system

Abstract

The system according to this embodiment aims to automatically generate and send personalized messages based on photos taken by the user. [Solution] The system according to the embodiment comprises a shooting unit, an analysis unit, a generation unit, and a transmission unit. The shooting unit receives a photograph taken by the user. The analysis unit analyzes the photograph received by the shooting unit. The generation unit generates a personalized message based on the content of the photograph analyzed by the analysis unit. The transmission unit transmits the message and photograph generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time and effort to create and send messages for confirmation of safety individually.

[0005] The system according to the embodiment aims to automatically generate and send a personalized message based on a photo taken by a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, an analysis unit, a generation unit, and a transmission unit. The shooting unit receives a photograph taken by the user. The analysis unit analyzes the photograph received by the shooting unit. The generation unit generates a personalized message based on the content of the photograph analyzed by the analysis unit. The transmission unit transmits the message and photograph generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and send personalized messages based on photos taken by the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The safety confirmation system according to an embodiment of the present invention is a system in which a generating AI personalizes and sends photos taken by the user and automatically generated messages to the person whose safety the user wishes to confirm. The safety confirmation system works as follows: the user takes a photo to send to the person whose safety the user wishes to confirm, the generating AI analyzes the photo, automatically generates a personalized message according to the recipient's name and situation, and sends the generated message and photo to the recipient. This mechanism allows the user to easily confirm the safety of others and send more personalized messages to them. For example, the safety confirmation system includes a "shooting unit" for the user to take a photo to send to the person whose safety the user wishes to confirm. Next, it includes an "analysis unit" for the generating AI to analyze the photo. The analysis unit analyzes the content of the photo and provides information to the "generation unit" which generates a message according to the recipient's name and situation. The generation unit generates a personalized message using the generating AI. Finally, it includes a "sending unit" that sends the generated message and photo to the recipient. In this way, the safety confirmation system can analyze a photo taken by the user, generate a personalized message, and send it.

[0029] The safety confirmation system according to this embodiment comprises a shooting unit, an analysis unit, a generation unit, and a transmission unit. The shooting unit receives photos taken by the user. The shooting unit can, for example, take photos using a smartphone or digital camera and input those photos into the system. The analysis unit analyzes the photos received by the shooting unit. The analysis unit can, for example, use image recognition technology to analyze the content of the photos and identify people or objects in the photos. The analysis unit can also use machine learning algorithms to analyze the content of the photos and provide the generation unit with information such as the recipient's name and situation. The generation unit generates a personalized message based on the content of the photos analyzed by the analysis unit. The generation unit automatically generates a message according to the recipient's name and situation using a generation AI. The generation unit can, for example, use a text generation AI (e.g., LLM) to generate a message according to the recipient's name and situation. The generation unit can also use a generation AI to generate a message according to the recipient's name and situation. The transmission unit sends the message and photos generated by the generation unit to the recipient. The transmission unit can, for example, send the generated message and photos to the recipient using email, social media, or a messaging app. The transmission unit is equipped with communication means for sending the generated message and photo to the recipient. This allows the safety confirmation system according to the embodiment to analyze a photo taken by the user, generate a personalized message, and send it.

[0030] The photography unit receives photos taken by users. For example, it can take photos using smartphones or digital cameras and input those photos into the system. Specifically, it has a mechanism that automatically uploads photos taken through a smartphone camera app to the system. Users can take photos using a dedicated application and send them directly to the system. In the case of digital cameras, after taking a photo, users can transfer the photo to a computer and upload it to the system using dedicated software. The photography unit receives photo data from these devices, converts it to an appropriate format, and makes it available for use within the system. Furthermore, the photography unit can simultaneously collect photo metadata (e.g., date and time of shooting, location information, camera settings, etc.) and provide it to the analysis unit. This allows the photography unit to easily input photos into the system and provides basic data for subsequent analysis and message generation.

[0031] The analysis unit analyzes the photographs received by the photography unit. For example, the analysis unit can analyze the content of a photograph using image recognition technology to identify people and objects in the photograph. Specifically, it utilizes deep learning-based image recognition algorithms to detect faces in photographs and uses face recognition technology to identify individuals. Furthermore, it can use object recognition technology to analyze the situation of objects and backgrounds in photographs to evaluate the situation and safety during a disaster. The analysis unit can also use machine learning algorithms to analyze the content of photographs and provide the generation unit with information such as the person's name and situation-specific details. For example, if a person in a photograph is performing a specific action, it can analyze that action and provide information to generate a message appropriate to the situation. The analysis unit can learn from past data and cases to perform more accurate analyses. As a result, the analysis unit plays a role in providing the basic information necessary for the generation unit to generate appropriate messages by analyzing the content of photographs in detail.

[0032] The generation unit generates personalized messages based on the content of the photos analyzed by the analysis unit. The generation unit uses generation AI to automatically generate messages tailored to the recipient's name and situation. Specifically, it can use text generation AI (e.g., LLM) to generate messages tailored to the recipient's name and situation. The generation AI generates messages in an appropriate context and tone based on the information provided by the analysis unit. For example, if it is confirmed that the person in the photo is safe, it will generate a message such as, "We have confirmed that [person's name] is safe. Please rest assured." If the situation is urgent, it can also generate a warning message such as, "[Person's name] may be in danger. Immediate action is required." The generation unit can also use generation AI to generate messages tailored to the recipient's name and situation. This allows the generation unit to automatically generate the most appropriate message for the user based on the information provided by the analysis unit and respond quickly.

[0033] The sending unit sends the message and photo generated by the generating unit to the recipient. The sending unit can send the generated message and photo to the recipient using, for example, email, social networking services (SNS), or messaging apps. Specifically, the sending unit selects the appropriate communication method based on the contact information specified by the user and sends the message and photo. In the case of email, the generated message and photo are sent as attachments so that the recipient can easily check them. In the case of SNS or messaging apps, the generated message and photo are sent directly, and notifications can be made in real time. The sending unit is equipped with a communication method for sending the generated message and photo to the recipient. Furthermore, the sending unit is also equipped with a function to monitor the transmission status and confirm whether the message has been sent successfully. As a result, the sending unit can deliver the generated message and photo to the recipient quickly and reliably, allowing the user to check on the safety of others with peace of mind.

[0034] The analysis unit can analyze the content of a photograph and provide the generation unit with information tailored to the recipient's name and situation. For example, the analysis unit can use image recognition technology to analyze the content of a photograph and identify people and objects in the photograph. The analysis unit can also use machine learning algorithms to analyze the content of a photograph and provide the generation unit with information tailored to the recipient's name and situation. This enables the generation of personalized messages by analyzing the content of a photograph and providing information tailored to the recipient's name and situation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the content of a photograph using an AI model that takes a photograph as input and outputs the recipient's name and situation.

[0035] The generation unit can generate personalized messages tailored to the recipient's name and situation. The generation unit can, for example, use a text generation AI (e.g., LLM) to generate messages tailored to the recipient's name and situation. The generation unit can also use a generation AI to generate messages tailored to the recipient's name and situation. This allows for the sending of more personalized messages by generating messages tailored to the recipient's name and situation. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate messages using a generation AI model that takes the recipient's name and situation as input and outputs a personalized message.

[0036] The transmitting unit can send the generated message and photo to the recipient. The transmitting unit sends the generated message and photo to the recipient, for example, using email, social networking services, or messaging apps. The transmitting unit is equipped with communication means for sending the generated message and photo to the recipient. This makes it easier to confirm the safety of the recipient by sending the generated message and photo to them. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can perform the transmission using an AI model that takes the generated message and photo as input and sends it to the recipient.

[0037] The shooting unit can analyze the user's past shooting history and suggest the optimal shooting method. For example, the shooting unit can automatically apply filters that the user has previously preferred to use. The shooting unit can also suggest optimal shooting settings considering the location and time of day the user has previously taken photos. The shooting unit can also analyze the composition of photos the user has previously taken and suggest similar compositions. In this way, by analyzing the user's past shooting history, the optimal shooting method can be suggested. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the user's past shooting data into a generating AI and have the generating AI suggest the optimal shooting method.

[0038] The shooting unit can automatically adjust shooting settings based on the user's current environment and circumstances. For example, the shooting unit can automatically adjust the camera's exposure and white balance according to lighting conditions. The shooting unit can also adjust the microphone sensitivity according to ambient noise levels. The shooting unit can also automatically enable image stabilization according to the user's movements. This allows for optimal photography by automatically adjusting shooting settings based on the user's current environment and circumstances. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input environmental data into a generating AI and have the generating AI perform the automatic adjustment of shooting settings.

[0039] The photography unit can suggest the optimal shooting location by considering the user's geographical location. For example, if the user is in a tourist area, the photography unit can suggest popular shooting spots. If the user is in a natural environment, the photography unit can also suggest places with beautiful scenery. If the user is in an urban area, the photography unit can also suggest shooting spots for buildings and cityscapes. In this way, the optimal shooting location can be suggested by considering the user's geographical location. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input the user's geographical location data into a generating AI and have the generating AI suggest the optimal shooting location.

[0040] The photography unit can analyze a user's social media activity and suggest relevant photography themes. For example, it can analyze themes that a user frequently posts about (food, scenery, etc.) and suggest relevant photography themes. It can also suggest themes that a user's followers might be interested in. It can also suggest new photography themes based on themes from posts that have received high ratings in the past. In this way, relevant photography themes can be suggested by analyzing a user's social media activity. Some or all of the above processing in the photography unit may be performed using AI, for example, or not using AI. For example, the photography unit can input a user's social media data into a generating AI and have the generating AI suggest relevant photography themes.

[0041] The analysis unit can identify the person's name and situation in more detail based on the content of the photograph. For example, the analysis unit can perform facial recognition on people in the photograph to identify their names. The analysis unit can also analyze the locations in the background of the photograph to identify the person's current situation. The analysis unit can also analyze objects in the photograph to identify the person's activities. This allows for the provision of more detailed information by identifying the person's name and situation based on the content of the photograph. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform the identification of the person's name and situation.

[0042] The analysis unit can improve the accuracy of the analysis by considering the background information of the photograph. For example, the analysis unit can perform the analysis by considering the geographical information of the location where the photograph was taken. The analysis unit can also perform the analysis by considering the date and time the photograph was taken, so as to reflect the conditions at that time. The analysis unit can also perform the analysis by considering the weather and lighting conditions shown in the photograph. In this way, the accuracy of the analysis is improved by considering the background information of the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the background information of the photograph into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0043] The analysis unit can perform analysis while considering the location information of the photograph. For example, the analysis unit can extract relevant information based on the geographical information of the location where the photograph was taken. The analysis unit can also perform analysis while considering the history and cultural background of the location where the photograph was taken. The analysis unit can also perform analysis that reflects the current situation of the location where the photograph was taken. This makes it possible to perform a more accurate analysis by considering the location information of the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the location information of the photograph into a generating AI and have the generating AI perform the analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the photograph. For example, the analysis unit performs its analysis by referring to relevant literature about the places and objects depicted in the photograph. The analysis unit can also perform its analysis by referring to research papers related to the content of the photograph. The analysis unit can also perform its analysis by referring to historical literature related to the background of the photograph. In this way, the accuracy of the analysis is improved by referring to relevant literature related to the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature related to the photograph into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The generation unit can generate the optimal message by referring to the recipient's past response history when generating a message. For example, the generation unit can generate a new message based on the pattern of messages to which the recipient has previously responded favorably. The generation unit can also generate a new message while avoiding the pattern of messages to which the recipient has previously not responded. The generation unit can also analyze the recipient's past response history and generate the most effective message. In this way, the optimal message can be generated by referring to the recipient's past response history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the recipient's past response data into a generation AI and have the generation AI perform the generation of the optimal message.

[0046] The generation unit can customize message content based on the recipient's current situation when generating a message. For example, if the recipient is busy, the generation unit can generate a concise and to-the-point message. If the recipient is relaxed, the generation unit can also generate a message containing detailed information. If the recipient is participating in a specific event, the generation unit can also generate a message related to that event. This allows for the generation of more appropriate messages by customizing the message content based on the recipient's current situation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the recipient's current situation data into the generation AI and have the generation AI perform the customization of the message content.

[0047] The generation unit can adjust the message content when generating a message, taking into account the recipient's geographical location. For example, if the recipient is in a specific city, the generation unit can generate a message containing information related to that city. If the recipient is traveling, the generation unit can also generate a message containing information related to their travel destination. If the recipient is at home, the generation unit can also generate a message containing information related to their home. This allows for the generation of more appropriate messages by considering the recipient's geographical location. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the recipient's geographical location data into the generation AI and have the generation AI perform the adjustment of the message content.

[0048] The generation unit can analyze the recipient's social media activity and generate relevant message content when generating a message. For example, the generation unit can generate a message related to the recipient's recent posts. The generation unit can also generate a message that includes content that the recipient's followers are likely to be interested in. The generation unit can also generate a new message based on themes from posts that the recipient has previously received high ratings for. In this way, relevant message content can be generated by analyzing the recipient's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the recipient's social media data into a generation AI and have the generation AI generate relevant message content.

[0049] The sending unit can select the optimal sending method by referring to the recipient's past receiving history when sending a message. For example, the sending unit may prioritize sending methods (email, messaging apps, etc.) that the recipient has previously responded favorably to. The sending unit can also avoid sending methods that the recipient has previously ignored and select a new sending method. The sending unit can also analyze the recipient's past receiving history and select the most effective sending method. This allows the sending unit to select the optimal sending method by referring to the recipient's past receiving history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the recipient's past receiving data into a generating AI and have the generating AI select the optimal sending method.

[0050] The sending unit can customize the message content based on the recipient's current situation at the time of transmission. For example, if the recipient is busy, the sending unit can send a concise and to-the-point message. If the recipient is relaxed, the sending unit can also send a message containing detailed information. If the recipient is participating in a specific event, the sending unit can also send a message related to that event. This allows for the sending of more appropriate messages by customizing the message content based on the recipient's current situation. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can input the recipient's current situation data into a generating AI and have the generating AI perform the customization of the message content.

[0051] The transmitting unit can select the optimal transmission method by considering the recipient's geographical location information at the time of transmission. For example, if the recipient is in a specific city, the transmitting unit can send a message containing information related to that city. If the recipient is traveling, the transmitting unit can also send a message containing information related to their travel destination. If the recipient is at home, the transmitting unit can also send a message containing information related to their home. This allows the transmitting unit to select the optimal transmission method by considering the recipient's geographical location information. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the recipient's geographical location data into a generating AI and have the generating AI select the optimal transmission method.

[0052] The sending unit can analyze the recipient's social media activity and suggest relevant content when sending a message. For example, the sending unit can send a message related to something the recipient has recently posted. The sending unit can also send a message that contains content that the recipient's followers might be interested in. The sending unit can also send a new message based on themes from posts that the recipient has previously received high ratings for. In this way, by analyzing the recipient's social media activity, it can suggest relevant content. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can input the recipient's social media data into a generating AI and have the generating AI suggest relevant content.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The safety confirmation system can also be equipped with a "notification unit." The notification unit has the function of notifying the user when the recipient receives a message. For example, it can send a real-time notification to the user when the recipient opens the message. It can also notify the user of the recipient's response if the recipient responds to the message. Furthermore, if the recipient receives the message but does not open it within a certain time, it can send a reminder notification to the user. This allows the user to quickly understand the recipient's response and take additional action as needed.

[0055] The safety confirmation system can also be equipped with a "translation unit." This unit has the function of translating generated messages into the recipient's native language. For example, if a user generates a message in English, and the recipient's native language is Japanese, the system can translate it into Japanese and send it. Furthermore, the translation unit can perform translations that take into account the recipient's culture and customs. In addition, the translation unit can perform translations that avoid simplification and technical jargon, depending on the recipient's level of understanding. This enables more effective communication across language barriers.

[0056] The safety confirmation system can also include a "scheduling unit." The scheduling unit has the function of scheduling the timing of message sending. For example, if a user wants to send a message at a specific date and time, they can set that date and time. It can also automatically adjust the optimal sending timing considering the recipient's time zone. Furthermore, the scheduling unit can also schedule the sending of periodic safety confirmation messages. This allows users to perform periodic safety confirmations without any extra effort.

[0057] The safety confirmation system can also be equipped with a "health monitoring unit." The health monitoring unit has the function of monitoring the health status of the other party and notifying the user if an abnormality is detected. For example, if the other party is using a wearable device, it can monitor data such as heart rate and blood pressure obtained from that device. Also, if the other party undergoes regular health checks, the system can collect the results and notify the user if there is an abnormality. Furthermore, the health monitoring unit can also provide appropriate advice and support based on the other party's health status. This allows the user to understand the other party's health status and take necessary actions quickly.

[0058] The safety confirmation system can also be equipped with a "learning support unit." The learning support unit has the function of monitoring the user's learning status and providing appropriate learning support. For example, if the user is a student, it can monitor their learning progress and provide necessary support. It can also provide learning resources related to a specific skill if the user is learning that skill. Furthermore, the learning support unit can send motivational messages based on the user's learning status. This effectively supports the user's learning and improves their learning outcomes.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The shooting unit receives photos taken by the user. The shooting unit can take photos using, for example, a smartphone or digital camera and input those photos into the system. Step 2: The analysis unit analyzes the photograph received by the shooting unit. The analysis unit can, for example, use image recognition technology to analyze the content of the photograph and identify people or objects in the photograph. The analysis unit can also use machine learning algorithms to analyze the content of the photograph and provide the generation unit with information such as the names of the people and information appropriate to the situation. Step 3: The generation unit generates a personalized message based on the content of the photo analyzed by the analysis unit. The generation unit uses a generation AI to automatically generate a message tailored to the recipient's name and situation. For example, the generation unit can use a text generation AI (e.g., LLM) to generate a message tailored to the recipient's name and situation. Step 4: The sending unit sends the message and photo generated by the generating unit to the recipient. The sending unit can send the generated message and photo to the recipient using, for example, email, social networking services, or messaging apps. The sending unit is equipped with communication means for sending the generated message and photo to the recipient.

[0061] (Example of form 2) The safety confirmation system according to an embodiment of the present invention is a system in which a generating AI personalizes and sends photos taken by the user and automatically generated messages to the person whose safety the user wishes to confirm. The safety confirmation system works as follows: the user takes a photo to send to the person whose safety the user wishes to confirm, the generating AI analyzes the photo, automatically generates a personalized message according to the recipient's name and situation, and sends the generated message and photo to the recipient. This mechanism allows the user to easily confirm the safety of others and send more personalized messages to them. For example, the safety confirmation system includes a "shooting unit" for the user to take a photo to send to the person whose safety the user wishes to confirm. Next, it includes an "analysis unit" for the generating AI to analyze the photo. The analysis unit analyzes the content of the photo and provides information to the "generation unit" which generates a message according to the recipient's name and situation. The generation unit generates a personalized message using the generating AI. Finally, it includes a "sending unit" that sends the generated message and photo to the recipient. In this way, the safety confirmation system can analyze a photo taken by the user, generate a personalized message, and send it.

[0062] The safety confirmation system according to this embodiment comprises a shooting unit, an analysis unit, a generation unit, and a transmission unit. The shooting unit receives photos taken by the user. The shooting unit can, for example, take photos using a smartphone or digital camera and input those photos into the system. The analysis unit analyzes the photos received by the shooting unit. The analysis unit can, for example, use image recognition technology to analyze the content of the photos and identify people or objects in the photos. The analysis unit can also use machine learning algorithms to analyze the content of the photos and provide the generation unit with information such as the recipient's name and situation. The generation unit generates a personalized message based on the content of the photos analyzed by the analysis unit. The generation unit automatically generates a message according to the recipient's name and situation using a generation AI. The generation unit can, for example, use a text generation AI (e.g., LLM) to generate a message according to the recipient's name and situation. The generation unit can also use a generation AI to generate a message according to the recipient's name and situation. The transmission unit sends the message and photos generated by the generation unit to the recipient. The transmission unit can, for example, send the generated message and photos to the recipient using email, social media, or a messaging app. The transmission unit is equipped with communication means for sending the generated message and photo to the recipient. This allows the safety confirmation system according to the embodiment to analyze a photo taken by the user, generate a personalized message, and send it.

[0063] The photography unit receives photos taken by users. For example, it can take photos using smartphones or digital cameras and input those photos into the system. Specifically, it has a mechanism that automatically uploads photos taken through a smartphone camera app to the system. Users can take photos using a dedicated application and send them directly to the system. In the case of digital cameras, after taking a photo, users can transfer the photo to a computer and upload it to the system using dedicated software. The photography unit receives photo data from these devices, converts it to an appropriate format, and makes it available for use within the system. Furthermore, the photography unit can simultaneously collect photo metadata (e.g., date and time of shooting, location information, camera settings, etc.) and provide it to the analysis unit. This allows the photography unit to easily input photos into the system and provides basic data for subsequent analysis and message generation.

[0064] The analysis unit analyzes the photographs received by the photography unit. For example, the analysis unit can analyze the content of a photograph using image recognition technology to identify people and objects in the photograph. Specifically, it utilizes deep learning-based image recognition algorithms to detect faces in photographs and uses face recognition technology to identify individuals. Furthermore, it can use object recognition technology to analyze the situation of objects and backgrounds in photographs to evaluate the situation and safety during a disaster. The analysis unit can also use machine learning algorithms to analyze the content of photographs and provide the generation unit with information such as the person's name and situation-specific details. For example, if a person in a photograph is performing a specific action, it can analyze that action and provide information to generate a message appropriate to the situation. The analysis unit can learn from past data and cases to perform more accurate analyses. As a result, the analysis unit plays a role in providing the basic information necessary for the generation unit to generate appropriate messages by analyzing the content of photographs in detail.

[0065] The generation unit generates personalized messages based on the content of the photos analyzed by the analysis unit. The generation unit uses generation AI to automatically generate messages tailored to the recipient's name and situation. Specifically, it can use text generation AI (e.g., LLM) to generate messages tailored to the recipient's name and situation. The generation AI generates messages in an appropriate context and tone based on the information provided by the analysis unit. For example, if it is confirmed that the person in the photo is safe, it will generate a message such as, "We have confirmed that [person's name] is safe. Please rest assured." If the situation is urgent, it can also generate a warning message such as, "[Person's name] may be in danger. Immediate action is required." The generation unit can also use generation AI to generate messages tailored to the recipient's name and situation. This allows the generation unit to automatically generate the most appropriate message for the user based on the information provided by the analysis unit and respond quickly.

[0066] The sending unit sends the message and photo generated by the generating unit to the recipient. The sending unit can send the generated message and photo to the recipient using, for example, email, social networking services (SNS), or messaging apps. Specifically, the sending unit selects the appropriate communication method based on the contact information specified by the user and sends the message and photo. In the case of email, the generated message and photo are sent as attachments so that the recipient can easily check them. In the case of SNS or messaging apps, the generated message and photo are sent directly, and notifications can be made in real time. The sending unit is equipped with a communication method for sending the generated message and photo to the recipient. Furthermore, the sending unit is also equipped with a function to monitor the transmission status and confirm whether the message has been sent successfully. As a result, the sending unit can deliver the generated message and photo to the recipient quickly and reliably, allowing the user to check on the safety of others with peace of mind.

[0067] The analysis unit can analyze the content of a photograph and provide the generation unit with information tailored to the recipient's name and situation. For example, the analysis unit can use image recognition technology to analyze the content of a photograph and identify people and objects in the photograph. The analysis unit can also use machine learning algorithms to analyze the content of a photograph and provide the generation unit with information tailored to the recipient's name and situation. This enables the generation of personalized messages by analyzing the content of a photograph and providing information tailored to the recipient's name and situation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the content of a photograph using an AI model that takes a photograph as input and outputs the recipient's name and situation.

[0068] The generation unit can generate personalized messages tailored to the recipient's name and situation. The generation unit can, for example, use a text generation AI (e.g., LLM) to generate messages tailored to the recipient's name and situation. The generation unit can also use a generation AI to generate messages tailored to the recipient's name and situation. This allows for the sending of more personalized messages by generating messages tailored to the recipient's name and situation. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate messages using a generation AI model that takes the recipient's name and situation as input and outputs a personalized message.

[0069] The transmitting unit can send the generated message and photo to the recipient. The transmitting unit sends the generated message and photo to the recipient, for example, using email, social networking services, or messaging apps. The transmitting unit is equipped with communication means for sending the generated message and photo to the recipient. This makes it easier to confirm the safety of the recipient by sending the generated message and photo to them. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can perform the transmission using an AI model that takes the generated message and photo as input and sends it to the recipient.

[0070] The camera unit can estimate the user's emotions and adjust the timing of the shot based on the estimated emotions. For example, if the user is nervous, the camera unit may delay the shot until the user relaxes. If the user is having fun, the camera unit may take a shot immediately to capture that moment. If the user is sad, the camera unit may wait until the user's emotions have calmed down before taking a shot. By adjusting the timing of the shot according to the user's emotions, more appropriate photos can be taken. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The shooting unit can analyze the user's past shooting history and suggest the optimal shooting method. For example, the shooting unit can automatically apply filters that the user has previously preferred to use. The shooting unit can also suggest optimal shooting settings considering the location and time of day the user has previously taken photos. The shooting unit can also analyze the composition of photos the user has previously taken and suggest similar compositions. In this way, by analyzing the user's past shooting history, the optimal shooting method can be suggested. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input the user's past shooting data into a generating AI and have the generating AI suggest the optimal shooting method.

[0072] The shooting unit can automatically adjust shooting settings based on the user's current environment and circumstances. For example, the shooting unit can automatically adjust the camera's exposure and white balance according to lighting conditions. The shooting unit can also adjust the microphone sensitivity according to ambient noise levels. The shooting unit can also automatically enable image stabilization according to the user's movements. This allows for optimal photography by automatically adjusting shooting settings based on the user's current environment and circumstances. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input environmental data into a generating AI and have the generating AI perform the automatic adjustment of shooting settings.

[0073] The camera unit can estimate the user's emotions and determine the priority of photos to take based on the estimated emotions. For example, if the user is happy, the camera unit will prioritize taking photos of the user smiling. If the user is surprised, the camera unit can also take a photo immediately to capture that moment. If the user is calm, the camera unit can also prioritize taking photos of landscapes or still lifes. This allows for the capture of more appropriate photos by prioritizing photos based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The photography unit can suggest the optimal shooting location by considering the user's geographical location. For example, if the user is in a tourist area, the photography unit can suggest popular shooting spots. If the user is in a natural environment, the photography unit can also suggest places with beautiful scenery. If the user is in an urban area, the photography unit can also suggest shooting spots for buildings and cityscapes. In this way, the optimal shooting location can be suggested by considering the user's geographical location. Some or all of the above processing in the photography unit may be performed using AI, for example, or without AI. For example, the photography unit can input the user's geographical location data into a generating AI and have the generating AI suggest the optimal shooting location.

[0075] The photography unit can analyze a user's social media activity and suggest relevant photography themes. For example, it can analyze themes that a user frequently posts about (food, scenery, etc.) and suggest relevant photography themes. It can also suggest themes that a user's followers might be interested in. It can also suggest new photography themes based on themes from posts that have received high ratings in the past. In this way, relevant photography themes can be suggested by analyzing a user's social media activity. Some or all of the above processing in the photography unit may be performed using AI, for example, or not using AI. For example, the photography unit can input a user's social media data into a generating AI and have the generating AI suggest relevant photography themes.

[0076] The analysis unit can estimate the user's emotions and adjust the photo analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and extract finer information. If the user is in a hurry, the analysis unit can also perform a simplified analysis and extract only the main information. If the user is excited, the analysis unit can also prioritize visually prominent elements in the analysis. This allows for more appropriate analysis by adjusting the photo analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0077] The analysis unit can identify the person's name and situation in more detail based on the content of the photograph. For example, the analysis unit can perform facial recognition on people in the photograph to identify their names. The analysis unit can also analyze the locations in the background of the photograph to identify the person's current situation. The analysis unit can also analyze objects in the photograph to identify the person's activities. This allows for the provision of more detailed information by identifying the person's name and situation based on the content of the photograph. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform the identification of the person's name and situation.

[0078] The analysis unit can improve the accuracy of the analysis by considering the background information of the photograph. For example, the analysis unit can perform the analysis by considering the geographical information of the location where the photograph was taken. The analysis unit can also perform the analysis by considering the date and time the photograph was taken, so as to reflect the conditions at that time. The analysis unit can also perform the analysis by considering the weather and lighting conditions shown in the photograph. In this way, the accuracy of the analysis is improved by considering the background information of the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the background information of the photograph into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0080] The analysis unit can perform analysis while considering the location information of the photograph. For example, the analysis unit can extract relevant information based on the geographical information of the location where the photograph was taken. The analysis unit can also perform analysis while considering the history and cultural background of the location where the photograph was taken. The analysis unit can also perform analysis that reflects the current situation of the location where the photograph was taken. This makes it possible to perform a more accurate analysis by considering the location information of the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the location information of the photograph into a generating AI and have the generating AI perform the analysis.

[0081] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the photograph. For example, the analysis unit performs its analysis by referring to relevant literature about the places and objects depicted in the photograph. The analysis unit can also perform its analysis by referring to research papers related to the content of the photograph. The analysis unit can also perform its analysis by referring to historical literature related to the background of the photograph. In this way, the accuracy of the analysis is improved by referring to relevant literature related to the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature related to the photograph into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0082] The generation unit can estimate the user's emotions and adjust the way the message is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a message using friendly language. If the user is in a hurry, the generation unit can also generate a concise and to-the-point message. If the user is excited, the generation unit can also generate a message using emotionally emphasized language. In this way, by adjusting the way the message is expressed based on the user's emotions, a more appropriate message can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0083] The generation unit can generate the optimal message by referring to the recipient's past response history when generating a message. For example, the generation unit can generate a new message based on the pattern of messages to which the recipient has previously responded favorably. The generation unit can also generate a new message while avoiding the pattern of messages to which the recipient has previously not responded. The generation unit can also analyze the recipient's past response history and generate the most effective message. In this way, the optimal message can be generated by referring to the recipient's past response history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the recipient's past response data into a generation AI and have the generation AI perform the generation of the optimal message.

[0084] The generation unit can customize message content based on the recipient's current situation when generating a message. For example, if the recipient is busy, the generation unit can generate a concise and to-the-point message. If the recipient is relaxed, the generation unit can also generate a message containing detailed information. If the recipient is participating in a specific event, the generation unit can also generate a message related to that event. This allows for the generation of more appropriate messages by customizing the message content based on the recipient's current situation. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the recipient's current situation data into the generation AI and have the generation AI perform the customization of the message content.

[0085] The generation unit can estimate the user's emotions and adjust the message length based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise message. If the user is relaxed, the generation unit can also generate a longer message with detailed explanations. If the user is excited, the generation unit can also generate a longer message that emphasizes emotions. By adjusting the message length based on the user's emotions, a more appropriate message can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0086] The generation unit can adjust the message content when generating a message, taking into account the recipient's geographical location. For example, if the recipient is in a specific city, the generation unit can generate a message containing information related to that city. If the recipient is traveling, the generation unit can also generate a message containing information related to their travel destination. If the recipient is at home, the generation unit can also generate a message containing information related to their home. This allows for the generation of more appropriate messages by considering the recipient's geographical location. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the recipient's geographical location data into the generation AI and have the generation AI perform the adjustment of the message content.

[0087] The generation unit can analyze the recipient's social media activity and generate relevant message content when generating a message. For example, the generation unit can generate a message related to the recipient's recent posts. The generation unit can also generate a message that includes content that the recipient's followers are likely to be interested in. The generation unit can also generate a new message based on themes from posts that the recipient has previously received high ratings for. In this way, relevant message content can be generated by analyzing the recipient's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the recipient's social media data into a generation AI and have the generation AI generate relevant message content.

[0088] The transmission unit can estimate the user's emotions and adjust the transmission timing based on the estimated emotions. For example, if the user is tense, the transmission unit may delay transmission until the user relaxes. If the user is having fun, the transmission unit may also transmit immediately to capture that moment. If the user is sad, the transmission unit may also wait until the user's emotions have calmed down before transmitting. By adjusting the transmission timing based on the user's emotions, messages can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmission unit may be performed using AI, for example, or not using AI. For example, the transmission unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The sending unit can select the optimal sending method by referring to the recipient's past receiving history when sending a message. For example, the sending unit may prioritize sending methods (email, messaging apps, etc.) that the recipient has previously responded favorably to. The sending unit can also avoid sending methods that the recipient has previously ignored and select a new sending method. The sending unit can also analyze the recipient's past receiving history and select the most effective sending method. This allows the sending unit to select the optimal sending method by referring to the recipient's past receiving history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the recipient's past receiving data into a generating AI and have the generating AI select the optimal sending method.

[0090] The sending unit can customize the message content based on the recipient's current situation at the time of transmission. For example, if the recipient is busy, the sending unit can send a concise and to-the-point message. If the recipient is relaxed, the sending unit can also send a message containing detailed information. If the recipient is participating in a specific event, the sending unit can also send a message related to that event. This allows for the sending of more appropriate messages by customizing the message content based on the recipient's current situation. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can input the recipient's current situation data into a generating AI and have the generating AI perform the customization of the message content.

[0091] The sending unit can estimate the user's emotions and determine the priority of messages to send based on the estimated emotions. For example, if the user is happy, the sending unit will prioritize sending a message of gratitude. If the user is surprised, the sending unit can also send a message immediately to capture that moment. If the user is calm, the sending unit can also prioritize sending a message containing detailed information. This allows for the sending of more appropriate messages by prioritizing messages based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI or not. For example, the sending unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0092] The transmitting unit can select the optimal transmission method by considering the recipient's geographical location information at the time of transmission. For example, if the recipient is in a specific city, the transmitting unit can send a message containing information related to that city. If the recipient is traveling, the transmitting unit can also send a message containing information related to their travel destination. If the recipient is at home, the transmitting unit can also send a message containing information related to their home. This allows the transmitting unit to select the optimal transmission method by considering the recipient's geographical location information. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the recipient's geographical location data into a generating AI and have the generating AI select the optimal transmission method.

[0093] The sending unit can analyze the recipient's social media activity and suggest relevant content when sending a message. For example, the sending unit can send a message related to something the recipient has recently posted. The sending unit can also send a message that contains content that the recipient's followers might be interested in. The sending unit can also send a new message based on themes from posts that the recipient has previously received high ratings for. In this way, by analyzing the recipient's social media activity, it can suggest relevant content. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can input the recipient's social media data into a generating AI and have the generating AI suggest relevant content.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The safety confirmation system can also be equipped with a "notification unit." The notification unit has the function of notifying the user when the recipient receives a message. For example, it can send a real-time notification to the user when the recipient opens the message. It can also notify the user of the recipient's response if the recipient responds to the message. Furthermore, if the recipient receives the message but does not open it within a certain time, it can send a reminder notification to the user. This allows the user to quickly understand the recipient's response and take additional action as needed.

[0096] The safety confirmation system can also be equipped with a "translation unit." This unit has the function of translating generated messages into the recipient's native language. For example, if a user generates a message in English, and the recipient's native language is Japanese, the system can translate it into Japanese and send it. Furthermore, the translation unit can perform translations that take into account the recipient's culture and customs. In addition, the translation unit can perform translations that avoid simplification and technical jargon, depending on the recipient's level of understanding. This enables more effective communication across language barriers.

[0097] The safety confirmation system can also be equipped with a "feedback unit." The feedback unit has the function of collecting feedback from the recipient and providing it to the user. For example, it can collect feedback on how the recipient felt about the message. It can also provide the user with specific comments on the content of the message. Furthermore, the feedback unit can analyze the recipient's reaction and provide information that can be used to create future messages. This allows the user to understand the recipient's reaction and use it as a reference to create more effective messages.

[0098] The safety confirmation system can also include a "scheduling unit." The scheduling unit has the function of scheduling the timing of message sending. For example, if a user wants to send a message at a specific date and time, they can set that date and time. It can also automatically adjust the optimal sending timing considering the recipient's time zone. Furthermore, the scheduling unit can also schedule the sending of periodic safety confirmation messages. This allows users to perform periodic safety confirmations without any extra effort.

[0099] The safety confirmation system can also be equipped with an "emergency notification unit." The emergency notification unit has the function of sending an emergency notification to the user when the other party is in an emergency. For example, if the other party sends an emergency message such as "Help" in response to a message, it can immediately send a notification to the user. It can also acquire the other party's location information and provide information to support emergency response. Furthermore, the emergency notification unit can monitor the other party's health status and safety information in real time and send a notification if an abnormality is detected. This allows the user to respond quickly to the other party's emergency.

[0100] The safety confirmation system can also be equipped with an "Entertainment Section." This section has the function of adding entertainment elements to messages sent to recipients. For example, it can include humor and jokes in messages. It can also include quizzes and games tailored to the recipient's interests and hobbies. Furthermore, the Entertainment Section can estimate the recipient's emotions and provide entertainment elements that correspond to those emotions. This can provide enjoyment and pleasure to message recipients, enriching communication.

[0101] The safety confirmation system can also be equipped with a "health monitoring unit." The health monitoring unit has the function of monitoring the health status of the other party and notifying the user if an abnormality is detected. For example, if the other party is using a wearable device, it can monitor data such as heart rate and blood pressure obtained from that device. Also, if the other party undergoes regular health checks, the system can collect the results and notify the user if there is an abnormality. Furthermore, the health monitoring unit can also provide appropriate advice and support based on the other party's health status. This allows the user to understand the other party's health status and take necessary actions quickly.

[0102] The safety confirmation system can also be equipped with a "learning support unit." The learning support unit has the function of monitoring the user's learning status and providing appropriate learning support. For example, if the user is a student, it can monitor their learning progress and provide necessary support. It can also provide learning resources related to a specific skill if the user is learning that skill. Furthermore, the learning support unit can send motivational messages based on the user's learning status. This effectively supports the user's learning and improves their learning outcomes.

[0103] The safety confirmation system can also be equipped with a "gratitude function." The gratitude function has the function of generating messages to express gratitude to the recipient. For example, if the recipient takes a specific action, it can automatically generate a message of gratitude for that action. It can also generate messages of gratitude for things the recipient does on a daily basis. Furthermore, the gratitude function can estimate the recipient's emotions and provide a message of gratitude that corresponds to those emotions. This makes it possible to effectively convey gratitude to the recipient and build good relationships.

[0104] The safety confirmation system can also be equipped with a "reminder function." The reminder function has the function of reminding the user or the recipient of important events and tasks. For example, it can remind the recipient of the time to take medication that they take regularly. It can also remind the recipient of the time of an event or meeting they are scheduled to attend. Furthermore, the reminder function can estimate the recipient's emotions and provide a reminder method that is appropriate to those emotions. This helps the recipient to remember and carry out important events and tasks.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The shooting unit receives photos taken by the user. The shooting unit can take photos using, for example, a smartphone or digital camera and input those photos into the system. Step 2: The analysis unit analyzes the photograph received by the shooting unit. The analysis unit can, for example, use image recognition technology to analyze the content of the photograph and identify people or objects in the photograph. The analysis unit can also use machine learning algorithms to analyze the content of the photograph and provide the generation unit with information such as the names of the people and information appropriate to the situation. Step 3: The generation unit generates a personalized message based on the content of the photo analyzed by the analysis unit. The generation unit uses a generation AI to automatically generate a message tailored to the recipient's name and situation. For example, the generation unit can use a text generation AI (e.g., LLM) to generate a message tailored to the recipient's name and situation. Step 4: The sending unit sends the message and photo generated by the generating unit to the recipient. The sending unit can send the generated message and photo to the recipient using, for example, email, social networking services, or messaging apps. The sending unit is equipped with communication means for sending the generated message and photo to the recipient.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] Each of the multiple elements described above, including the shooting unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit takes a photograph using the camera 42 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the photograph. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized message using a generation AI. The transmission unit transmits the generated message and photograph to the recipient using the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 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.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the imaging unit takes a photograph using the camera 42 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the photograph. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized message using a generation AI. The transmission unit transmits the generated message and photograph to the recipient using the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the imaging unit takes a photograph using the camera 42 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the photograph. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized message using a generation AI. The transmission unit transmits the generated message and photograph to the recipient using the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the imaging unit, analysis unit, generation unit, and transmission unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the imaging unit takes a photograph using the camera 42 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the photograph. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a personalized message using a generation AI. The transmission unit transmits the generated message and photograph to the recipient using the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) The photography department accepts photos taken by users, An analysis unit that analyzes the photographs received by the aforementioned shooting unit, A generation unit that generates a personalized message based on the content of the photograph analyzed by the analysis unit, The system includes a transmission unit that transmits the message and photograph generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system analyzes the content of the photograph and provides the generator with information such as the person's name and details relevant to the situation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate personalized messages based on the recipient's name and situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned transmitting unit Send the generated message and photo to the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned imaging unit is It analyzes the user's past shooting history and suggests the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is Automatically adjusts shooting settings based on the user's current environment and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of photos to take based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is It suggests the optimal shooting location considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is Analyze users' social media activity and suggest relevant photography themes. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the photo analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Based on the content of the photograph, we can identify the person's name and situation in more detail. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Improve the accuracy of the analysis by taking background information from the photograph into account. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The analysis will take into account the location information of the photograph. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Referencing relevant literature on photographs improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the way messages are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a message, the system refers to the recipient's past response history to generate the most suitable message. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a message, customize the message content based on the recipient's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the message length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a message, the message content is adjusted to take the recipient's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a message, the system analyzes the recipient's social media activity and generates relevant message content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned transmitting unit It estimates the user's emotions and adjusts the sending timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned transmitting unit When sending a message, the system selects the optimal sending method by referring to the recipient's past receiving history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned transmitting unit When sending a message, customize the content based on the recipient's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned transmitting unit It estimates the user's emotions and determines the priority of messages to send based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transmitting unit When sending a message, the system selects the optimal transmission method by considering the recipient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transmitting unit When sending a message, the system analyzes the recipient's social media activity and suggests relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography department accepts photos taken by users, An analysis unit that analyzes the photographs received by the aforementioned shooting unit, A generation unit that generates a personalized message based on the content of the photograph analyzed by the analysis unit, The system includes a transmission unit that transmits the message and photograph generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, The system analyzes the content of the photograph and provides the generation unit with information such as the person's name and details relevant to the situation. The system according to feature 1.

3. The generating unit is Generate personalized messages based on the recipient's name and situation. The system according to feature 1.

4. The aforementioned transmitting unit Send the generated message and photo to the recipient. The system according to feature 1.

5. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

6. The aforementioned imaging unit is It analyzes the user's past shooting history and suggests the optimal shooting method. The system according to feature 1.

7. The aforementioned imaging unit is Automatically adjusts shooting settings based on the user's current environment and circumstances. The system according to feature 1.

8. The aforementioned imaging unit is It estimates the user's emotions and determines the priority of photos to take based on the estimated user emotions. The system according to feature 1.

Images