system

Abstract

The system according to this embodiment aims to enable people with disabilities and the elderly to communicate smoothly with support centers. [Solution] The system according to the embodiment comprises a reception unit, a follow-up unit, and a conversion unit. The reception unit receives a call from the user when they open their tablet and call the support center. The follow-up unit follows up on the call received by the reception unit via video call. The conversion unit automatically converts the content of the communication conducted by the follow-up unit into text.

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 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 is difficult for people with disabilities or the elderly to communicate smoothly with a support center. [[ID=3৬]]

[0005] The system according to the embodiment aims to enable people with disabilities or the elderly to communicate smoothly with a support center.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a follow-up unit, and a conversion unit. The reception unit receives a call from a user who opens a tablet and makes a call to the support center. The follow-up unit follows up on the call received by the reception unit via video call. The conversion unit automatically converts the content of the communication conducted by the follow-up unit into text. [Effects of the Invention]

[0007] The system according to this embodiment allows people with disabilities and the elderly to communicate smoothly with the support center. [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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 communication follow-up system according to an embodiment of the present invention is a system that provides remote communication follow-up using images (sign language, text) and audio (subtitles) to customers who have a tablet contract, by members of the support center who have experience in disability care. The communication follow-up system is available to tablet contract holders on a monthly contract + additional option basis. This system is intended for people who are deaf, blind, or have speech impairments. When a user opens their tablet and calls the support center, a member of the support center will follow up on the user's communication via video call using sign language, text, or audio. Specifically, it consists of the following steps: First, the user opens their tablet and calls the support center. Next, a member of the support center will follow up on the communication via video call using sign language, text, or audio. For example, they will use sign language or text for people who are deaf, audio for people who are blind, and text or sign language for people with speech impairments. Furthermore, the system also provides a function that uses generation AI to automatically convert what is being said into text (subtitles). In the future, we aim to develop technologies that can convert sign language directly into speech and subtitles without any time lag, as well as apps for finger braille, enabling users to choose the most suitable communication method. This system will allow people who have difficulty communicating to have someone translate what everyone is saying for them, and to express their own thoughts and feelings. We also see this as an investment in contributing to society and building a foundation for the SDGs, and in making people happier through the information revolution. As a result, the communication follow-up system will be able to provide remote communication support to people with disabilities, or those who have difficulty communicating due to illness or aging.

[0029] The communication follow-up system according to the embodiment comprises a reception unit, a follow-up unit, and a conversion unit. The reception unit receives a call from a user who opens a tablet and makes a call to the support center. When the user opens the tablet, the reception unit can make a call to the support center, for example, by tapping a button displayed on the tablet screen. The reception unit may include AI processing and can, for example, recognize the user's voice command and make a call. The follow-up unit follows up on the communication via video call based on the call received by the reception unit. The follow-up unit can, for example, start a video call with a member of the support center by tapping a video call start button. The follow-up unit may include AI processing and can, for example, recognize the user's voice command and start a video call. The conversion unit automatically converts the communication content made by the follow-up unit into text. The conversion unit includes generation AI processing and can, for example, use speech recognition technology to convert what is being said into text in real time. The conversion unit can, for example, use generation AI to automatically convert what is being said into text and display it as subtitles. As a result, the communication follow-up system according to the embodiment allows users to call a support center via tablet, receive follow-up via video call, and automatically convert the communication content into text.

[0030] The reception desk allows users to call the support center by opening a tablet. When a user opens the tablet, the reception desk can, for example, make a call to the support center by tapping a button displayed on the tablet screen. Specifically, a dedicated application is installed on the tablet's home screen, and when this application is launched, a call button to the support center appears. By tapping this button, the user is immediately connected to the support center. Furthermore, the reception desk may also incorporate AI processing, for example, by recognizing the user's voice command and making a call. To recognize voice commands, the microphone built into the tablet is used, and voice recognition technology is utilized. When a user utters a voice command such as "Call the support center," the AI ​​analyzes the voice and automatically starts the call. This voice recognition technology has a noise-canceling function, which eliminates ambient noise and can accurately recognize voice commands. The reception desk also has a function that refers to the user's past call history and prioritizes displaying frequently asked questions and assigned personnel. This allows users to receive support quickly and efficiently.

[0031] The follow-up department provides communication follow-up via video call based on calls received by the reception department. For example, the follow-up department allows users to initiate a video call with a support center member by tapping a "start video call" button. Specifically, after the reception department receives a call, the follow-up department automatically prepares for the video call and displays a "start video call" button on the user's screen. Tapping this button initiates a video call with a support center representative. The follow-up department may also incorporate AI processing, for example, by recognizing user voice commands to initiate a video call. When using voice commands, if the user says something like "start a video call," the AI ​​analyzes the voice and automatically starts the video call. Furthermore, the follow-up department also has the ability to analyze the user's facial expressions and tone of voice during the video call to understand their emotional state. This allows support center representatives to respond appropriately to the user's emotional state. The follow-up department also monitors the network status in real time and adjusts image and sound quality as needed to optimize video call quality. This ensures that users always receive support through high-quality video calls.

[0032] The conversion unit automatically converts the communication content conducted by the follow-up unit into text. The conversion unit includes generation AI processing, and can, for example, use speech recognition technology to convert spoken content into text in real time. Specifically, it collects the content spoken between the user and the support center representative during a video call using the tablet's built-in microphone, and the generation AI analyzes this audio data and converts it into text. This generation AI utilizes natural language processing technology to accurately transcribe spoken language into text. Using the generation AI, the conversion unit can, for example, automatically convert spoken content into text and display it as subtitles. This allows users to view subtitles in real time during video calls, facilitating smooth communication even for those with hearing impairments. The conversion unit also has a function to save the video call content as a text file. This makes it easy for users to review the content after the call ends and for the support center to manage the communication history. Furthermore, the conversion unit supports multiple languages, facilitating communication between users and the support center who speak different languages. For example, if a user speaks English and a support center representative speaks Japanese, the conversion unit can translate each language in real time and display them as subtitles. This enables smooth communication that transcends language barriers.

[0033] The follow-up unit includes a sign language conversion unit that converts sign language into speech in real time. The sign language conversion unit can, for example, capture sign language with a camera and convert it into speech in real time using sign language recognition technology. The sign language conversion unit may also include AI processing, for example, it can convert sign language into speech using a sign language recognition algorithm. The sign language conversion unit can, for example, convert sign language into speech in real time and provide voice feedback to the user. This facilitates communication with people who are deaf by converting sign language into speech in real time.

[0034] The follow-up department includes an app development department that develops apps for finger braille. The app development department can, for example, develop an interface for inputting finger braille, enabling users to input it. The app development department may also include AI processing, for example, it can convert finger braille to text using a finger braille recognition algorithm. The app development department can, for example, convert finger braille to text in real time and provide feedback to the user. In this way, developing apps for finger braille supports communication for people who use finger braille.

[0035] The conversion unit automatically converts spoken content into text using generative AI. The conversion unit can, for example, use speech recognition technology to convert spoken content into text in real time. The conversion unit can, for example, use generative AI to automatically convert spoken content into text and display it as subtitles. The conversion unit can also, for example, use generative AI to automatically convert spoken content into text and save it as text data. This improves the accuracy of automatically converting spoken content into text by using generative AI.

[0036] The reception desk analyzes the user's past call history and selects the optimal reception method. For example, the reception desk can store the user's past call history in a database and analyze it using AI. The reception desk may also include AI processing, for example, using machine learning algorithms to analyze past call history and select the optimal reception method. For example, the reception desk can automatically display call methods frequently used by the user in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest reception methods to be used during specific time periods based on the user's past call history. This allows the reception desk to provide the user with the most suitable reception method by analyzing past call history.

[0037] The reception desk filters calls based on the user's current situation and areas of interest. For example, the reception desk provides an interface for the user to input their current situation. The reception desk may include AI processing, for example, analyzing user input data and filtering based on the current situation and areas of interest. For example, when a user inputs their current situation, the reception desk can prioritize connecting them to a relevant support center member. For example, the reception desk can suggest appropriate support based on the user's areas of interest. For example, the reception desk can provide the optimal call reception method according to the user's current situation. This allows for the provision of appropriate support by filtering based on the user's current situation and areas of interest.

[0038] The reception desk prioritizes receiving calls based on their relevance, taking into account the user's geographical location. For example, the reception desk can obtain the user's geographical location using GPS data or an IP address. The reception desk may also incorporate AI processing, for example, analyzing geographical location information to prioritize receiving highly relevant calls. For example, if a user is in a specific region, the reception desk can prioritize connecting them to a support center member familiar with that region. For example, the reception desk can suggest the most appropriate support based on the user's geographical location. For example, if a user is on the move, the reception desk can update geographical location information in real time to provide the most suitable call reception method. This allows for the provision of regionally-focused support by considering geographical location information.

[0039] The reception desk analyzes the user's social media activity when receiving a call and receives relevant calls. For example, the reception desk can store the user's social media activity in a database and analyze it using AI. The reception desk may also include AI processing, for example, using machine learning algorithms to analyze social media activity and receive relevant calls. For example, the reception desk can understand the user's current interests and situation from their social media activity and propose appropriate support. For example, the reception desk can provide the optimal call handling method based on information the user has shared on social media. For example, the reception desk can analyze the user's social media activity and prioritize connecting them to relevant support center members. This allows for support tailored to the user's interests by analyzing their social media activity.

[0040] The follow-up unit analyzes the user's past communication history to select the optimal follow-up method during follow-up. For example, the follow-up unit can store the user's past communication history in a database and analyze it using AI. The follow-up unit may also include AI processing; for example, it can use machine learning algorithms to analyze past communication history and select the optimal follow-up method. For example, the follow-up unit can propose the optimal follow-up method based on the follow-up content the user has received in the past. For example, the follow-up unit can propose the optimal solution to a specific problem based on the user's past communication history. For example, the follow-up unit can analyze the user's past follow-up history and select the most effective follow-up method. This allows the system to provide the user with the most optimal follow-up method by analyzing past communication history.

[0041] The follow-up unit customizes the follow-up methods based on the user's current situation during follow-up. For example, the follow-up unit provides an interface for the user to input their current situation. The follow-up unit may include AI processing, for example, analyzing the user's input data and customizing the follow-up methods based on the current situation. For example, the follow-up unit can suggest the optimal follow-up method when the user inputs their current situation. For example, the follow-up unit can customize the follow-up methods according to the user's current situation. For example, the follow-up unit can grasp the user's current situation in real time and provide the optimal follow-up method. This allows for more appropriate responses by customizing the follow-up methods based on the current situation.

[0042] The follow-up unit selects the optimal follow-up method during follow-up, taking into account the user's geographical location information. The follow-up unit can, for example, obtain the user's geographical location information using GPS data or an IP address. The follow-up unit may also include AI processing, for example, analyzing geographical location information to select the optimal follow-up method. For example, if the user is in a specific region, the follow-up unit can prioritize connecting them to a support center member familiar with that region. For example, the follow-up unit can propose the most appropriate follow-up content based on the user's geographical location information. For example, if the user is on the move, the follow-up unit can update geographical location information in real time and provide the optimal follow-up method. This allows for the provision of support tailored to the region by considering geographical location information.

[0043] The follow-up department analyzes the user's social media activity during follow-up and proposes follow-up methods. For example, the follow-up department can store the user's social media activity in a database and analyze it using AI. The follow-up department may also include AI processing, for example, using machine learning algorithms to analyze social media activity and propose follow-up methods. For example, the follow-up department can understand the user's current interests and situation from their social media activity and propose appropriate follow-up content. For example, the follow-up department can provide the optimal follow-up method based on the information the user has posted on social media. For example, the follow-up department can analyze the user's social media activity and prioritize connecting them with relevant support center members. This allows for support tailored to the user's interests by analyzing their social media activity.

[0044] The conversion unit adjusts the level of detail in the conversion based on the importance of the communication content. For example, the conversion unit can evaluate the importance of the communication content and provide detailed textual representations for important content. The conversion unit includes generation AI processing and can evaluate the importance of the communication content using, for example, natural language processing technology. For example, the conversion unit can provide concise textual representations for general communication content. For example, the conversion unit can perform rapid conversions for urgent communication content and provide concise textual representations. In this way, appropriate textual representations are provided by adjusting the level of detail in the conversion based on the importance of the communication content.

[0045] The conversion unit applies different conversion algorithms depending on the communication category during conversion. For example, the conversion unit can apply a conversion algorithm that uses formal expressions for business communication. The conversion unit includes generation AI processing, and can, for example, use natural language processing technology to determine the communication category and apply an appropriate conversion algorithm. For example, the conversion unit can apply a conversion algorithm that uses friendly expressions for casual communication. For example, the conversion unit can apply an algorithm that performs rapid conversion for urgent communication. In this way, appropriate textual representations are provided by applying a conversion algorithm according to the communication category.

[0046] The conversion unit determines the conversion priority based on the submission timing of the communication during the conversion process. For example, the conversion unit can evaluate the submission timing of communications and convert urgent content with the highest priority. The conversion unit includes generation AI processing and can, for example, use natural language processing technology to evaluate the submission timing and set appropriate priorities. For example, the conversion unit can convert general communication content while prioritizing other urgent content. For example, the conversion unit can perform detailed conversions on important communication content and provide it preferentially. This allows for responses tailored to urgency by determining the conversion priority based on the submission timing.

[0047] The conversion unit adjusts the order of conversion based on the relevance of the communication during the conversion process. For example, the conversion unit can evaluate the relevance of the communication content and convert important content with the highest priority. The conversion unit includes generation AI processing and can evaluate relevance using natural language processing techniques and set an appropriate order. For example, the conversion unit can convert general communication content while prioritizing other important content. For example, the conversion unit can quickly convert urgent communication content and provide a concise written representation. In this way, by adjusting the order of conversion based on relevance, important content can be converted preferentially.

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

[0049] The reception department can analyze a user's past call history and select the most suitable reception method. For example, it can store a user's past call history in a database and analyze it using AI. It can automatically display call methods that the user has frequently used in the past as suggestions. It can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. It can predict and suggest reception methods to be used during specific time periods based on the user's past call history.

[0050] The reception desk can filter calls based on the user's current situation and areas of interest. For example, it can provide an interface for users to input their current situation. When a user inputs their current situation, it can prioritize connecting them to a member of the relevant support center. It can suggest appropriate support based on the user's areas of interest. It can provide the most suitable call reception method according to the user's current situation.

[0051] The follow-up department can analyze a user's past communication history to select the optimal follow-up method during follow-up. For example, it can store a user's past communication history in a database and analyze it using AI. Based on the follow-up content the user has received in the past, it can propose the most suitable follow-up method. Based on the user's past communication history, it can propose the best solution for a specific problem. By analyzing the user's past follow-up history, it can select the most effective follow-up method.

[0052] The follow-up unit can customize the follow-up methods based on the user's current situation during follow-up. For example, it can provide an interface for the user to input their current situation. When the user inputs their current situation, it can suggest the most appropriate follow-up method. The follow-up methods can be customized according to the user's current situation. The system can grasp the user's current situation in real time and provide the most appropriate follow-up method.

[0053] The conversion unit can adjust the level of detail in the conversion based on the importance of the communication content. For example, it can evaluate the importance of the communication content and provide detailed text representations for important content. It can provide concise text representations for general communication content. For urgent communication content, it can perform a rapid conversion and provide text representations that capture the main points. In this way, by adjusting the level of detail in the conversion based on the importance of the communication content, appropriate text representations are provided.

[0054] The conversion unit can apply different conversion algorithms depending on the communication category during conversion. For example, a conversion algorithm using formal expressions can be applied to business communication. A conversion algorithm using friendly expressions can be applied to casual communication. An algorithm that performs rapid conversion can be applied to urgent communication. In this way, appropriate textual representations are provided by applying a conversion algorithm according to the communication category.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk allows the user to open their tablet and make a call to the support center. The user can make a call to the support center by tapping a button displayed on the tablet screen. The reception desk also incorporates AI processing and can recognize the user's voice commands and make the call accordingly. Step 2: The follow-up department follows up on the call received by the reception department via video call. The follow-up department can start a video call with a support center member by tapping the start video call button. The follow-up department also incorporates AI processing and can recognize the user's voice command to start a video call. Step 3: The conversion unit automatically converts the communication content from the follow-up unit into text. The conversion unit includes generation AI processing and can convert spoken content into text in real time using speech recognition technology. In addition, the conversion unit can automatically convert spoken content into text using generation AI and display it as subtitles.

[0057] (Example of form 2) The communication follow-up system according to an embodiment of the present invention is a system that provides remote communication follow-up using images (sign language, text) and audio (subtitles) to customers who have a tablet contract, by members of the support center who have experience in disability care. The communication follow-up system is available to tablet contract holders on a monthly contract + additional option basis. This system is intended for people who are deaf, blind, or have speech impairments. When a user opens their tablet and calls the support center, a member of the support center will follow up on the user's communication via video call using sign language, text, or audio. Specifically, it consists of the following steps: First, the user opens their tablet and calls the support center. Next, a member of the support center will follow up on the communication via video call using sign language, text, or audio. For example, they will use sign language or text for people who are deaf, audio for people who are blind, and text or sign language for people with speech impairments. Furthermore, the system also provides a function that uses generation AI to automatically convert what is being said into text (subtitles). In the future, we aim to develop technologies that can convert sign language directly into speech and subtitles without any time lag, as well as apps for finger braille, enabling users to choose the most suitable communication method. This system will allow people who have difficulty communicating to have someone translate what everyone is saying for them, and to express their own thoughts and feelings. We also see this as an investment in contributing to society and building a foundation for the SDGs, and in making people happier through the information revolution. As a result, the communication follow-up system will be able to provide remote communication support to people with disabilities, or those who have difficulty communicating due to illness or aging.

[0058] The communication follow-up system according to the embodiment comprises a reception unit, a follow-up unit, and a conversion unit. The reception unit receives a call from a user who opens a tablet and makes a call to the support center. When the user opens the tablet, the reception unit can make a call to the support center, for example, by tapping a button displayed on the tablet screen. The reception unit may include AI processing and can, for example, recognize the user's voice command and make a call. The follow-up unit follows up on the communication via video call based on the call received by the reception unit. The follow-up unit can, for example, start a video call with a member of the support center by tapping a video call start button. The follow-up unit may include AI processing and can, for example, recognize the user's voice command and start a video call. The conversion unit automatically converts the communication content made by the follow-up unit into text. The conversion unit includes generation AI processing and can, for example, use speech recognition technology to convert what is being said into text in real time. The conversion unit can, for example, use generation AI to automatically convert what is being said into text and display it as subtitles. As a result, the communication follow-up system according to the embodiment allows users to call a support center via tablet, receive follow-up via video call, and automatically convert the communication content into text.

[0059] The reception desk allows users to call the support center by opening a tablet. When a user opens the tablet, the reception desk can, for example, make a call to the support center by tapping a button displayed on the tablet screen. Specifically, a dedicated application is installed on the tablet's home screen, and when this application is launched, a call button to the support center appears. By tapping this button, the user is immediately connected to the support center. Furthermore, the reception desk may also incorporate AI processing, for example, by recognizing the user's voice command and making a call. To recognize voice commands, the microphone built into the tablet is used, and voice recognition technology is utilized. When a user utters a voice command such as "Call the support center," the AI ​​analyzes the voice and automatically starts the call. This voice recognition technology has a noise-canceling function, which eliminates ambient noise and can accurately recognize voice commands. The reception desk also has a function that refers to the user's past call history and prioritizes displaying frequently asked questions and assigned personnel. This allows users to receive support quickly and efficiently.

[0060] The follow-up department provides communication follow-up via video call based on calls received by the reception department. For example, the follow-up department allows users to initiate a video call with a support center member by tapping a "start video call" button. Specifically, after the reception department receives a call, the follow-up department automatically prepares for the video call and displays a "start video call" button on the user's screen. Tapping this button initiates a video call with a support center representative. The follow-up department may also incorporate AI processing, for example, by recognizing user voice commands to initiate a video call. When using voice commands, if the user says something like "start a video call," the AI ​​analyzes the voice and automatically starts the video call. Furthermore, the follow-up department also has the ability to analyze the user's facial expressions and tone of voice during the video call to understand their emotional state. This allows support center representatives to respond appropriately to the user's emotional state. The follow-up department also monitors the network status in real time and adjusts image and sound quality as needed to optimize video call quality. This ensures that users always receive support through high-quality video calls.

[0061] The conversion unit automatically converts the communication content conducted by the follow-up unit into text. The conversion unit includes generation AI processing, and can, for example, use speech recognition technology to convert spoken content into text in real time. Specifically, it collects the content spoken between the user and the support center representative during a video call using the tablet's built-in microphone, and the generation AI analyzes this audio data and converts it into text. This generation AI utilizes natural language processing technology to accurately transcribe spoken language into text. Using the generation AI, the conversion unit can, for example, automatically convert spoken content into text and display it as subtitles. This allows users to view subtitles in real time during video calls, facilitating smooth communication even for those with hearing impairments. The conversion unit also has a function to save the video call content as a text file. This makes it easy for users to review the content after the call ends and for the support center to manage the communication history. Furthermore, the conversion unit supports multiple languages, facilitating communication between users and the support center who speak different languages. For example, if a user speaks English and a support center representative speaks Japanese, the conversion unit can translate each language in real time and display them as subtitles. This enables smooth communication that transcends language barriers.

[0062] The follow-up unit includes a sign language conversion unit that converts sign language into speech in real time. The sign language conversion unit can, for example, capture sign language with a camera and convert it into speech in real time using sign language recognition technology. The sign language conversion unit may also include AI processing, for example, it can convert sign language into speech using a sign language recognition algorithm. The sign language conversion unit can, for example, convert sign language into speech in real time and provide voice feedback to the user. This facilitates communication with people who are deaf by converting sign language into speech in real time.

[0063] The follow-up department includes an app development department that develops apps for finger braille. The app development department can, for example, develop an interface for inputting finger braille, enabling users to input it. The app development department may also include AI processing, for example, it can convert finger braille to text using a finger braille recognition algorithm. The app development department can, for example, convert finger braille to text in real time and provide feedback to the user. In this way, developing apps for finger braille supports communication for people who use finger braille.

[0064] The conversion unit automatically converts spoken content into text using generative AI. The conversion unit can, for example, use speech recognition technology to convert spoken content into text in real time. The conversion unit can, for example, use generative AI to automatically convert spoken content into text and display it as subtitles. The conversion unit can also, for example, use generative AI to automatically convert spoken content into text and save it as text data. This improves the accuracy of automatically converting spoken content into text by using generative AI.

[0065] The reception desk estimates the user's emotions and adjusts the call reception method based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk may also include AI processing, for example, using facial recognition technology to estimate the user's emotions. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. This allows for a more appropriate response by adjusting the call reception method according to 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. For example, if the user is nervous, the reception desk can provide a simple and reassuring interface and simplify the call reception procedure. For example, if the user is relaxed, the reception desk can provide detailed options and suggest a customizable reception method. For example, if the user is in a hurry, the reception desk can prioritize voice input to quickly accept the call.

[0066] The reception desk analyzes the user's past call history and selects the optimal reception method. For example, the reception desk can store the user's past call history in a database and analyze it using AI. The reception desk may also include AI processing, for example, using machine learning algorithms to analyze past call history and select the optimal reception method. For example, the reception desk can automatically display call methods frequently used by the user in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest reception methods to be used during specific time periods based on the user's past call history. This allows the reception desk to provide the user with the most suitable reception method by analyzing past call history.

[0067] The reception desk filters calls based on the user's current situation and areas of interest. For example, the reception desk provides an interface for the user to input their current situation. The reception desk may include AI processing, for example, analyzing user input data and filtering based on the current situation and areas of interest. For example, when a user inputs their current situation, the reception desk can prioritize connecting them to a relevant support center member. For example, the reception desk can suggest appropriate support based on the user's areas of interest. For example, the reception desk can provide the optimal call reception method according to the user's current situation. This allows for the provision of appropriate support by filtering based on the user's current situation and areas of interest.

[0068] The reception desk estimates the user's emotions and determines the priority of calls to be received based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk may also include AI processing, for example, using facial recognition technology to estimate the user's emotions. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. This allows for responses tailored to the urgency of the call by determining the priority of calls 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. For example, the reception desk can give top priority to calls when the user is in an urgent situation. For example, the reception desk can give priority to other urgent calls when the user is relaxed. For example, the reception desk can prioritize connecting a member who can respond quickly when the user is stressed.

[0069] The reception desk prioritizes receiving calls based on their relevance, taking into account the user's geographical location. For example, the reception desk can obtain the user's geographical location using GPS data or an IP address. The reception desk may also incorporate AI processing, for example, analyzing geographical location information to prioritize receiving highly relevant calls. For example, if a user is in a specific region, the reception desk can prioritize connecting them to a support center member familiar with that region. For example, the reception desk can suggest the most appropriate support based on the user's geographical location. For example, if a user is on the move, the reception desk can update geographical location information in real time to provide the most suitable call reception method. This allows for the provision of regionally-focused support by considering geographical location information.

[0070] The reception desk analyzes the user's social media activity when receiving a call and receives relevant calls. For example, the reception desk can store the user's social media activity in a database and analyze it using AI. The reception desk may also include AI processing, for example, using machine learning algorithms to analyze social media activity and receive relevant calls. For example, the reception desk can understand the user's current interests and situation from their social media activity and propose appropriate support. For example, the reception desk can provide the optimal call handling method based on information the user has shared on social media. For example, the reception desk can analyze the user's social media activity and prioritize connecting them to relevant support center members. This allows for support tailored to the user's interests by analyzing their social media activity.

[0071] The follow-up unit estimates the user's emotions and adjusts the follow-up method based on the estimated emotions. For example, the follow-up unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The follow-up unit may also include AI processing, for example, using facial recognition technology to estimate the user's emotions. The follow-up unit can also record the user's voice and estimate their emotions using voice analysis technology. This allows for a more appropriate response by adjusting the follow-up method according to 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. For example, if the user is nervous, the follow-up unit can provide follow-up in a calm tone. For example, if the user is relaxed, the follow-up unit can provide detailed information and follow-up. For example, if the user is in a hurry, the follow-up unit can provide concise and rapid follow-up.

[0072] The follow-up unit analyzes the user's past communication history to select the optimal follow-up method during follow-up. For example, the follow-up unit can store the user's past communication history in a database and analyze it using AI. The follow-up unit may also include AI processing; for example, it can use machine learning algorithms to analyze past communication history and select the optimal follow-up method. For example, the follow-up unit can propose the optimal follow-up method based on the follow-up content the user has received in the past. For example, the follow-up unit can propose the optimal solution to a specific problem based on the user's past communication history. For example, the follow-up unit can analyze the user's past follow-up history and select the most effective follow-up method. This allows the system to provide the user with the most optimal follow-up method by analyzing past communication history.

[0073] The follow-up unit customizes the follow-up methods based on the user's current situation during follow-up. For example, the follow-up unit provides an interface for the user to input their current situation. The follow-up unit may include AI processing, for example, analyzing the user's input data and customizing the follow-up methods based on the current situation. For example, the follow-up unit can suggest the optimal follow-up method when the user inputs their current situation. For example, the follow-up unit can customize the follow-up methods according to the user's current situation. For example, the follow-up unit can grasp the user's current situation in real time and provide the optimal follow-up method. This allows for more appropriate responses by customizing the follow-up methods based on the current situation.

[0074] The follow-up unit estimates the user's emotions and determines the priority of follow-up based on the estimated emotions. For example, the follow-up unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The follow-up unit may also include AI processing, for example, using facial recognition technology to estimate the user's emotions. The follow-up unit can also record the user's voice and estimate their emotions using voice analysis technology. This allows for responses tailored to the urgency of the situation by determining the priority of follow-up based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the follow-up unit can prioritize follow-up if the user is in an urgent situation. For example, if the user is relaxed, the follow-up unit can prioritize other urgent follow-ups while still responding. For example, if the user is stressed, the follow-up unit can prioritize connecting them with members who can respond quickly.

[0075] The follow-up unit selects the optimal follow-up method during follow-up, taking into account the user's geographical location information. The follow-up unit can, for example, obtain the user's geographical location information using GPS data or an IP address. The follow-up unit may also include AI processing, for example, analyzing geographical location information to select the optimal follow-up method. For example, if the user is in a specific region, the follow-up unit can prioritize connecting them to a support center member familiar with that region. For example, the follow-up unit can propose the most appropriate follow-up content based on the user's geographical location information. For example, if the user is on the move, the follow-up unit can update geographical location information in real time and provide the optimal follow-up method. This allows for the provision of support tailored to the region by considering geographical location information.

[0076] The follow-up department analyzes the user's social media activity during follow-up and proposes follow-up methods. For example, the follow-up department can store the user's social media activity in a database and analyze it using AI. The follow-up department may also include AI processing, for example, using machine learning algorithms to analyze social media activity and propose follow-up methods. For example, the follow-up department can understand the user's current interests and situation from their social media activity and propose appropriate follow-up content. For example, the follow-up department can provide the optimal follow-up method based on the information the user has posted on social media. For example, the follow-up department can analyze the user's social media activity and prioritize connecting them with relevant support center members. This allows for support tailored to the user's interests by analyzing their social media activity.

[0077] The conversion unit estimates the user's emotions and adjusts the way the converted text is expressed based on the estimated emotions. For example, the conversion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The conversion unit includes processing by a generative AI and can estimate the user's emotions using, for example, facial recognition technology. The conversion unit can also record the user's voice and estimate the emotions using, for example, speech analysis technology. This allows for more appropriate text representations to be provided by adjusting the way the text is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is tense, the conversion unit can provide a simple and highly legible text representation. For example, if the user is relaxed, the conversion unit can provide a text representation that includes detailed information. For example, if the user is in a hurry, the conversion unit can provide a concise and rapid text representation.

[0078] The conversion unit adjusts the level of detail in the conversion based on the importance of the communication content. For example, the conversion unit can evaluate the importance of the communication content and provide detailed textual representations for important content. The conversion unit includes generation AI processing and can evaluate the importance of the communication content using, for example, natural language processing technology. For example, the conversion unit can provide concise textual representations for general communication content. For example, the conversion unit can perform rapid conversions for urgent communication content and provide concise textual representations. In this way, appropriate textual representations are provided by adjusting the level of detail in the conversion based on the importance of the communication content.

[0079] The conversion unit applies different conversion algorithms depending on the communication category during conversion. For example, the conversion unit can apply a conversion algorithm that uses formal expressions for business communication. The conversion unit includes generation AI processing, and can, for example, use natural language processing technology to determine the communication category and apply an appropriate conversion algorithm. For example, the conversion unit can apply a conversion algorithm that uses friendly expressions for casual communication. For example, the conversion unit can apply an algorithm that performs rapid conversion for urgent communication. In this way, appropriate textual representations are provided by applying a conversion algorithm according to the communication category.

[0080] The conversion unit estimates the user's emotions and adjusts the length of the characters to be converted based on the estimated emotions. For example, the conversion unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The conversion unit includes processing by a generative AI and can estimate the user's emotions using, for example, facial recognition technology. The conversion unit can also record the user's voice and estimate the emotions using, for example, speech analysis technology. This allows for more appropriate textual expression by adjusting the length of the characters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is in a hurry, the conversion unit can provide a short, concise textual expression. For example, if the user is relaxed, the conversion unit can provide a longer textual expression that includes detailed explanations. For example, if the user is excited, the conversion unit can provide a textual expression with visually stimulating effects.

[0081] The conversion unit determines the conversion priority based on the submission timing of the communication during the conversion process. For example, the conversion unit can evaluate the submission timing of communications and convert urgent content with the highest priority. The conversion unit includes generation AI processing and can, for example, use natural language processing technology to evaluate the submission timing and set appropriate priorities. For example, the conversion unit can convert general communication content while prioritizing other urgent content. For example, the conversion unit can perform detailed conversions on important communication content and provide it preferentially. This allows for responses tailored to urgency by determining the conversion priority based on the submission timing.

[0082] The conversion unit adjusts the order of conversion based on the relevance of the communication during the conversion process. For example, the conversion unit can evaluate the relevance of the communication content and convert important content with the highest priority. The conversion unit includes generation AI processing and can evaluate relevance using natural language processing techniques and set an appropriate order. For example, the conversion unit can convert general communication content while prioritizing other important content. For example, the conversion unit can quickly convert urgent communication content and provide a concise written representation. In this way, by adjusting the order of conversion based on relevance, important content can be converted preferentially.

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

[0084] The reception system can estimate the user's emotions and adjust the call reception method based on those estimates. For example, it can capture the user's facial expressions with a camera and use an emotion estimation algorithm to estimate their emotions. If the user is nervous, it can provide a simple and reassuring interface and simplify the call reception procedure. If the user is relaxed, it can offer detailed options and suggest a customizable reception method. If the user is in a hurry, it can prioritize voice input to ensure the call is answered quickly.

[0085] The follow-up unit can estimate the user's emotions and adjust the follow-up method based on the estimated emotions. For example, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is nervous, the follow-up can be conducted in a calm tone. If the user is relaxed, detailed information can be provided during the follow-up. If the user is in a hurry, a concise and quick follow-up can be conducted.

[0086] The conversion unit can estimate the user's emotions and adjust the way the converted text is expressed based on the estimated emotions. For example, the user's facial expression can be captured by a camera, and the emotions can be estimated using an emotion estimation algorithm. If the user is tense, a simple and highly legible text representation can be provided. If the user is relaxed, a text representation containing detailed information can be provided. If the user is in a hurry, a concise and rapid text representation can be provided.

[0087] The reception department can analyze a user's past call history and select the most suitable reception method. For example, it can store a user's past call history in a database and analyze it using AI. It can automatically display call methods that the user has frequently used in the past as suggestions. It can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. It can predict and suggest reception methods to be used during specific time periods based on the user's past call history.

[0088] The reception desk can filter calls based on the user's current situation and areas of interest. For example, it can provide an interface for users to input their current situation. When a user inputs their current situation, it can prioritize connecting them to a member of the relevant support center. It can suggest appropriate support based on the user's areas of interest. It can provide the most suitable call reception method according to the user's current situation.

[0089] The follow-up department can analyze a user's past communication history to select the optimal follow-up method during follow-up. For example, it can store a user's past communication history in a database and analyze it using AI. Based on the follow-up content the user has received in the past, it can propose the most suitable follow-up method. Based on the user's past communication history, it can propose the best solution for a specific problem. By analyzing the user's past follow-up history, it can select the most effective follow-up method.

[0090] The follow-up unit can customize the follow-up methods based on the user's current situation during follow-up. For example, it can provide an interface for the user to input their current situation. When the user inputs their current situation, it can suggest the most appropriate follow-up method. The follow-up methods can be customized according to the user's current situation. The system can grasp the user's current situation in real time and provide the most appropriate follow-up method.

[0091] The conversion unit can adjust the level of detail in the conversion based on the importance of the communication content. For example, it can evaluate the importance of the communication content and provide detailed text representations for important content. It can provide concise text representations for general communication content. For urgent communication content, it can perform a rapid conversion and provide text representations that capture the main points. In this way, by adjusting the level of detail in the conversion based on the importance of the communication content, appropriate text representations are provided.

[0092] The conversion unit can apply different conversion algorithms depending on the communication category during conversion. For example, a conversion algorithm using formal expressions can be applied to business communication. A conversion algorithm using friendly expressions can be applied to casual communication. An algorithm that performs rapid conversion can be applied to urgent communication. In this way, appropriate textual representations are provided by applying a conversion algorithm according to the communication category.

[0093] The conversion unit can estimate the user's emotions and adjust the length of the converted text based on the estimated emotions. For example, the user's facial expression can be captured by a camera, and the emotion can be estimated using an emotion estimation algorithm. If the user is in a hurry, a short, concise text expression can be provided. If the user is relaxed, a longer text expression with detailed explanations can be provided. If the user is excited, a text expression with visually stimulating effects can be provided.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The reception desk allows the user to open their tablet and make a call to the support center. The user can make a call to the support center by tapping a button displayed on the tablet screen. The reception desk also incorporates AI processing and can recognize the user's voice commands and make the call accordingly. Step 2: The follow-up department follows up on the call received by the reception department via video call. The follow-up department can start a video call with a support center member by tapping the start video call button. The follow-up department also incorporates AI processing and can recognize the user's voice command to start a video call. Step 3: The conversion unit automatically converts the communication content from the follow-up unit into text. The conversion unit includes generation AI processing and can convert spoken content into text in real time using speech recognition technology. In addition, the conversion unit can automatically convert spoken content into text using generation AI and display it as subtitles.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements mentioned above, including the reception unit, follow-up unit, conversion unit, sign language conversion unit, application development unit, emotion estimation unit, history analysis unit, filtering unit, priority determination unit, geographic information unit, social media analysis unit, emotion adjustment unit, importance adjustment unit, category application unit, character length adjustment unit, submission timing priority unit, and relevance adjustment unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user calls. The follow-up unit is implemented by the identification processing unit 290 of the data processing unit 12 and follows up on communication through video calls. The conversion unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically converts spoken content into text. The sign language conversion unit converts sign language into speech in real time using the camera 42 of the smart device 14. The application development unit develops finger braille applications using the control unit 46A of the smart device 14. The emotion estimation unit estimates the user's emotions using the camera 42 of the smart device 14. The history analysis unit analyzes past call history using the specific processing unit 290 of the data processing device 12. The filtering unit filters based on the user's current situation and areas of interest using the control unit 46A of the smart device 14. The priority determination unit determines the priority of calls using the specific processing unit 290 of the data processing device 12. The geographic information unit acquires geographic location information using GPS data from the smart device 14. The social media analysis unit analyzes social media activity using the specific processing unit 290 of the data processing device 12. The emotion adjustment unit adjusts the expression method of text based on emotion using the camera 42 of the smart device 14. The importance adjustment unit adjusts the level of detail of the conversion based on the importance of the communication content using the specific processing unit 290 of the data processing device 12. The category application unit applies a conversion algorithm according to the category of the communication using the specific processing unit 290 of the data processing device 12. The character length adjustment unit adjusts the length of characters based on emotion using the camera 42 of the smart device 14. The submission timing priority unit determines the conversion priority based on the submission timing using the specific processing unit 290 of the data processing device 12.The relationship adjustment unit adjusts the order of conversions based on the relationship of communication using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements mentioned above, including the reception unit, follow-up unit, conversion unit, sign language conversion unit, application development unit, emotion estimation unit, history analysis unit, filtering unit, priority determination unit, geographic information unit, social media analysis unit, emotion adjustment unit, importance adjustment unit, category application unit, character length adjustment unit, submission timing priority unit, and relevance adjustment unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user calls. The follow-up unit is implemented by the specific processing unit 290 of the data processing unit 12 and follows up on communication through video calls. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts spoken content into text. The sign language conversion unit converts sign language into speech in real time using the camera 42 of the smart glasses 214. The application development unit develops finger braille applications using the control unit 46A of the smart glasses 214. The emotion estimation unit estimates the user's emotions using the camera 42 of the smart glasses 214. The history analysis unit analyzes past call history using the identification processing unit 290 of the data processing device 12. The filtering unit filters based on the user's current situation and areas of interest using the control unit 46A of the smart glasses 214. The priority determination unit determines the priority of calls using the identification processing unit 290 of the data processing device 12. The geographic information unit acquires geographic location information using GPS data from the smart glasses 214. The social media analysis unit analyzes social media activity using the identification processing unit 290 of the data processing device 12. The emotion adjustment unit adjusts the way text is expressed based on emotions using the camera 42 of the smart glasses 214. The importance adjustment unit adjusts the level of detail of the conversion based on the importance of the communication content using the identification processing unit 290 of the data processing device 12. The category application unit applies a conversion algorithm according to the category of the communication using the identification processing unit 290 of the data processing device 12. The character length adjustment unit adjusts the length of characters based on emotions using the camera 42 of the smart glasses 214. The submission timing priority unit determines the conversion priority based on the submission timing using the specific processing unit 290 of the data processing device 12.The relationship adjustment unit adjusts the order of conversions based on the relationship of communication using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements mentioned above, including the reception unit, follow-up unit, conversion unit, sign language conversion unit, application development unit, emotion estimation unit, history analysis unit, filtering unit, priority determination unit, geographic information unit, social media analysis unit, emotion adjustment unit, importance adjustment unit, category application unit, character length adjustment unit, submission timing priority unit, and relevance adjustment unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user calls. The follow-up unit is implemented by the specific processing unit 290 of the data processing unit 12 and follows up on communication through video calls. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts spoken content into text. The sign language conversion unit converts sign language into speech in real time using the camera 42 of the headset terminal 314. The application development unit develops finger braille applications using the control unit 46A of the headset terminal 314. The emotion estimation unit estimates the user's emotions using the camera 42 of the headset terminal 314. The history analysis unit analyzes past call history using the identification processing unit 290 of the data processing device 12. The filtering unit filters based on the user's current situation and areas of interest using the control unit 46A of the headset terminal 314. The priority determination unit determines the priority of calls using the identification processing unit 290 of the data processing device 12. The geographic information unit acquires geographic location information using GPS data from the headset terminal 314. The social media analysis unit analyzes social media activity using the identification processing unit 290 of the data processing device 12. The emotion adjustment unit adjusts the expression method of characters based on emotions using the camera 42 of the headset terminal 314. The importance adjustment unit adjusts the level of detail of the conversion based on the importance of the communication content using the identification processing unit 290 of the data processing device 12. The category application unit applies a conversion algorithm according to the category of the communication using the identification processing unit 290 of the data processing device 12. The character length adjustment unit adjusts the length of characters based on emotions using the camera 42 of the headset terminal 314.The submission timing priority unit determines the conversion priority based on the submission timing using the specific processing unit 290 of the data processing device 12. The relevance adjustment unit adjusts the conversion order based on the relevance of communications using the specific processing unit 290 of the data processing device 12. 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.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements mentioned above, including the reception unit, follow-up unit, conversion unit, sign language conversion unit, application development unit, emotion estimation unit, history analysis unit, filtering unit, priority determination unit, geographic information unit, social media analysis unit, emotion adjustment unit, importance adjustment unit, category application unit, character length adjustment unit, submission timing priority unit, and relevance adjustment unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user calls. The follow-up unit is implemented by the specific processing unit 290 of the data processing unit 12 and follows up on communication through video calls. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts spoken content into text. The sign language conversion unit converts sign language into speech in real time using the camera 42 of the robot 414. The application development unit develops finger braille applications using the control unit 46A of the robot 414. The emotion estimation unit estimates the user's emotions using the camera 42 of the robot 414. The history analysis unit analyzes past call history using the specific processing unit 290 of the data processing device 12. The filtering unit filters based on the user's current situation and areas of interest using the control unit 46A of the robot 414. The priority determination unit determines the priority of calls using the specific processing unit 290 of the data processing device 12. The geographic information unit acquires geographic location information using GPS data from the robot 414. The social media analysis unit analyzes social media activity using the specific processing unit 290 of the data processing device 12. The emotion adjustment unit adjusts the expression method of characters based on emotions using the camera 42 of the robot 414. The importance adjustment unit adjusts the level of detail of the conversion based on the importance of the communication content using the specific processing unit 290 of the data processing device 12. The category application unit applies a conversion algorithm according to the category of the communication using the specific processing unit 290 of the data processing device 12. The character length adjustment unit adjusts the length of characters based on emotions using the camera 42 of the robot 414. The submission timing priority unit determines the conversion priority based on the submission timing using the specific processing unit 290 of the data processing device 12.The relationship adjustment unit adjusts the order of conversions based on the relationship of communication using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) The user opens the tablet and makes a call to the support center, Based on the call received by the aforementioned reception unit, the follow-up unit conducts follow-up communication via video call, The system includes a conversion unit that automatically converts the communication content performed by the aforementioned follow-up unit into text. A system characterized by the following features. (Note 2) The aforementioned follow-up unit is, It is equipped with a sign language conversion unit that converts sign language into speech in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned follow-up unit is, The company has an app development department that develops apps for finger braille. The system described in Appendix 1, characterized by the features described herein. (Note 4) The conversion unit is The AI ​​automatically converts spoken content into text. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts how calls are handled based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past call history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When a call is received, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of incoming calls based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a call, the system prioritizes receiving calls that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a call is received, the system analyzes the user's social media activity and receives relevant calls. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned follow-up unit is, It estimates the user's emotions and adjusts the follow-up method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned follow-up unit is, During follow-up, the system analyzes the user's past communication history to select the most appropriate follow-up method. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned follow-up unit is, During follow-up, customize the follow-up methods based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned follow-up unit is, The system estimates the user's emotions and determines the priority of follow-up based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned follow-up unit is, During follow-up, the optimal follow-up method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned follow-up unit is, During follow-up, we analyze the user's social media activity and suggest follow-up methods. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is It estimates the user's emotions and adjusts the way text is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During conversion, adjust the level of detail based on the importance of the communication content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The conversion unit is During conversion, different conversion algorithms are applied depending on the communication category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The conversion unit is It estimates the user's emotions and adjusts the length of the characters to be converted based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The conversion unit is During the conversion process, the conversion priority is determined based on the submission date of the communication. The system described in Appendix 1, characterized by the features described herein. (Note 22) The conversion unit is During conversion, the order of conversions is adjusted based on the relevance of the communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 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 user opens the tablet and makes a call to the support center, Based on the call received by the aforementioned reception unit, the follow-up unit conducts follow-up communication via video call, The system includes a conversion unit that automatically converts the communication content performed by the aforementioned follow-up unit into text. A system characterized by the following features.

2. The aforementioned follow-up unit is, It is equipped with a sign language conversion unit that converts sign language into speech in real time. The system according to feature 1.

3. The aforementioned follow-up unit is, The company has an app development department that develops apps for finger braille. The system according to feature 1.

4. The conversion unit is The AI ​​automatically converts what you're saying into text. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts how calls are handled based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past call history and select the optimal reception method. The system according to feature 1.

7. The aforementioned reception unit is When a call is received, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of incoming calls based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving a call, the system prioritizes receiving calls that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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