

[0067] The data transmission unit translates the contents of the message into multiple languages, making it possible to accommodate international users. For example, the generation AI translates the contents of the message into multiple languages, making it possible to accommodate international users. For example, it translates into multiple languages ​​such as English, Japanese, and French. This allows the contents of the message to be translated into multiple languages, making it possible to provide services to a wide range of users.

[0068] The data sending unit can analyze the content of the message and automatically add related images and audio. For example, the generation AI in the data sending unit analyzes the content of the message and automatically adds related images and audio. For example, an image expressing gratitude can be added to a thank-you message. This allows the content of the message to be analyzed and related images and audio automatically added, making it possible to provide a richer message.

[0069] The data transmission unit can use the emotion estimation function to suggest a message transmission method based on the user's emotion. The data transmission unit, for example, uses the emotion estimation function to suggest a message transmission method based on the user's emotion. For example, for a message expressing gratitude, a transmission method that emphasizes the gratitude is suggested. In this way, by using the emotion estimation function to suggest a message transmission method based on the user's emotion, it becomes possible to send a message that is in line with the emotion.

[0070] The data broadcasting unit can automatically adjust the frequency of message broadcasts and broadcast them at the optimal timing. For example, a small satellite can automatically adjust the frequency of message broadcasts and broadcast them at the optimal timing. For example, important messages can be broadcast frequently, and general messages can be broadcast at intervals. This allows the frequency of message broadcasts to be automatically adjusted and broadcast at the optimal timing, making it possible to broadcast messages effectively.

[0071] The data broadcasting unit can analyze the contents of messages and optimize the broadcast order. For example, a small satellite can analyze the contents of messages and optimize the broadcast order. For example, it can broadcast emotionally important messages with priority. This allows for effective message broadcasting by analyzing the contents of messages and optimizing the broadcast order.

[0072] The data broadcasting unit can use the emotion estimation function to broadcast emotionally important messages with priority. The data broadcasting unit, for example, uses the emotion estimation function to broadcast emotionally important messages with priority. For example, the data broadcasting unit broadcasts messages expressing gratitude by users with priority. This enables broadcasting messages that are in tune with emotions by using the emotion estimation function to broadcast emotionally important messages with priority.

[0073] The data broadcasting unit can analyze the content of a message and broadcast it by linking it to related constellations and celestial bodies. For example, a small satellite can analyze the content of a message and broadcast it by linking it to related constellations and celestial bodies. For example, a message of gratitude can be broadcast by linking it to a specific constellation. This makes it possible to broadcast a more moving message by analyzing the content of the message and broadcasting it by linking it to related constellations and celestial bodies.

[0074] The data broadcasting unit can analyze the content of the message and add visual effects during broadcast. For example, a small satellite can analyze the content of the message and add visual effects during broadcast. For example, an effect expressing gratitude can be added to a message of gratitude. In this way, by analyzing the content of the message and adding visual effects during broadcast, it becomes possible to broadcast a more moving message.

[0075] The data broadcasting unit can use the emotion estimation function to adjust broadcast content based on the user's emotions in real time. The data broadcasting unit, for example, uses the emotion estimation function to adjust broadcast content based on the user's emotions in real time. For example, for a message expressing gratitude, broadcast content is adjusted to emphasize the gratitude. In this way, by using the emotion estimation function to adjust broadcast content based on the user's emotions in real time, it becomes possible to broadcast messages that are in tune with the user's emotions.

[0076] The signal tracking unit can analyze signal strength and location information and suggest the optimal tracking method to the user. For example, the signal tracking unit uses a generation AI to analyze signal strength and location information and suggest the optimal tracking method to the user. For example, it can identify locations with strong signals and guide the user to those locations. This enables effective signal tracking by analyzing signal strength and location information and suggesting the optimal tracking method to the user.

[0077] The signal tracking unit can analyze the signal history and make predictions based on past tracking data. For example, the signal tracking unit uses a generation AI to analyze the signal history and make predictions based on past tracking data. For example, it predicts future signal strength based on fluctuations in signal strength in the past. This enables effective signal tracking by analyzing the signal history and making predictions based on past tracking data.

[0078] The signal tracking unit can use the emotion estimation function to provide a signal tracking interface based on the user's emotion. The signal tracking unit, for example, uses the emotion estimation function to provide a signal tracking interface based on the user's emotion. For example, if the user is feeling anxious, an interface that gives a sense of security is provided. In this way, by using the emotion estimation function to provide a signal tracking interface based on the user's emotion, signal tracking that is sensitive to the user's emotion becomes possible.

[0079] The signal tracking unit can analyze the location information of the signal and suggest the optimal observation point to the user. For example, the generation AI analyzes the location information of the signal and suggests the optimal observation point to the user. For example, it identifies a location where the signal is strong and guides the user to that location. This enables effective signal tracking by analyzing the location information of the signal and suggesting the optimal observation point to the user.

[0080] The signal tracking unit can analyze the signal strength and suggest the optimal receiving device to the user. For example, the signal tracking unit uses a generation AI to analyze the signal strength and suggest the optimal receiving device to the user. For example, if the signal is weak, a more sensitive receiving device will be suggested. This enables effective signal tracking by analyzing the signal strength and suggesting the optimal receiving device to the user.

[0081] The signal tracking unit can use the emotion estimation function to provide signal tracking feedback based on the user's emotion. The signal tracking unit, for example, uses the emotion estimation function to provide signal tracking feedback based on the user's emotion. For example, if the user is feeling anxious, feedback that gives a sense of security is provided. In this way, by using the emotion estimation function to provide signal tracking feedback based on the user's emotion, signal tracking that is sensitive to the user's emotion becomes possible.

[0082] The system monitors the status of the satellite in real time and can automatically make corrections if an abnormality is detected. For example, the system uses a generation AI to monitor the status of the satellite in real time and automatically make corrections if an abnormality is detected. For example, if a communication failure occurs, it will automatically attempt to reconnect. This allows the system to monitor the status of the satellite in real time and automatically make corrections if an abnormality is detected, enabling stable satellite operation.

[0083] The system can periodically back up satellite data to prevent data loss. For example, the generating AI can periodically back up satellite data to prevent data loss. For example, the data can be stored in the cloud on a daily basis. This ensures data safety by periodically backing up satellite data to prevent data loss.

[0084] The system can use the emotion estimation function to suggest the timing of data updates based on the user's emotions. For example, the system uses the emotion estimation function to suggest the timing of data updates based on the user's emotions. For example, if the user sends a message expressing gratitude, the system updates the data immediately. In this way, by using the emotion estimation function to suggest the timing of data updates based on the user's emotions, it becomes possible to update data in accordance with the user's emotions.

[0085] The system can automatically optimize satellite maintenance schedules and achieve efficient operations. For example, generative AI can automatically optimize satellite maintenance schedules and achieve efficient operations. For example, it can carry out regular maintenance at the optimal timing. This automatically optimizes satellite maintenance schedules and achieves efficient operations, enabling stable satellite operations.

[0086] The system can analyze satellite data and suggest the optimal data update method to the user. For example, the system uses a generative AI to analyze satellite data and suggest the optimal data update method to the user. For example, important data is updated immediately. This allows for efficient data updates by analyzing satellite data and suggesting the optimal data update method to the user.

[0087] The system can use the emotion estimation function to provide maintenance feedback based on the user's emotions. For example, the system uses the emotion estimation function to provide maintenance feedback based on the user's emotions. For example, if the user is feeling anxious, feedback that gives the user a sense of security is provided. In this way, by using the emotion estimation function to provide maintenance feedback based on the user's emotions, maintenance that is sensitive to the user's emotions becomes possible.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When receiving a user's message, the message receiving unit can automatically select relevant music based on the content of the message and send it along with the message. For example, music that expresses gratitude can be selected for a message of gratitude, and uplifting music can be selected for a message of encouragement. By automatically selecting music according to the content of the message, it is possible to provide a more moving message.

[0090] The message receiving unit can analyze the user's emotions in real time and automatically generate a message format that corresponds to the emotion. For example, if the user is feeling happy, the generation AI will generate a message with a bright tone that matches that emotion. On the other hand, if the user is feeling angry, it will generate a message with a calm tone that will soothe the emotion. In this way, by automatically generating a message format that corresponds to the user's emotions, it is possible to provide a message that is in tune with their emotions.

[0091] The message receiving unit can analyze the user's past message history and suggest the optimal message format and content. For example, it can suggest new messages based on the tone and style of messages sent in the past. It can also suggest related messages to users who frequently use specific keywords or phrases. This allows it to provide messages that are in line with the user's intentions by suggesting the optimal message format and content based on the user's past message history.

[0092] The message receiving unit can automatically adjust the tone and style of the message based on the user's emotions. For example, if the user is feeling happy, the generation AI will generate a message with a bright tone that matches that emotion. On the other hand, if the user is feeling sad, it will generate a message with a gentle tone that matches that emotion. In this way, by automatically adjusting the tone and style of the message based on the user's emotions, it is possible to provide a message that is in tune with their emotions.

[0093] The message receiving unit can analyze the user's tone of voice and facial expression and generate a voice message that corresponds to the emotion. For example, if the user is feeling happy, a voice message with a bright tone that reflects that emotion is generated. On the other hand, if the user is feeling angry, a voice message with a calm tone that soothes the emotion is generated. In this way, by analyzing the user's tone of voice and facial expression and generating a voice message that corresponds to the emotion, a voice message that is in tune with the emotion can be provided.

[0094] The message receiving unit can analyze a user's handwritten message, digitize it, and send it. For example, it can recognize handwritten characters and convert them into digital text before sending it. It can also analyze handwritten illustrations and drawings and send them in digital format. This allows a user's handwritten message to be digitized and sent in digital format while preserving the warmth of handwriting.

[0095] The message receiving unit can analyze the emotions of the user when they input a message in real time and make suggestions to elicit positive emotions. For example, if the user is feeling sad, it can suggest an encouraging message. Also, if the user is feeling anxious, it can suggest a message that gives a sense of security. In this way, by analyzing the emotions of the user when they input a message in real time and making suggestions to elicit positive emotions, it is possible to provide a message that is in line with the user's emotions.

[0096] The data transmission unit can analyze the content of a message and automatically determine the optimal transmission timing. For example, important messages can be sent immediately and general messages can be sent later. Messages can also be sent to coincide with specific events or anniversaries. This allows for effective message transmission by analyzing the content of the message and automatically determining the optimal transmission timing.

[0097] The data transmission unit can evaluate the importance of messages and prioritize them for transmission. For example, emotionally important messages can be sent with priority. Also, urgent messages can be sent immediately, and general messages can be sent later. In this way, by evaluating the importance of messages and prioritizing their transmission, important messages can be sent with priority.

[0098] The data transmission unit can use the emotion estimation function to preferentially transmit emotionally important messages. For example, a message expressing a user's gratitude is preferentially transmitted. Also, if a user wants to send a message of encouragement, the data transmission unit preferentially transmits that message. In this way, by using the emotion estimation function to preferentially transmit emotionally important messages, it becomes possible to transmit messages that are in tune with the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The message receiver receives messages, images, and audio from the user. For example, if the user sends a message such as "Thank you, Mom. I'll never forget your smile," the generation AI analyzes this message and converts it into an appropriate format. The message receiver also receives image and audio data sent by the user, which are then converted into an appropriate format by the generation AI. Step 2: The data transmitter transmits the received messages, images, and audio to a small satellite. For example, the generation AI transmits the received data to a small satellite and prepares it for broadcast in space. Step 3: The data broadcasting unit broadcasts the received message, image, and sound into space. For example, a message sent by a user can be broadcast into space, allowing the memory of the deceased to remain forever in the infinite expanse of space. Step 4: The signal tracking unit tracks the signals emitted by the small satellites. For example, users can use a dedicated application to check the satellite's position and feel connected to space.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 message receiving unit for receiving messages, images, and voices from users; a data transmission unit that transmits the message, image, and voice received by the message receiving unit to a small satellite; a data broadcasting unit that broadcasts the message, image, and sound transmitted by the data transmitting unit in outer space; a signal tracking unit that tracks the signal emitted by the small satellite. A system characterized by:

2. The message receiving unit The user's emotions are analyzed in real time, and a message format is automatically generated according to the emotions.

2. The system of claim 1.

3. The data transmission unit Analyze the contents of the message and automatically determine the optimal timing for sending it.

2. The system of claim 1.

4. The data broadcasting unit Automatically adjust the frequency of broadcasting the message and broadcast it at the optimal time.

2. The system of claim 1.

5. The signal tracking unit Analyzing the signal strength and location information and suggesting the optimal tracking method for the user 2. The system of claim 1.

6. The system comprises: Using emotion estimation function, the timing of data update is suggested based on the user's emotion.

2. The system of claim 1.

7. The message receiving unit Automatically adjusting the tone and style of the message based on the user's emotions.

2. The system of claim 1.

8. The data transmission unit Using an emotion estimation function, the emotionally significant messages are preferentially sent.

2. The system of claim 1.

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