system

Abstract

The system according to this embodiment aims to suggest menus tailored to the user's physical condition via voice instructions and to provide them in multiple languages. [Solution] The system according to the embodiment comprises a reception unit, a data collection unit, an analysis unit, a suggestion unit, and a multilingual support unit. The reception unit receives voice instructions. The data collection unit collects health data based on the voice instructions received by the reception unit. The analysis unit analyzes the health data collected by the data collection unit. The suggestion unit proposes a menu based on the analysis results obtained by the analysis unit. The multilingual support unit provides the menu proposed by the suggestion unit in multiple languages.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, it is difficult to select a menu according to the physical condition, it is troublesome to propose daily meals, and there is a problem that voice devices have a single function.

[0005] The system according to the embodiment aims to propose a menu according to the physical condition of the user by voice instruction and provide it in multiple languages.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, a proposal unit, and a multilingual support unit. The reception unit receives voice instructions. The data collection unit collects health data based on the voice instructions received by the reception unit. The analysis unit analyzes the health data collected by the data collection unit. The proposal unit proposes a menu based on the analysis results obtained by the analysis unit. The multilingual support unit provides the menu proposed by the proposal unit in multiple languages. [Effects of the Invention]

[0007] The system according to this embodiment can suggest menus tailored to the user's physical condition via voice instructions and provide them in multiple languages. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 reception 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 reception 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 home health AI agent system according to an embodiment of the present invention is a system that provides meal suggestions based on the user's health condition. This home health AI agent system integrates a voice assistant device, allowing the user to initiate meal suggestions by voice command. Next, health data is collected from wearable devices and user input information, and the AI ​​analyzes this data. Based on the analysis results, a personalized menu is proposed that is tailored to the user's physical condition and allergy information. Furthermore, under the supervision of a nutritionist, adjustments are made to the nutritional balance and preferences to provide healthy and delicious meals. The suggestions are multilingual, and voice guidance and suggestions can be provided in the user's native language. This system improves the user's health awareness, shortens meal preparation time, and supports healthy lifestyle habits. For example, if the user gives the voice command, "Suggest a dinner for tonight," the AI ​​will start suggesting meals. At this time, the voice assistant device recognizes the user's voice and sends instructions to the AI. Next, health data is collected from wearable devices and user input information, and the AI ​​analyzes this data. For example, if the user is wearing a wearable device, data such as heart rate, body temperature, and activity level are collected. The user can also input physical condition and allergy information through the app. This data is analyzed by AI to understand the user's health status. Based on the analysis results, personalized menus are suggested that are tailored to the user's physical condition and allergy information. For example, if a user says, "I feel feverish, so I want something warm and easy to digest," the AI ​​will suggest, "How about egg and ginger porridge?" In this way, menus are provided that are suited to the user's physical condition. Furthermore, under the supervision of a nutritionist, adjustments are made to ensure nutritional balance and suitability, providing healthy and delicious meals. For example, based on recipes supervised by a nutritionist, the AI ​​adjusts the menu to suit the user's preferences. This ensures that nutritious and delicious meals are provided. Suggestions are multilingual, and voice guidance and suggestions can be provided in the user's native language. For example, suggestions are available in multiple languages ​​such as English, Spanish, and Chinese. This allows for smooth communication even for users with different native languages.This system improves users' health awareness, reduces meal preparation time, and supports healthy lifestyles. For example, users can leave daily meal suggestions to the AI, saving them the trouble of preparing meals. Furthermore, the suggestion of healthy menus increases users' health awareness and helps them adopt healthy lifestyle habits. This allows the home health AI agent system to provide meal suggestions based on the user's health condition.

[0029] The home health AI agent system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, a suggestion unit, and a multilingual support unit. The reception unit receives voice instructions. For example, the reception unit can receive voice instructions if the user says, "Suggest what to have for dinner tonight." The data collection unit collects health data based on the voice instructions received by the reception unit. For example, if the user is wearing a wearable device, the data collection unit collects data such as heart rate, body temperature, and activity level. The data collection unit can also receive user information about their physical condition and allergies through an app. The analysis unit analyzes the health data collected by the data collection unit. For example, the analysis unit analyzes the health data using AI. For example, the AI ​​analyzes the health data using technologies such as deep learning and machine learning. The suggestion unit suggests menus based on the analysis results obtained by the analysis unit. For example, the suggestion unit suggests menus that are appropriate for the user's physical condition and allergy information. For example, if the user says, "I feel feverish, so I want to eat something warm and easy to digest," the suggestion unit suggests, "How about egg and ginger porridge?" The multilingual support unit provides menus proposed by the proposal unit in multiple languages. The multilingual support unit makes proposals in multiple languages, such as English, Spanish, and Chinese. This allows the home health AI agent system to provide meal suggestions based on the user's health condition.

[0030] The reception desk receives voice commands. For example, if a user gives a voice command such as "Suggest what to have for dinner tonight," the reception desk can receive the command. Specifically, the reception desk combines a high-sensitivity microphone with voice recognition technology to accurately capture the user's voice commands. The voice recognition technology uses natural language processing (NLP) to convert the user's speech into text data. This text data is sent to other departments within the system for further processing. The voice recognition technology has a noise-canceling function, which removes ambient noise and allows for clear recognition of the user's voice. The voice command reception also includes a function to learn the characteristics of the user's voice, so it can accurately recognize the commands of a specific user even in an environment with multiple users. As a result, the reception desk can receive user voice commands quickly and accurately, improving the overall usability of the system.

[0031] The data collection unit collects health data based on voice instructions received by the reception unit. For example, if the user is wearing a wearable device, the data collection unit collects data such as heart rate, body temperature, and activity level. Specifically, the data collection unit acquires data from wearable devices in real time using wireless communication technologies such as Bluetooth® and Wi-Fi. This allows for constant monitoring of the user's health status. The data collection unit also allows users to input health and allergy information through an app. The app is designed with an intuitive and easy-to-use user interface, allowing users to easily input their health information. Furthermore, the data collection unit stores data on a cloud server and can refer to past data as needed. This allows the data collection unit to comprehensively understand the user's health status and provide accurate data to the analysis and recommendation units.

[0032] The Analysis Department analyzes health data collected by the Data Collection Department. For example, the Analysis Department uses AI to analyze health data. Specifically, AI employs technologies such as deep learning and machine learning to comprehensively analyze user health data. Deep learning uses multi-layered neural networks to extract data features and identify patterns in health status. Machine learning builds predictive models based on past data to predict changes in the user's health status. For example, it can detect abnormalities in a user's physical condition early from fluctuations in heart rate and body temperature. Furthermore, the Analysis Department provides foundational data for making health-conscious meal suggestions, taking into account the user's dietary history and allergy information. This allows the Analysis Department to accurately understand the user's health status and provide appropriate information to the Suggestion Department.

[0033] The suggestion department proposes menus based on the analysis results obtained by the analysis department. For example, the suggestion department proposes menus tailored to the user's physical condition and allergy information. Specifically, the suggestion department uses AI to generate menus that take into account the user's health condition and preferences. The AI ​​proposes the optimal meal plan based on the user's past eating history and current health condition. For example, if the user gives a voice command saying, "I feel feverish, so I want to eat something warm and easy to digest," the suggestion department will suggest, "How about egg and ginger porridge?" The suggestion department can provide nutritionally balanced menus according to the user's health condition. In addition, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. As a result, the suggestion department can provide optimal meal suggestions for the user's health condition and support the user in maintaining their health.

[0034] The multilingual support unit provides menus proposed by the proposal unit in multiple languages. The multilingual support unit makes proposals in multiple languages, such as English, Spanish, and Chinese. Specifically, the multilingual support unit uses natural language processing (NLP) technology to translate proposals into multiple languages. Because NLP technology understands context and can provide appropriate translations, users can receive proposals in their native language. Furthermore, the multilingual support unit can automatically recognize the user's language settings and make proposals in the appropriate language. This allows the multilingual support unit to support users who speak different languages, expanding the system's usability. In addition, the multilingual support unit can continuously improve translation accuracy based on user feedback. This enables the multilingual support unit to provide users with accurate and appropriate information, improving the system's usability.

[0035] The reception desk can receive voice commands using a voice assistant device. For example, if a user gives a voice command such as "Suggest dinner for tonight" using a voice assistant device, the reception desk can receive the voice command. This allows the user to initiate meal suggestions by voice command using a voice assistant device. Voice assistant devices include, but are not limited to, smart speakers and smartphones. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input voice data obtained from a voice assistant device into a generating AI and have the generating AI perform analysis of the voice command.

[0036] The data collection unit can collect health data from wearable devices and user input information. For example, if the user is wearing a wearable device, the data collection unit can collect data such as heart rate, body temperature, and activity level. The data collection unit can also receive user input on their physical condition and allergy information through an app. This allows the system to understand the user's health status by collecting health data from wearable devices and user input information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input health data acquired from a wearable device into a generating AI and have the generating AI perform analysis of the health data.

[0037] The analysis unit can analyze the collected health data using AI. The analysis unit can, for example, analyze the health data using AI. The AI ​​can analyze the health data using techniques such as deep learning and machine learning. As a result, the accuracy of the health data analysis is improved by using AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the collected health data into a generating AI and have the generating AI perform the analysis of the health data.

[0038] The suggestion unit can propose menus tailored to the user's physical condition and allergy information based on the analysis results. For example, if the user gives a voice command saying, "I feel feverish, so I want something warm and easy to digest," the suggestion unit will suggest, "How about egg and ginger porridge?" This allows for individually optimized meal suggestions by proposing menus tailored to the user's physical condition and allergy information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into a generating AI and have the generating AI execute menu suggestions.

[0039] The multilingual support unit can provide the proposed menu in multiple languages. For example, the multilingual support unit makes suggestions in multiple languages, such as English, Spanish, and Chinese. This allows the system to cater to users who speak various languages ​​by providing menus in multiple languages. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the proposed menu into a generation AI and have the generation AI generate suggestions in multiple languages.

[0040] The reception unit can select the optimal reception method when receiving a voice command by referring to the user's past command history. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. The reception unit can also predict and suggest voice commands to be used during specific time periods based on the user's past command history. Furthermore, the reception unit can prioritize suggesting voice input methods (voice, text, etc.) that the user has used in the past. This allows the reception unit to select the optimal reception method by referring to the user's past command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past command history data into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception unit can perform noise cancellation when it receives a voice command, taking into account the user's current ambient noise. For example, if the user is in a noisy environment, the reception unit can automatically enable the noise cancellation function. Conversely, if the user is in a quiet environment, the reception unit can also disable the noise cancellation function. Furthermore, the reception unit can analyze the user's ambient noise in real time and suggest the optimal noise cancellation settings. This improves the accuracy of receiving voice commands by performing noise cancellation according to the user's ambient noise. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input ambient noise data into a generating AI and have the generating AI execute the noise cancellation settings.

[0042] The data collection unit can select the optimal data collection method by referring to the user's past health data when collecting health data. For example, the data collection unit can propose the optimal data collection method based on the health data the user has collected in the past. The data collection unit can also predict and propose data to be collected at specific time periods based on the user's past health data. Furthermore, the data collection unit can analyze the user's past health data and propose the most efficient data collection method. In this way, the optimal data collection method can be selected by referring to the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can adjust the frequency of data collection, taking into account the user's current activity level, when collecting health data. For example, the unit may collect health data more frequently when the user is exercising. It can also collect health data at a normal frequency when the user is resting. Furthermore, the unit can suggest an optimal collection frequency based on the user's activity level. By adjusting the data collection frequency according to the user's activity level, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current activity data into a generating AI and have the generating AI adjust the collection frequency.

[0044] The analysis unit can select the optimal analysis algorithm by referring to the user's past health data when analyzing health data. For example, the analysis unit can propose the optimal analysis algorithm based on the health data the user has collected in the past. The analysis unit can also predict and propose data to be analyzed at a specific time period based on the user's past health data. Furthermore, the analysis unit can analyze the user's past health data and propose the most efficient analysis algorithm. In this way, the optimal analysis algorithm can be selected by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past health data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis of health data by taking into account the user's current lifestyle. For example, if the user is exercising, the analysis unit will prioritize analyzing data related to exercise. If the user is resting, the analysis unit can analyze the data using the normal analysis method. The analysis unit can also suggest the optimal analysis method according to the user's lifestyle. This improves the accuracy of the analysis according to the user's lifestyle, enabling more accurate analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input current lifestyle data into a generating AI and have the generating AI adjust the analysis method.

[0046] The suggestion unit can select the optimal suggestion method by referring to the user's past meal history when suggesting menus. For example, the suggestion unit can prioritize suggesting menus that the user has enjoyed eating in the past. The suggestion unit can also predict and suggest menus that the user might eat at a specific time of day based on their past meal history. Furthermore, the suggestion unit can analyze the user's past meal history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by referring to the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past meal history data into a generating AI and have the generating AI select the optimal suggestion method.

[0047] The suggestion unit can adjust its menu suggestions based on the user's current physical condition. For example, if the user is tired, the suggestion unit can suggest easily digestible menus. It can also suggest highly nutritious menus if the user is seeking healthy exercise. Furthermore, if the user is unwell, the suggestion unit can suggest menus tailored to their physical condition. This allows for more appropriate menu suggestions by adjusting the suggestions according to the user's physical condition. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input current physical condition data into a generating AI and have the generating AI adjust the suggested menus.

[0048] The multilingual support unit can select the optimal language by referring to the user's past language selection history when providing multilingual support. For example, the multilingual support unit can prioritize displaying languages ​​previously selected by the user. Furthermore, the multilingual support unit can predict and suggest languages ​​to be used during specific time periods based on the user's past language selection history. It can also analyze the user's past language selection history and suggest the most efficient language. This allows for the selection of the optimal language by referring to the user's past language selection history. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input past language selection history data into a generating AI and have the generating AI perform the selection of the optimal language.

[0049] The multilingual support unit can adjust the displayed content considering the user's current language environment when providing multilingual support. For example, if the user is in a specific language environment, the multilingual support unit will prioritize providing content related to that language. Furthermore, if the user is on the move, the multilingual support unit can prioritize providing language related to movement. The multilingual support unit can also suggest optimal displayed content based on the user's current language environment. This allows for more appropriate display by adjusting the displayed content according to the user's current language environment. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input current language environment data into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0050] The multilingual support unit can prioritize the most relevant languages ​​when providing multilingual support, taking into account the user's geographical location. For example, if the user is in a specific location, the multilingual support unit will prioritize displaying languages ​​relevant to that location. Furthermore, if the user is on the move, the multilingual support unit can prioritize displaying languages ​​relevant to their movement. The multilingual support unit can also suggest the most appropriate language based on the user's current location. This allows for more appropriate support by prioritizing the most relevant languages ​​according to the user's geographical location. Some or all of the above processing in the multilingual support unit may be performed using AI, or without AI. For example, the multilingual support unit can input geographical location data into a generating AI and have the generating AI select the most relevant languages.

[0051] The multilingual support unit can analyze the user's social media activity and support relevant languages ​​when providing multilingual support. For example, the multilingual support unit can prioritize displaying relevant languages ​​based on information shared by the user on social media. It can also suggest languages ​​related to specific topics based on the user's social media activity. Furthermore, the multilingual support unit can analyze the content of the user's social media posts and suggest the most appropriate language. This allows for more appropriate support by supporting relevant languages ​​based on the user's social media activity. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input social media activity data into a generating AI and have the generating AI select relevant languages.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The reception unit can select the optimal reception method when receiving a voice command by referring to the user's past command history. For example, it can prioritize receiving voice commands that the user has frequently used in the past. It can also predict and suggest voice commands to be used during specific time periods based on the user's past command history. Furthermore, it can prioritize suggesting voice input methods (voice, text, etc.) that the user has used in the past. In this way, the optimal reception method can be selected by referring to the user's past command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past command history data into a generating AI and have the generating AI select the optimal reception method.

[0054] The reception unit can perform noise cancellation when it receives a voice command, taking into account the user's current ambient noise. For example, if the user is in a noisy environment, the noise cancellation function can be automatically enabled. Conversely, if the user is in a quiet environment, the noise cancellation function can be disabled. Furthermore, it can analyze the user's ambient noise in real time and suggest the optimal noise cancellation settings. This improves the accuracy of receiving voice commands by performing noise cancellation according to the user's ambient noise. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input ambient noise data into a generating AI and have the generating AI execute the noise cancellation settings.

[0055] The data collection unit can select the optimal data collection method by referring to the user's past health data when collecting health data. For example, it can suggest the optimal data collection method based on the health data the user has collected in the past. It can also predict and suggest data to collect at specific time periods based on the user's past health data. Furthermore, it can analyze the user's past health data and suggest the most efficient data collection method. In this way, the optimal data collection method can be selected by referring to the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0056] The analysis unit can select the optimal analysis algorithm by referring to the user's past health data when analyzing health data. For example, it can propose the optimal analysis algorithm based on health data collected by the user in the past. It can also predict and propose data to analyze at a specific time period based on the user's past health data. Furthermore, it can analyze the user's past health data and propose the most efficient analysis algorithm. In this way, the optimal analysis algorithm can be selected by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past health data into a generating AI and have the generating AI perform the selection of the optimal analysis algorithm.

[0057] The suggestion unit can select the optimal suggestion method by referring to the user's past meal history when suggesting menus. For example, it can prioritize suggesting menus that the user has enjoyed eating in the past. It can also predict and suggest menus that the user might eat at a specific time of day based on their past meal history. Furthermore, it can analyze the user's past meal history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by referring to the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past meal history data into a generating AI and have the generating AI select the optimal suggestion method.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives voice commands. For example, if a user gives a voice command such as "Suggest what to have for dinner tonight," the reception desk will receive that voice command. Step 2: The collection unit collects health data based on voice instructions received by the reception unit. For example, if the user is wearing a wearable device, it collects data such as heart rate, body temperature, and activity level. The collection unit can also receive user information about their health condition and allergies through the app. Step 3: The analysis unit analyzes the health data collected by the collection unit. For example, they might use AI to analyze the health data. The AI ​​uses technologies such as deep learning and machine learning to analyze the health data. Step 4: The suggestion department proposes a menu based on the analysis results obtained by the analysis department. For example, it proposes a menu tailored to the user's physical condition and allergy information. If the user gives a voice command saying, "I feel feverish, so I want to eat something warm and easy to digest," the suggestion department will suggest, "How about egg and ginger porridge?" Step 5: The multilingual support department provides the menu proposed by the proposal department in multiple languages. For example, they will make proposals in multiple languages ​​such as English, Spanish, and Chinese.

[0060] (Example of form 2) The home health AI agent system according to an embodiment of the present invention is a system that provides meal suggestions based on the user's health condition. This home health AI agent system integrates a voice assistant device, allowing the user to initiate meal suggestions by voice command. Next, health data is collected from wearable devices and user input information, and the AI ​​analyzes this data. Based on the analysis results, a personalized menu is proposed that is tailored to the user's physical condition and allergy information. Furthermore, under the supervision of a nutritionist, adjustments are made to the nutritional balance and preferences to provide healthy and delicious meals. The suggestions are multilingual, and voice guidance and suggestions can be provided in the user's native language. This system improves the user's health awareness, shortens meal preparation time, and supports healthy lifestyle habits. For example, if the user gives the voice command, "Suggest a dinner for tonight," the AI ​​will start suggesting meals. At this time, the voice assistant device recognizes the user's voice and sends instructions to the AI. Next, health data is collected from wearable devices and user input information, and the AI ​​analyzes this data. For example, if the user is wearing a wearable device, data such as heart rate, body temperature, and activity level are collected. The user can also input physical condition and allergy information through the app. This data is analyzed by AI to understand the user's health status. Based on the analysis results, personalized menus are suggested that are tailored to the user's physical condition and allergy information. For example, if a user says, "I feel feverish, so I want something warm and easy to digest," the AI ​​will suggest, "How about egg and ginger porridge?" In this way, menus are provided that are suited to the user's physical condition. Furthermore, under the supervision of a nutritionist, adjustments are made to ensure nutritional balance and suitability, providing healthy and delicious meals. For example, based on recipes supervised by a nutritionist, the AI ​​adjusts the menu to suit the user's preferences. This ensures that nutritious and delicious meals are provided. Suggestions are multilingual, and voice guidance and suggestions can be provided in the user's native language. For example, suggestions are available in multiple languages ​​such as English, Spanish, and Chinese. This allows for smooth communication even for users with different native languages.This system improves users' health awareness, reduces meal preparation time, and supports healthy lifestyles. For example, users can leave daily meal suggestions to the AI, saving them the trouble of preparing meals. Furthermore, the suggestion of healthy menus increases users' health awareness and helps them adopt healthy lifestyle habits. This allows the home health AI agent system to provide meal suggestions based on the user's health condition.

[0061] The home health AI agent system according to this embodiment comprises a reception unit, a data collection unit, an analysis unit, a suggestion unit, and a multilingual support unit. The reception unit receives voice instructions. For example, the reception unit can receive voice instructions if the user says, "Suggest what to have for dinner tonight." The data collection unit collects health data based on the voice instructions received by the reception unit. For example, if the user is wearing a wearable device, the data collection unit collects data such as heart rate, body temperature, and activity level. The data collection unit can also receive user information about their physical condition and allergies through an app. The analysis unit analyzes the health data collected by the data collection unit. For example, the analysis unit analyzes the health data using AI. For example, the AI ​​analyzes the health data using technologies such as deep learning and machine learning. The suggestion unit suggests menus based on the analysis results obtained by the analysis unit. For example, the suggestion unit suggests menus that are appropriate for the user's physical condition and allergy information. For example, if the user says, "I feel feverish, so I want to eat something warm and easy to digest," the suggestion unit suggests, "How about egg and ginger porridge?" The multilingual support unit provides menus proposed by the proposal unit in multiple languages. The multilingual support unit makes proposals in multiple languages, such as English, Spanish, and Chinese. This allows the home health AI agent system to provide meal suggestions based on the user's health condition.

[0062] The reception desk receives voice commands. For example, if a user gives a voice command such as "Suggest what to have for dinner tonight," the reception desk can receive the command. Specifically, the reception desk combines a high-sensitivity microphone with voice recognition technology to accurately capture the user's voice commands. The voice recognition technology uses natural language processing (NLP) to convert the user's speech into text data. This text data is sent to other departments within the system for further processing. The voice recognition technology has a noise-canceling function, which removes ambient noise and allows for clear recognition of the user's voice. The voice command reception also includes a function to learn the characteristics of the user's voice, so it can accurately recognize the commands of a specific user even in an environment with multiple users. As a result, the reception desk can receive user voice commands quickly and accurately, improving the overall usability of the system.

[0063] The data collection unit collects health data based on voice instructions received by the reception unit. For example, if the user is wearing a wearable device, the data collection unit collects data such as heart rate, body temperature, and activity level. Specifically, the data collection unit acquires data from wearable devices in real time using wireless communication technologies such as Bluetooth and Wi-Fi. This allows for constant monitoring of the user's health status. The data collection unit also allows users to input health and allergy information through an app. The app is designed with an intuitive and user-friendly interface, allowing users to easily input their health information. Furthermore, the data collection unit stores data on a cloud server and can refer to past data as needed. This allows the data collection unit to comprehensively understand the user's health status and provide accurate data to the analysis and recommendation units.

[0064] The Analysis Department analyzes health data collected by the Data Collection Department. For example, the Analysis Department uses AI to analyze health data. Specifically, AI employs technologies such as deep learning and machine learning to comprehensively analyze user health data. Deep learning uses multi-layered neural networks to extract data features and identify patterns in health status. Machine learning builds predictive models based on past data to predict changes in the user's health status. For example, it can detect abnormalities in a user's physical condition early from fluctuations in heart rate and body temperature. Furthermore, the Analysis Department provides foundational data for making health-conscious meal suggestions, taking into account the user's dietary history and allergy information. This allows the Analysis Department to accurately understand the user's health status and provide appropriate information to the Suggestion Department.

[0065] The suggestion department proposes menus based on the analysis results obtained by the analysis department. For example, the suggestion department proposes menus tailored to the user's physical condition and allergy information. Specifically, the suggestion department uses AI to generate menus that take into account the user's health condition and preferences. The AI ​​proposes the optimal meal plan based on the user's past eating history and current health condition. For example, if the user gives a voice command saying, "I feel feverish, so I want to eat something warm and easy to digest," the suggestion department will suggest, "How about egg and ginger porridge?" The suggestion department can provide nutritionally balanced menus according to the user's health condition. In addition, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestions. As a result, the suggestion department can provide optimal meal suggestions for the user's health condition and support the user in maintaining their health.

[0066] The multilingual support unit provides menus proposed by the proposal unit in multiple languages. The multilingual support unit makes proposals in multiple languages, such as English, Spanish, and Chinese. Specifically, the multilingual support unit uses natural language processing (NLP) technology to translate proposals into multiple languages. Because NLP technology understands context and can provide appropriate translations, users can receive proposals in their native language. Furthermore, the multilingual support unit can automatically recognize the user's language settings and make proposals in the appropriate language. This allows the multilingual support unit to support users who speak different languages, expanding the system's usability. In addition, the multilingual support unit can continuously improve translation accuracy based on user feedback. This enables the multilingual support unit to provide users with accurate and appropriate information, improving the system's usability.

[0067] The reception desk can receive voice commands using a voice assistant device. For example, if a user gives a voice command such as "Suggest dinner for tonight" using a voice assistant device, the reception desk can receive the voice command. This allows the user to initiate meal suggestions by voice command using a voice assistant device. Voice assistant devices include, but are not limited to, smart speakers and smartphones. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input voice data obtained from a voice assistant device into a generating AI and have the generating AI perform analysis of the voice command.

[0068] The data collection unit can collect health data from wearable devices and user input information. For example, if the user is wearing a wearable device, the data collection unit can collect data such as heart rate, body temperature, and activity level. The data collection unit can also receive user input on their physical condition and allergy information through an app. This allows the system to understand the user's health status by collecting health data from wearable devices and user input information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input health data acquired from a wearable device into a generating AI and have the generating AI perform analysis of the health data.

[0069] The analysis unit can analyze the collected health data using AI. The analysis unit can, for example, analyze the health data using AI. The AI ​​can analyze the health data using techniques such as deep learning and machine learning. As a result, the accuracy of the health data analysis is improved by using AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the collected health data into a generating AI and have the generating AI perform the analysis of the health data.

[0070] The suggestion unit can propose menus tailored to the user's physical condition and allergy information based on the analysis results. For example, if the user gives a voice command saying, "I feel feverish, so I want something warm and easy to digest," the suggestion unit will suggest, "How about egg and ginger porridge?" This allows for individually optimized meal suggestions by proposing menus tailored to the user's physical condition and allergy information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into a generating AI and have the generating AI execute menu suggestions.

[0071] The multilingual support unit can provide the proposed menu in multiple languages. For example, the multilingual support unit makes suggestions in multiple languages, such as English, Spanish, and Chinese. This allows the system to cater to users who speak various languages ​​by providing menus in multiple languages. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the proposed menu into a generation AI and have the generation AI generate suggestions in multiple languages.

[0072] The reception unit can estimate the user's emotions and adjust how voice instructions are received based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can prioritize voice input and quickly receive voice instructions. This allows for a more appropriate interface by adjusting how voice instructions are received according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception unit can select the optimal reception method when receiving a voice command by referring to the user's past command history. For example, the reception unit can prioritize receiving voice commands that the user has frequently used in the past. The reception unit can also predict and suggest voice commands to be used during specific time periods based on the user's past command history. Furthermore, the reception unit can prioritize suggesting voice input methods (voice, text, etc.) that the user has used in the past. This allows the reception unit to select the optimal reception method by referring to the user's past command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past command history data into a generating AI and have the generating AI select the optimal reception method.

[0074] The reception unit can perform noise cancellation when it receives a voice command, taking into account the user's current ambient noise. For example, if the user is in a noisy environment, the reception unit can automatically enable the noise cancellation function. Conversely, if the user is in a quiet environment, the reception unit can also disable the noise cancellation function. Furthermore, the reception unit can analyze the user's ambient noise in real time and suggest the optimal noise cancellation settings. This improves the accuracy of receiving voice commands by performing noise cancellation according to the user's ambient noise. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input ambient noise data into a generating AI and have the generating AI execute the noise cancellation settings.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect health data at a relaxed time. Alternatively, if the user is relaxed, the data collection unit can collect health data at a normal time. Furthermore, if the user is in a hurry, the data collection unit can collect health data quickly. This allows for data collection at a more appropriate time by adjusting the timing of health data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0076] The data collection unit can select the optimal data collection method by referring to the user's past health data when collecting health data. For example, the data collection unit can propose the optimal data collection method based on the health data the user has collected in the past. The data collection unit can also predict and propose data to be collected at specific time periods based on the user's past health data. Furthermore, the data collection unit can analyze the user's past health data and propose the most efficient data collection method. In this way, the optimal data collection method can be selected by referring to the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can adjust the frequency of data collection, taking into account the user's current activity level, when collecting health data. For example, the unit may collect health data more frequently when the user is exercising. It can also collect health data at a normal frequency when the user is resting. Furthermore, the unit can suggest an optimal collection frequency based on the user's activity level. By adjusting the data collection frequency according to the user's activity level, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current activity data into a generating AI and have the generating AI adjust the collection frequency.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method of health data based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing stress-related data. If the user is relaxed, the analysis unit can also analyze the data using the normal analysis method. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze health data. This allows for more appropriate analysis by adjusting the analysis method of health data 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0079] The analysis unit can select the optimal analysis algorithm by referring to the user's past health data when analyzing health data. For example, the analysis unit can propose the optimal analysis algorithm based on the health data the user has collected in the past. The analysis unit can also predict and propose data to be analyzed at a specific time period based on the user's past health data. Furthermore, the analysis unit can analyze the user's past health data and propose the most efficient analysis algorithm. In this way, the optimal analysis algorithm can be selected by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past health data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0080] The analysis unit can improve the accuracy of its analysis of health data by taking into account the user's current lifestyle. For example, if the user is exercising, the analysis unit will prioritize analyzing data related to exercise. If the user is resting, the analysis unit can analyze the data using the normal analysis method. The analysis unit can also suggest the optimal analysis method according to the user's lifestyle. This improves the accuracy of the analysis according to the user's lifestyle, enabling more accurate analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input current lifestyle data into a generating AI and have the generating AI adjust the analysis method.

[0081] The suggestion unit can estimate the user's emotions and adjust the presentation of menu suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and highly visible menu suggestions. If the user is relaxed, the suggestion unit can also provide menu suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide concise menu suggestions. By adjusting the presentation of menu suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation.

[0082] The suggestion unit can select the optimal suggestion method by referring to the user's past meal history when suggesting menus. For example, the suggestion unit can prioritize suggesting menus that the user has enjoyed eating in the past. The suggestion unit can also predict and suggest menus that the user might eat at a specific time of day based on their past meal history. Furthermore, the suggestion unit can analyze the user's past meal history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by referring to the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past meal history data into a generating AI and have the generating AI select the optimal suggestion method.

[0083] The suggestion unit can adjust its menu suggestions based on the user's current physical condition. For example, if the user is tired, the suggestion unit can suggest easily digestible menus. It can also suggest highly nutritious menus if the user is seeking healthy exercise. Furthermore, if the user is unwell, the suggestion unit can suggest menus tailored to their physical condition. This allows for more appropriate menu suggestions by adjusting the suggestions according to the user's physical condition. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input current physical condition data into a generating AI and have the generating AI adjust the suggested menus.

[0084] The multilingual support unit can estimate the user's emotions and adjust the multilingual display method based on the estimated emotions. For example, if the user is stressed, the multilingual support unit can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. If the user is in a hurry, it can also provide a display method that gets straight to the point. By adjusting the multilingual display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or not using AI. For example, the multilingual support unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0085] The multilingual support unit can select the optimal language by referring to the user's past language selection history when providing multilingual support. For example, the multilingual support unit can prioritize displaying languages ​​previously selected by the user. Furthermore, the multilingual support unit can predict and suggest languages ​​to be used during specific time periods based on the user's past language selection history. It can also analyze the user's past language selection history and suggest the most efficient language. This allows for the selection of the optimal language by referring to the user's past language selection history. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input past language selection history data into a generating AI and have the generating AI perform the selection of the optimal language.

[0086] The multilingual support unit can adjust the displayed content considering the user's current language environment when providing multilingual support. For example, if the user is in a specific language environment, the multilingual support unit will prioritize providing content related to that language. Furthermore, if the user is on the move, the multilingual support unit can prioritize providing language related to movement. The multilingual support unit can also suggest optimal displayed content based on the user's current language environment. This allows for more appropriate display by adjusting the displayed content according to the user's current language environment. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input current language environment data into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0087] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the user is stressed, the multilingual support unit will prioritize displaying the user's native language. If the user is relaxed, the multilingual support unit can also display languages ​​with normal priority. Furthermore, if the user is in a hurry, the multilingual support unit can prioritize displaying the language that can be understood quickly. This allows for a more appropriate response by determining the priority of multilingual support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, or not using AI. For example, the multilingual support unit can input user emotion data into the generative AI and have the generative AI perform the priority determination.

[0088] The multilingual support unit can prioritize the most relevant languages ​​when providing multilingual support, taking into account the user's geographical location. For example, if the user is in a specific location, the multilingual support unit will prioritize displaying languages ​​relevant to that location. Furthermore, if the user is on the move, the multilingual support unit can prioritize displaying languages ​​relevant to their movement. The multilingual support unit can also suggest the most appropriate language based on the user's current location. This allows for more appropriate support by prioritizing the most relevant languages ​​according to the user's geographical location. Some or all of the above processing in the multilingual support unit may be performed using AI, or without AI. For example, the multilingual support unit can input geographical location data into a generating AI and have the generating AI select the most relevant languages.

[0089] The multilingual support unit can analyze the user's social media activity and support relevant languages ​​when providing multilingual support. For example, the multilingual support unit can prioritize displaying relevant languages ​​based on information shared by the user on social media. It can also suggest languages ​​related to specific topics based on the user's social media activity. Furthermore, the multilingual support unit can analyze the content of the user's social media posts and suggest the most appropriate language. This allows for more appropriate support by supporting relevant languages ​​based on the user's social media activity. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input social media activity data into a generating AI and have the generating AI select relevant languages.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception unit can estimate the user's emotions and adjust how voice instructions are received based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick reception of voice instructions. This allows for a more appropriate interface to be provided by adjusting how voice instructions are received 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. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0092] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, health data can be collected when the user is relaxed. If the user is relaxed, health data can be collected at normal times. Furthermore, if the user is in a hurry, health data can be collected quickly. By adjusting the timing of health data collection according to the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis method of health data based on the estimated user emotions. For example, if the user is stressed, stress-related data will be prioritized for analysis. If the user is relaxed, the data can be analyzed using the normal analysis method. Furthermore, if the user is in a hurry, health data can be analyzed quickly. This allows for more appropriate analysis by adjusting the analysis method of health data 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0094] The suggestion unit can estimate the user's emotions and adjust the presentation of menu suggestions based on those emotions. For example, if the user is stressed, it can provide simple and highly visible menu suggestions. If the user is relaxed, it can provide menu suggestions that include detailed information. Furthermore, if the user is in a hurry, it can provide menu suggestions that get straight to the point. By adjusting the presentation of menu suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the presentation.

[0095] The multilingual support unit can estimate the user's emotions and adjust the multilingual display method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the multilingual display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or not using AI. For example, the multilingual support unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0096] The reception unit can select the optimal reception method when receiving a voice command by referring to the user's past command history. For example, it can prioritize receiving voice commands that the user has frequently used in the past. It can also predict and suggest voice commands to be used during specific time periods based on the user's past command history. Furthermore, it can prioritize suggesting voice input methods (voice, text, etc.) that the user has used in the past. In this way, the optimal reception method can be selected by referring to the user's past command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past command history data into a generating AI and have the generating AI select the optimal reception method.

[0097] The reception unit can perform noise cancellation when it receives a voice command, taking into account the user's current ambient noise. For example, if the user is in a noisy environment, the noise cancellation function can be automatically enabled. Conversely, if the user is in a quiet environment, the noise cancellation function can be disabled. Furthermore, it can analyze the user's ambient noise in real time and suggest the optimal noise cancellation settings. This improves the accuracy of receiving voice commands by performing noise cancellation according to the user's ambient noise. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input ambient noise data into a generating AI and have the generating AI execute the noise cancellation settings.

[0098] The data collection unit can select the optimal data collection method by referring to the user's past health data when collecting health data. For example, it can suggest the optimal data collection method based on the health data the user has collected in the past. It can also predict and suggest data to collect at specific time periods based on the user's past health data. Furthermore, it can analyze the user's past health data and suggest the most efficient data collection method. In this way, the optimal data collection method can be selected by referring to the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past health data into a generating AI and have the generating AI select the optimal data collection method.

[0099] The analysis unit can select the optimal analysis algorithm by referring to the user's past health data when analyzing health data. For example, it can propose the optimal analysis algorithm based on health data collected by the user in the past. It can also predict and propose data to analyze at a specific time period based on the user's past health data. Furthermore, it can analyze the user's past health data and propose the most efficient analysis algorithm. In this way, the optimal analysis algorithm can be selected by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past health data into a generating AI and have the generating AI perform the selection of the optimal analysis algorithm.

[0100] The suggestion unit can select the optimal suggestion method by referring to the user's past meal history when suggesting menus. For example, it can prioritize suggesting menus that the user has enjoyed eating in the past. It can also predict and suggest menus that the user might eat at a specific time of day based on their past meal history. Furthermore, it can analyze the user's past meal history and select the most efficient suggestion method. In this way, the optimal suggestion method can be selected by referring to the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past meal history data into a generating AI and have the generating AI select the optimal suggestion method.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives voice commands. For example, if a user gives a voice command such as "Suggest what to have for dinner tonight," the reception desk will receive that voice command. Step 2: The collection unit collects health data based on voice instructions received by the reception unit. For example, if the user is wearing a wearable device, it collects data such as heart rate, body temperature, and activity level. The collection unit can also receive user information about their health condition and allergies through the app. Step 3: The analysis unit analyzes the health data collected by the collection unit. For example, they might use AI to analyze the health data. The AI ​​uses technologies such as deep learning and machine learning to analyze the health data. Step 4: The suggestion department proposes a menu based on the analysis results obtained by the analysis department. For example, it proposes a menu tailored to the user's physical condition and allergy information. If the user gives a voice command saying, "I feel feverish, so I want to eat something warm and easy to digest," the suggestion department will suggest, "How about egg and ginger porridge?" Step 5: The multilingual support department provides the menu proposed by the proposal department in multiple languages. For example, they will make proposals in multiple languages ​​such as English, Spanish, and Chinese.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and multilingual support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and receives voice commands from the user. The collection unit collects health data from the wearable device via the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected health data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a menu based on the analysis results. The multilingual support unit is implemented by the control unit 46A of the smart device 14 and provides the proposed menu in multiple languages. 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.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and multilingual support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and receives voice commands from the user. The collection unit collects health data from the wearable device via the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected health data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a menu based on the analysis results. The multilingual support unit is implemented by the control unit 46A of the smart glasses 214 and provides the proposed menu in multiple languages. 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.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and multilingual support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and receives voice instructions from the user. The collection unit collects health data from the wearable device via the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected health data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a menu based on the analysis results. The multilingual support unit is implemented by the control unit 46A of the headset terminal 314 and provides the proposed menu in multiple languages. 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.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, proposal unit, and multilingual support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414 and receives voice instructions from the user. The collection unit collects health data from a wearable device via the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected health data using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a menu based on the analysis results. The multilingual support unit is implemented by the control unit 46A of the robot 414 and provides the proposed menu in multiple languages. 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A reception desk that accepts voice commands, A collection unit that collects health data based on voice instructions received by the reception unit, An analysis unit analyzes the health data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a menu, The system includes a multilingual support unit that provides the menu proposed by the aforementioned proposal unit in multiple languages. A system characterized by the following features. (Note 2) The aforementioned reception unit is Voice assistant devices accept voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect health data from wearable devices and user input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is The collected health data is analyzed using AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we propose menus tailored to the user's health condition and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned multilingual support unit is The proposed menu will be offered in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving a voice command, the system selects the optimal method of receiving the command by referring to the user's past command history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving voice commands, noise cancellation is performed while taking into account the user's current ambient noise. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, the system selects the optimal collection method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, the frequency of data collection is adjusted considering the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of health data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing health data, the system selects the optimal analysis algorithm by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing health data, we improve the accuracy of the analysis by taking into account the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the presentation of menu suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When suggesting menus, the system selects the most suitable suggestion method by referring to the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When suggesting menu items, we adjust the suggestions based on the user's current physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the display method for multilingual support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned multilingual support unit is When supporting multiple languages, the system selects the most suitable language by referring to the user's past language selection history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned multilingual support unit is When supporting multiple languages, the displayed content is adjusted to take into account the user's current language environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned multilingual support unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned multilingual support unit is When supporting multiple languages, the system prioritizes the most relevant languages, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned multilingual support unit is When implementing multilingual support, we analyze users' social media activity and assign support to relevant languages. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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. A reception desk that accepts voice commands, A collection unit that collects health data based on voice instructions received by the reception unit, An analysis unit analyzes the health data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a menu, The system includes a multilingual support unit that provides the menu proposed by the proposal unit in multiple languages. A system characterized by the following features.

2. The aforementioned reception unit is Voice assistant devices accept voice commands. The system according to feature 1.

3. The aforementioned collection unit is Collect health data from wearable devices and user input. The system according to feature 1.

4. The aforementioned analysis unit is The collected health data is analyzed using AI. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we propose menus tailored to the user's health condition and allergy information. The system according to feature 1.

6. The aforementioned multilingual support unit is The proposed menu will be offered in multiple languages. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving a voice command, the system selects the optimal method of receiving the command by referring to the user's past command history. The system according to feature 1.

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