System

Abstract

An object of a system according to an embodiment is to easily record a meal using generated AI and provide effective improvement advice to a user.SOLUTION: A system includes a meal recording part, a meal report generation part, and a dietitian supervision part. The meal recording unit performs meal recording using the generated AI. The meal report generation unit generates a meal report based on the meal contents recorded by the meal recording unit. The dietitian supervision unit allows a dietitian to supervise the meal report generated by the meal report generation unit and provides improvement advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, recording data for meal management apps is a time-consuming process, which creates challenges for app retention rates.

[0005] The system of the embodiment aims to use a generation AI to easily record meals and provide effective improvement advice to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal recording unit, a meal report generation unit, and a dietitian supervision unit. The meal recording unit records meals using a generation AI. The meal report generation unit generates a meal report based on the meal details recorded by the meal recording unit. The dietitian supervision unit has a dietitian supervise the meal report generated by the meal report generation unit and provide improvement advice to the user. [Effects of the Invention]

[0007] The system according to the embodiment uses a generation AI to easily record meals and provide effective advice on improvement to the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A dietary management system according to an embodiment of the present invention uses a generative AI to help users record their meals more easily. The system includes a meal recording unit, a meal report generation unit, and a nutritionist supervision unit. This allows users to effortlessly record their meals and receive specific advice on maintaining a healthy diet.

[0029] A dietary management system according to an embodiment includes a dietary recorder, a dietary report generator, and a dietitian supervision unit. The dietary recorder uses a generation AI to record dietary information. For example, when a user inputs a dietary content by voice, the generation AI analyzes the input and reflects it in the dietary record. The dietary recorder also allows users to record by text input or photography. For example, if a user inputs "I had toast and coffee for breakfast" by voice, the generation AI analyzes the input and reflects it in the dietary record. The dietary report generator generates a dietary report based on the dietary content recorded by the dietary recorder. For example, the generation AI analyzes the dietary content for one day and creates a detailed dietary report including calorie intake, nutritional balance, and areas for improvement. The generation AI evaluates nutrient deficiencies and excesses based on the dietary content input by the user and generates a report. For example, specific findings such as "Today's meal is lacking in vitamin C" are included. The dietitian supervision unit has a dietitian supervise the dietary report generated by the dietary report generator and provide the user with advice on how to improve the dietary report. For example, a nutritionist may review the report generated by the AI ​​and provide corrections or additional advice as necessary. For example, advice such as "Try including fruits rich in vitamin C in your meal tomorrow" may be provided. This allows the dietary management system according to the embodiment to effortlessly record meals and receive specific advice for maintaining a healthy diet. For example, even in busy daily lives, users can easily record meals and maintain a balanced diet. Furthermore, receiving reliable advice supervised by a nutritionist can improve the quality of health management.

[0030] The meal recording unit can analyze the user's voice input and record the meal details. For example, when the user inputs the meal details by voice, the generation AI analyzes the tone and speed of the voice, estimates the stress and fatigue levels, and reflects them in the meal details. For example, if the user inputs "I had toast and coffee for breakfast," the generation AI analyzes that content and reflects it in the meal record. In addition, the meal recording unit analyzes changes in the user's voice in real time during voice input and estimates the stress and fatigue levels. For example, a sudden change in voice tone can be determined to indicate increased stress. In addition, the meal recording unit analyzes the voice data and estimates the stress and fatigue levels from the user's voice tone and speed, and reflects them in the meal details. For example, if the voice is trembling, it can be determined to indicate high stress and reflect this in the meal details. This allows the user to easily record the meal details through voice input.

[0031] The meal report generation unit can reference the user's past meal history and automatically complete meal details. In the meal report generation unit, for example, the generation AI references the user's past meal history and automatically completes similar meal details. For example, if a user enters "toast for breakfast," "coffee" is automatically completed from the past history. In addition, when recording meals, the generation AI analyzes the past meal history and automatically completes frequently consumed meal details. For example, if a user enters "salad for lunch," "chicken" is automatically completed from the past history. In addition, the meal report generation unit provides a function in which the generation AI automatically completes similar meal details based on the user's past meal history. For example, if a user enters "pasta for dinner," "garlic bread" is automatically completed from the past history. This allows meal details to be automatically completed based on the user's past meal history.

[0032] The meal recording unit can automatically recognize the user's meal environment and reflect it in the record. In the meal recording unit, for example, the generation AI automatically recognizes the user's meal environment and reflects it in the record. For example, it uses GPS data to detect that the user is at a restaurant and reflects this in the meal record. In addition, when recording meals, the generation AI analyzes the user's meal environment and automatically reflects the location and time of day in the record. For example, it detects that the user is having breakfast at home and reflects this in the record. In addition, the meal recording unit provides a function whereby the generation AI automatically recognizes the user's meal environment and reflects this in the record. For example, it detects that the user is having lunch at the office and reflects this in the record. In this way, the user's meal environment can be automatically recognized and reflected in the record.

[0033] The meal recording unit can analyze the user's gestures or facial expressions to complete the meal details. For example, the meal recording unit provides a function in which the generation AI analyzes the user's gestures and facial expressions to complete the meal details. For example, the user can use the smartphone camera while eating to complete the meal details. In addition to voice input of the meal details, the meal recording unit also provides a function in which the generation AI analyzes the user's gestures and facial expressions to complete the meal details. For example, if the user smiles while eating, positive meal details will be completed. In addition, the meal recording unit provides a function in which the generation AI analyzes the user's gestures and facial expressions to complete the meal details. For example, if the user makes a gesture with their hand to indicate food while eating, the meal details will be completed. This allows the user's gestures and facial expressions to be analyzed and the meal details to be completed.

[0034] The meal report generation unit can refer to the user's past health data and provide a personalized meal report. For example, the generation AI of the meal report generation unit refers to the user's past health data and provides a personalized meal report. For example, the generation AI may specifically indicate areas for improvement in the diet based on the user's weight and blood pressure data. Furthermore, when creating a meal report, the generation AI analyzes the user's past health data and provides personalized advice. For example, if the user's blood pressure is high, the generation AI may advise the user to reduce their salt intake. Furthermore, the meal report generation unit creates a personalized meal report based on the user's past health data. For example, the generation AI may take into account fluctuations in the user's weight and suggest adjustments to calorie intake. This makes it possible to provide a personalized meal report based on the user's past health data.

[0035] The meal report generation unit can automatically generate a meal report in different formats and allow the user to select from them. For example, the generation AI of the meal report generation unit automatically generates a meal report in different formats and allows the user to select from them. For example, calorie intake is displayed in a graph or chart. The meal report generation unit can also automatically generate a meal report in different formats and allow the user to select from them. For example, nutritional balance is displayed in a pie chart or bar graph. The meal report generation unit can also automatically generate a meal report in different formats and allow the user to select from them. For example, meal contents are displayed in a timeline format or a list format. This allows the user to select a meal report in different formats.

[0036] The dietary report generation unit can provide a comprehensive health report by taking into account the user's lifestyle habits. For example, when the generation AI creates a dietary report, the dietary report generation unit also takes into account the user's lifestyle habits (e.g., amount of exercise and sleep time) and provides a comprehensive health report. For example, the generation AI can specifically indicate areas for improvement in dietary content based on the amount of exercise and sleep time. Furthermore, when creating a dietary report, the generation AI analyzes the user's lifestyle habits and provides comprehensive advice. For example, if the user is not getting enough exercise, the generation AI can advise the user to increase the amount of exercise. Furthermore, the dietary report generation unit can create a comprehensive health report by taking into account the user's lifestyle habits. For example, if the user is sleeping less, the generation AI can provide dietary advice to improve sleep. This makes it possible to provide a comprehensive health report by taking into account the user's lifestyle habits.

[0037] The nutritionist supervision department enables nutritionists to add comments and advice in real time to the meal report created by the generation AI. For example, the nutritionist supervision department provides a function that allows nutritionists to add comments and advice in real time to the meal report created by the generation AI. For example, a nutritionist can enter comments directly into the report and provide feedback to the user. The nutritionist supervision department also provides a function that allows nutritionists to add advice in real time to the meal report created by the generation AI. For example, a nutritionist can add supplementary information or specific improvement suggestions to the report. The nutritionist supervision department also builds a system that allows nutritionists to add comments and advice in real time to the meal report created by the generation AI. For example, a nutritionist can add an audio message to the report. This allows nutritionists to add comments and advice in real time.

[0038] The nutritionist supervision department enables the generation AI to automatically present the user's dietary history and health data, allowing the nutritionist to provide more accurate advice. For example, the nutritionist supervision department provides a function whereby the generation AI automatically presents the user's dietary history and health data when a nutritionist is supervising. For example, the nutritionist can check the user's past dietary history and provide accurate advice. The nutritionist supervision department also enables the generation AI to automatically present the user's dietary history and health data, allowing the nutritionist to provide more accurate advice. For example, advice can be provided based on the user's weight fluctuations and blood pressure data. The nutritionist supervision department also builds a system whereby the generation AI automatically presents the user's dietary history and health data when a nutritionist is supervising. For example, the nutritionist can check the user's health data in real time and provide advice. This allows the nutritionist to provide more accurate advice.

[0039] The nutritionist supervision department can enable the generation AI to automatically translate into different languages, making it possible to accommodate international users. For example, the nutritionist supervision department provides a function that enables the generation AI to automatically translate nutritionist supervision and advice into different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The nutritionist supervision department can also enable the generation AI to automatically translate nutritionist advice into different languages, making it possible to accommodate international users. For example, it can provide advice in the language selected by the user. The nutritionist supervision department can also build a system that enables the generation AI to automatically translate nutritionist supervision and advice into different languages. For example, it can enable users to receive advice in their native language. This makes it possible to accommodate international users.

[0040] The nutritionist supervision department allows the generation AI to provide comprehensive advice that incorporates the opinions of other experts. For example, when a nutritionist supervises, the nutritionist supervision department allows the generation AI to provide comprehensive advice that incorporates the opinions of other experts (e.g., doctors and fitness trainers). For example, health management advice is provided based on the opinion of a doctor. In addition, the nutritionist supervision department allows the generation AI to incorporate the opinions of other experts and the nutritionist provides comprehensive advice. For example, it suggests a balance between exercise and diet based on the opinion of a fitness trainer. In addition, the nutritionist supervision department builds a system where, when a nutritionist supervises, the generation AI automatically provides advice that incorporates the opinions of other experts. For example, it provides advice that integrates the opinions of doctors and fitness trainers. This allows for comprehensive advice that incorporates the opinions of other experts.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] When recording the user's meal contents, the meal recording unit can automatically obtain the origin and production method of ingredients and reflect this in the record. For example, if a user inputs "I ate tomato salad," the generation AI automatically obtains the origin of the tomatoes and whether they were organically grown, and reflects this in the record. The meal recording unit can also automatically obtain the nutritional value and allergen information of ingredients and reflect this in the record. For example, if a user inputs "I drank milk," the generation AI automatically obtains the nutritional value and allergen information of the milk and reflects this in the record. This allows users to easily record detailed information about ingredients.

[0043] The meal report generator can evaluate the environmental impact based on the user's meal content. For example, the generation AI analyzes the meal content, calculates the carbon footprint of the ingredients, and reflects this in the report. The meal report generator can also evaluate the environmental impact of the meal content and recommend sustainable meal choices. For example, if a user inputs "I ate steak," the generation AI evaluates the environmental impact of that meal and recommends, "Next time, try choosing plant-based ingredients." The meal report generator can also evaluate the environmental impact based on the user's meal content and suggest specific areas for improvement. For example, it could advise, "Today's meal had a high environmental impact, so next time, try choosing locally produced ingredients." This allows the user to make environmentally conscious meal choices.

[0044] When recording the user's meal contents, the meal recording unit can automatically obtain the storage and cooking methods of ingredients and reflect them in the record. For example, if a user inputs, "I ate grilled chicken," the generation AI automatically obtains the storage and cooking methods for the chicken and reflects them in the record. The meal recording unit can also automatically obtain the storage period and cooking time of ingredients and reflect them in the record. For example, if a user inputs, "I used frozen vegetables," the generation AI automatically obtains the storage period and cooking time and reflects them in the record. This allows users to easily record how they store and cook ingredients.

[0045] When recording a user's meal, the meal recording unit can automatically obtain allergen information for ingredients and reflect it in the record. For example, if a user inputs "I ate peanut butter," the generation AI automatically obtains peanut allergen information and reflects it in the record. The meal recording unit can also evaluate allergy risk based on the allergen information for ingredients and provide a warning to the user. For example, if a user inputs "I ate shrimp," the generation AI obtains shrimp allergen information, evaluates allergy risk, and provides a warning. The meal recording unit can also automatically obtain allergen information for ingredients based on the user's allergy history and reflect it in the record. This allows users to easily record allergen information and manage allergy risk.

[0046] When recording the user's meal contents, the meal recording unit can automatically obtain the nutritional value of ingredients and reflect it in the record. For example, if the user inputs "I ate broccoli," the generation AI automatically obtains the nutritional value of broccoli and reflects it in the record. The meal recording unit can also evaluate the user's nutritional balance based on the nutritional value of ingredients and suggest specific areas for improvement. For example, if the user inputs "I ate cheese," the generation AI obtains the nutritional value of cheese, evaluates the calcium intake, and suggests areas for improvement. The meal recording unit can also automatically obtain nutritional value based on the user's meal contents and reflect it in the record. This allows users to easily record the nutritional value of ingredients and manage their nutritional balance.

[0047] When recording the contents of a user's meal, the meal recording unit can automatically obtain ingredient price information and reflect it in the record. For example, if a user inputs "I ate salmon," the generation AI automatically obtains salmon price information and reflects it in the record. The meal recording unit can also provide advice for managing the user's food expenses based on the ingredient price information. For example, if a user inputs "I ate steak," the generation AI obtains steak price information and provides advice for managing food expenses. The meal recording unit can also automatically obtain ingredient price information based on the user's meal contents and reflect it in the record. This allows users to easily record ingredient price information and manage their food expenses.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The meal recording unit uses the generation AI to record meals. For example, when a user inputs meal details by voice, the generation AI analyzes the details and reflects them in the meal record. Users can also record by entering text or taking photos. For example, if a user inputs by voice, "I had toast and coffee for breakfast," the generation AI analyzes the details and reflects them in the meal record. Step 2: The meal report generator generates a meal report based on the meal details recorded by the meal recording unit. For example, the generation AI analyzes the meal details for the day and creates a detailed meal report including calorie intake, nutritional balance, and areas for improvement. Based on the meal details entered by the user, the generation AI evaluates nutrient deficiencies and balance and generates a report. For example, it may include specific indications such as "Today's meal is lacking in vitamin C." Step 3: In the nutritionist supervision department, a nutritionist supervises the dietary report generated by the dietary report generation department and provides improvement advice to the user. For example, the nutritionist checks the report generated by the generation AI and makes corrections or additional advice as necessary. For example, advice such as "Try including fruits rich in vitamin C in tomorrow's meal" is provided.

[0050] (Example 2) A dietary management system according to an embodiment of the present invention uses a generative AI to help users record their meals more easily. The system includes a meal recording unit, a meal report generation unit, and a nutritionist supervision unit. This allows users to effortlessly record their meals and receive specific advice on maintaining a healthy diet.

[0051] A dietary management system according to an embodiment includes a dietary recorder, a dietary report generator, and a dietitian supervision unit. The dietary recorder uses a generation AI to record dietary information. For example, when a user inputs a dietary content by voice, the generation AI analyzes the input and reflects it in the dietary record. The dietary recorder also allows users to record by text input or photography. For example, if a user inputs "I had toast and coffee for breakfast" by voice, the generation AI analyzes the input and reflects it in the dietary record. The dietary report generator generates a dietary report based on the dietary content recorded by the dietary recorder. For example, the generation AI analyzes the dietary content for one day and creates a detailed dietary report including calorie intake, nutritional balance, and areas for improvement. The generation AI evaluates nutrient deficiencies and excesses based on the dietary content input by the user and generates a report. For example, specific findings such as "Today's meal is lacking in vitamin C" are included. The dietitian supervision unit has a dietitian supervise the dietary report generated by the dietary report generator and provide the user with advice on how to improve the dietary report. For example, a nutritionist may review the report generated by the AI ​​and provide corrections or additional advice as necessary. For example, advice such as "Try including fruits rich in vitamin C in your meal tomorrow" may be provided. This allows the dietary management system according to the embodiment to effortlessly record meals and receive specific advice for maintaining a healthy diet. For example, even in busy daily lives, users can easily record meals and maintain a balanced diet. Furthermore, receiving reliable advice supervised by a nutritionist can improve the quality of health management.

[0052] The meal recording unit can analyze the user's voice input and record the meal details. For example, when the user inputs the meal details by voice, the generation AI analyzes the tone and speed of the voice, estimates the stress and fatigue levels, and reflects them in the meal details. For example, if the user inputs "I had toast and coffee for breakfast," the generation AI analyzes that content and reflects it in the meal record. In addition, the meal recording unit analyzes changes in the user's voice in real time during voice input and estimates the stress and fatigue levels. For example, a sudden change in voice tone can be determined to indicate increased stress. In addition, the meal recording unit analyzes the voice data and estimates the stress and fatigue levels from the user's voice tone and speed, and reflects them in the meal details. For example, if the voice is trembling, it can be determined to indicate high stress and reflect this in the meal details. This allows the user to easily record the meal details through voice input.

[0053] The meal report generation unit can reference the user's past meal history and automatically complete meal details. In the meal report generation unit, for example, the generation AI references the user's past meal history and automatically completes similar meal details. For example, if a user enters "toast for breakfast," "coffee" is automatically completed from the past history. In addition, when recording meals, the generation AI analyzes the past meal history and automatically completes frequently consumed meal details. For example, if a user enters "salad for lunch," "chicken" is automatically completed from the past history. In addition, the meal report generation unit provides a function in which the generation AI automatically completes similar meal details based on the user's past meal history. For example, if a user enters "pasta for dinner," "garlic bread" is automatically completed from the past history. This allows meal details to be automatically completed based on the user's past meal history.

[0054] The meal report generation unit can analyze the user's emotional state and provide feedback to elicit positive emotions. For example, the meal report generation unit uses an emotion estimation function to analyze the emotions of the user when inputting meal details and provide feedback to elicit positive emotions. For example, if the user inputs, "I ate a lot of vegetables today," the feedback is "Great choice!" The meal report generation unit also analyzes the user's emotions and provides feedback to elicit positive emotions. For example, if the user inputs, "I ate a little too much today," the feedback is "Try adding a salad next time to balance it out." The meal report generation unit also uses the emotion estimation function to analyze the user's emotions and provide feedback to elicit positive emotions. For example, if the user inputs, "I ate a healthy meal today," the feedback is "Great! Keep it up!" This makes it possible to provide positive feedback according to the user's emotional state.

[0055] The meal recording unit can automatically recognize the user's meal environment and reflect it in the record. In the meal recording unit, for example, the generation AI automatically recognizes the user's meal environment and reflects it in the record. For example, it uses GPS data to detect that the user is at a restaurant and reflects this in the meal record. In addition, when recording meals, the generation AI analyzes the user's meal environment and automatically reflects the location and time of day in the record. For example, it detects that the user is having breakfast at home and reflects this in the record. In addition, the meal recording unit provides a function whereby the generation AI automatically recognizes the user's meal environment and reflects this in the record. For example, it detects that the user is having lunch at the office and reflects this in the record. In this way, the user's meal environment can be automatically recognized and reflected in the record.

[0056] The meal recording unit can analyze the user's gestures or facial expressions to complete the meal details. For example, the meal recording unit provides a function in which the generation AI analyzes the user's gestures and facial expressions to complete the meal details. For example, the user can use the smartphone camera while eating to complete the meal details. In addition to voice input of the meal details, the meal recording unit also provides a function in which the generation AI analyzes the user's gestures and facial expressions to complete the meal details. For example, if the user smiles while eating, positive meal details will be completed. In addition, the meal recording unit provides a function in which the generation AI analyzes the user's gestures and facial expressions to complete the meal details. For example, if the user makes a gesture with their hand to indicate food while eating, the meal details will be completed. This allows the user's gestures and facial expressions to be analyzed and the meal details to be completed.

[0057] The meal report generation unit can analyze the user's emotional state in real time and provide advice to reduce negative emotions. For example, the meal report generation unit uses an emotion estimation function to analyze the user's emotions in real time when inputting meal details and provide advice to reduce negative emotions. For example, if the user inputs, "I ate too much today," the unit may advise, "Try adding a salad next time to balance it out." The meal report generation unit also analyzes the user's emotions in real time and provides advice to reduce negative emotions. For example, if the user inputs, "I ate too much today because of stress," the unit may advise, "Try making healthier choices next time." The meal report generation unit also analyzes the user's emotions in real time and provides advice to reduce negative emotions using the emotion estimation function. For example, if the user inputs, "I ate too much today because of stress," the unit may advise, "Try taking a walk to relax." This allows the unit to provide advice to reduce the user's negative emotions.

[0058] The meal report generation unit can refer to the user's past health data and provide a personalized meal report. For example, the generation AI of the meal report generation unit refers to the user's past health data and provides a personalized meal report. For example, the generation AI may specifically indicate areas for improvement in the diet based on the user's weight and blood pressure data. Furthermore, when creating a meal report, the generation AI analyzes the user's past health data and provides personalized advice. For example, if the user's blood pressure is high, the generation AI may advise the user to reduce their salt intake. Furthermore, the meal report generation unit creates a personalized meal report based on the user's past health data. For example, the generation AI may take into account fluctuations in the user's weight and suggest adjustments to calorie intake. This makes it possible to provide a personalized meal report based on the user's past health data.

[0059] The meal report generation unit reflects the user's emotional state and can also evaluate the emotional health state. The meal report generation unit, for example, uses an emotion estimation function to reflect the user's emotional state in the meal report. For example, if the user is feeling stressed, the emotional state is recorded in the report and dietary advice for stress reduction is provided. The meal report generation unit also reflects the user's emotional state in the meal report and evaluates the emotional health state. For example, if the user is feeling positive, the emotional state is recorded in the report and positive dietary choices are recommended. The meal report generation unit also uses the emotion estimation function to reflect the user's emotional state in the meal report and evaluate the emotional health state. For example, if the user is feeling anxious, the emotional state is recorded in the report and dietary advice for anxiety reduction is provided. In this way, the user's emotional state can be reflected and the emotional health state can be evaluated.

[0060] The meal report generation unit can automatically generate a meal report in different formats and allow the user to select from them. For example, the generation AI of the meal report generation unit automatically generates a meal report in different formats and allows the user to select from them. For example, calorie intake is displayed in a graph or chart. The meal report generation unit can also automatically generate a meal report in different formats and allow the user to select from them. For example, nutritional balance is displayed in a pie chart or bar graph. The meal report generation unit can also automatically generate a meal report in different formats and allow the user to select from them. For example, meal contents are displayed in a timeline format or a list format. This allows the user to select a meal report in different formats.

[0061] The dietary report generation unit can provide a comprehensive health report by taking into account the user's lifestyle habits. For example, when the generation AI creates a dietary report, the dietary report generation unit also takes into account the user's lifestyle habits (e.g., amount of exercise and sleep time) and provides a comprehensive health report. For example, the generation AI can specifically indicate areas for improvement in dietary content based on the amount of exercise and sleep time. Furthermore, when creating a dietary report, the generation AI analyzes the user's lifestyle habits and provides comprehensive advice. For example, if the user is not getting enough exercise, the generation AI can advise the user to increase the amount of exercise. Furthermore, the dietary report generation unit can create a comprehensive health report by taking into account the user's lifestyle habits. For example, if the user is sleeping less, the generation AI can provide dietary advice to improve sleep. This makes it possible to provide a comprehensive health report by taking into account the user's lifestyle habits.

[0062] The meal report generation unit can collect the user's emotional responses and provide feedback to improve the content of the report. For example, the meal report generation unit uses an emotion estimation function to collect the user's emotional responses to the meal report and provide feedback to improve the content of the report. For example, if the user expresses positive emotions toward the report, the content is reinforced. The meal report generation unit also collects the user's emotional responses and provides feedback to improve the content of the report. For example, if the user expresses negative emotions, the unit suggests correcting that part. The meal report generation unit also uses the emotion estimation function to collect the user's emotional responses to the meal report in real time and provide feedback to improve the content of the report. For example, the content of the report is adjusted based on the user's emotion score. This makes it possible to provide feedback to improve the content of the report based on the user's emotional responses.

[0063] The nutritionist supervision department enables nutritionists to add comments and advice in real time to the meal report created by the generation AI. For example, the nutritionist supervision department provides a function that allows nutritionists to add comments and advice in real time to the meal report created by the generation AI. For example, a nutritionist can enter comments directly into the report and provide feedback to the user. The nutritionist supervision department also provides a function that allows nutritionists to add advice in real time to the meal report created by the generation AI. For example, a nutritionist can add supplementary information or specific improvement suggestions to the report. The nutritionist supervision department also builds a system that allows nutritionists to add comments and advice in real time to the meal report created by the generation AI. For example, a nutritionist can add an audio message to the report. This allows nutritionists to add comments and advice in real time.

[0064] The nutritionist supervision department enables the generation AI to automatically present the user's dietary history and health data, allowing the nutritionist to provide more accurate advice. For example, the nutritionist supervision department provides a function whereby the generation AI automatically presents the user's dietary history and health data when a nutritionist is supervising. For example, the nutritionist can check the user's past dietary history and provide accurate advice. The nutritionist supervision department also enables the generation AI to automatically present the user's dietary history and health data, allowing the nutritionist to provide more accurate advice. For example, advice can be provided based on the user's weight fluctuations and blood pressure data. The nutritionist supervision department also builds a system whereby the generation AI automatically presents the user's dietary history and health data when a nutritionist is supervising. For example, the nutritionist can check the user's health data in real time and provide advice. This allows the nutritionist to provide more accurate advice.

[0065] The nutritionist supervision unit uses the emotion estimation function to enable the nutritionist to grasp the user's emotional state and provide advice that takes the emotion into consideration. The nutritionist supervision unit, for example, uses the emotion estimation function to enable the nutritionist to grasp the user's emotional state and provide advice that takes the emotion into consideration. For example, if the user is feeling stressed, the nutritionist supervision unit makes a meal suggestion that will help the user relax. The nutritionist supervision unit also uses the emotion estimation function to enable the nutritionist to grasp the user's emotional state in real time and provide advice that takes the emotion into consideration. For example, if the user is feeling anxious, the nutritionist supervision unit makes a meal suggestion that will help the user maintain that emotion. In this way, advice that takes the user's emotional state into consideration can be provided.

[0066] The nutritionist supervision department can enable the generation AI to automatically translate into different languages, making it possible to accommodate international users. For example, the nutritionist supervision department provides a function that enables the generation AI to automatically translate nutritionist supervision and advice into different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The nutritionist supervision department can also enable the generation AI to automatically translate nutritionist advice into different languages, making it possible to accommodate international users. For example, it can provide advice in the language selected by the user. The nutritionist supervision department can also build a system that enables the generation AI to automatically translate nutritionist supervision and advice into different languages. For example, it can enable users to receive advice in their native language. This makes it possible to accommodate international users.

[0067] The nutritionist supervision department allows the generation AI to provide comprehensive advice that incorporates the opinions of other experts. For example, when a nutritionist supervises, the nutritionist supervision department allows the generation AI to provide comprehensive advice that incorporates the opinions of other experts (e.g., doctors and fitness trainers). For example, health management advice is provided based on the opinion of a doctor. In addition, the nutritionist supervision department allows the generation AI to incorporate the opinions of other experts and the nutritionist provides comprehensive advice. For example, it suggests a balance between exercise and diet based on the opinion of a fitness trainer. In addition, the nutritionist supervision department builds a system where, when a nutritionist supervises, the generation AI automatically provides advice that incorporates the opinions of other experts. For example, it provides advice that integrates the opinions of doctors and fitness trainers. This allows for comprehensive advice that incorporates the opinions of other experts.

[0068] The nutritionist supervision unit uses the emotion estimation function to enable a nutritionist to monitor a user's emotional state in real time and continuously provide advice according to the emotion. For example, the nutritionist supervision unit uses the emotion estimation function to enable a nutritionist to monitor a user's emotional state in real time and continuously provide advice according to the emotion. For example, if the user is feeling stressed, the nutritionist supervision unit makes meal suggestions that take the emotion into consideration. The nutritionist supervision unit also monitors a user's emotional state in real time and continuously provides advice according to the emotion. For example, if the user is feeling positive, the nutritionist supervision unit makes meal suggestions to maintain that emotion. The nutritionist supervision unit also uses the emotion estimation function to build a system in which a nutritionist monitors a user's emotional state in real time and continuously provides advice according to the emotion. For example, if the user is feeling anxious, the nutritionist supervision unit makes meal suggestions to alleviate the emotion. In this way, the user's emotional state can be monitored in real time and continuously provided with advice.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] When recording the user's meal contents, the meal recording unit can automatically obtain the origin and production method of ingredients and reflect this in the record. For example, if a user inputs "I ate tomato salad," the generation AI automatically obtains the origin of the tomatoes and whether they were organically grown, and reflects this in the record. The meal recording unit can also automatically obtain the nutritional value and allergen information of ingredients and reflect this in the record. For example, if a user inputs "I drank milk," the generation AI automatically obtains the nutritional value and allergen information of the milk and reflects this in the record. This allows users to easily record detailed information about ingredients.

[0071] The meal report generator can evaluate the environmental impact based on the user's meal content. For example, the generation AI analyzes the meal content, calculates the carbon footprint of the ingredients, and reflects this in the report. The meal report generator can also evaluate the environmental impact of the meal content and recommend sustainable meal choices. For example, if a user inputs "I ate steak," the generation AI evaluates the environmental impact of that meal and recommends, "Next time, try choosing plant-based ingredients." The meal report generator can also evaluate the environmental impact based on the user's meal content and suggest specific areas for improvement. For example, it could advise, "Today's meal had a high environmental impact, so next time, try choosing locally produced ingredients." This allows the user to make environmentally conscious meal choices.

[0072] When recording the user's meal contents, the meal recording unit can automatically obtain the storage and cooking methods of ingredients and reflect them in the record. For example, if a user inputs, "I ate grilled chicken," the generation AI automatically obtains the storage and cooking methods for the chicken and reflects them in the record. The meal recording unit can also automatically obtain the storage period and cooking time of ingredients and reflect them in the record. For example, if a user inputs, "I used frozen vegetables," the generation AI automatically obtains the storage period and cooking time and reflects them in the record. This allows users to easily record how they store and cook ingredients.

[0073] The meal report generation unit can analyze the user's emotional state and provide feedback that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions of the user when entering meal details, and provide feedback that elicits positive emotions. For example, if the user enters, "I ate a lot of vegetables today," the feedback can be "Great choice!". The meal report generation unit can also analyze the user's emotions and provide feedback that elicits positive emotions. For example, if the user enters, "I ate a little too much today," the feedback can be "Try adding a salad next time to balance it out." The meal report generation unit can also use the emotion estimation function to analyze the user's emotions and provide feedback that elicits positive emotions. For example, if the user enters, "I ate a healthy meal today," the feedback can be "Great! Keep it up!". This makes it possible to provide positive feedback that corresponds to the user's emotional state.

[0074] When recording a user's meal, the meal recording unit can automatically obtain allergen information for ingredients and reflect it in the record. For example, if a user inputs "I ate peanut butter," the generation AI automatically obtains peanut allergen information and reflects it in the record. The meal recording unit can also evaluate allergy risk based on the allergen information for ingredients and provide a warning to the user. For example, if a user inputs "I ate shrimp," the generation AI obtains shrimp allergen information, evaluates allergy risk, and provides a warning. The meal recording unit can also automatically obtain allergen information for ingredients based on the user's allergy history and reflect it in the record. This allows users to easily record allergen information and manage allergy risk.

[0075] The meal report generation unit can analyze the user's emotional state in real time and provide advice to reduce negative emotions. For example, using the emotion estimation function, the emotion when the user inputs the meal details can be analyzed in real time and advice to reduce negative emotions can be provided. For example, if the user inputs "I ate too much today," the advice provided is "Try adding a salad next time to balance it out." The meal report generation unit can also analyze the user's emotion in real time and provide advice to reduce negative emotions. For example, if the user inputs "I ate too much today because of stress," the advice provided is "Try making a healthy choice next time." The meal report generation unit can also analyze the user's emotion in real time and provide advice to reduce negative emotions using the emotion estimation function. For example, if the user inputs "I ate too much today because of stress," the advice provided is "Try taking a walk to relax." This allows advice to be provided to reduce the user's negative emotions.

[0076] When recording the user's meal contents, the meal recording unit can automatically obtain the nutritional value of ingredients and reflect it in the record. For example, if the user inputs "I ate broccoli," the generation AI automatically obtains the nutritional value of broccoli and reflects it in the record. The meal recording unit can also evaluate the user's nutritional balance based on the nutritional value of ingredients and suggest specific areas for improvement. For example, if the user inputs "I ate cheese," the generation AI obtains the nutritional value of cheese, evaluates the calcium intake, and suggests areas for improvement. The meal recording unit can also automatically obtain nutritional value based on the user's meal contents and reflect it in the record. This allows users to easily record the nutritional value of ingredients and manage their nutritional balance.

[0077] The meal report generation unit reflects the user's emotional state and can also evaluate the emotional health state. For example, the emotion estimation function is used to reflect the user's emotional state in the meal report. For example, if the user is feeling stressed, the emotional state is recorded in the report and dietary advice for stress reduction is provided. The meal report generation unit also reflects the user's emotional state in the meal report and evaluates the emotional health state. For example, if the user is feeling positive, the emotional state is recorded in the report and positive dietary choices are recommended. The meal report generation unit also uses the emotion estimation function to reflect the user's emotional state in the meal report and evaluate the emotional health state. For example, if the user is feeling anxious, the emotional state is recorded in the report and dietary advice for anxiety reduction is provided. In this way, the user's emotional state can be reflected and the emotional health state can be evaluated.

[0078] When recording the contents of a user's meal, the meal recording unit can automatically obtain ingredient price information and reflect it in the record. For example, if a user inputs "I ate salmon," the generation AI automatically obtains salmon price information and reflects it in the record. The meal recording unit can also provide advice for managing the user's food expenses based on the ingredient price information. For example, if a user inputs "I ate steak," the generation AI obtains steak price information and provides advice for managing food expenses. The meal recording unit can also automatically obtain ingredient price information based on the user's meal contents and reflect it in the record. This allows users to easily record ingredient price information and manage their food expenses.

[0079] The nutritionist supervision unit uses the emotion estimation function to enable the nutritionist to grasp the user's emotional state and provide advice that takes the emotion into consideration. For example, if the user is feeling stressed, the nutritionist will suggest a meal that will help them relax. The nutritionist supervision unit also uses the emotion estimation function to enable the nutritionist to grasp the user's emotional state in real time and provide advice that takes the emotion into consideration. For example, if the user is feeling anxious, the nutritionist will suggest a meal that will help them relieve that emotion. This makes it possible to provide advice that takes the user's emotional state into consideration.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The meal recording unit uses the generation AI to record meals. For example, when a user inputs meal details by voice, the generation AI analyzes the details and reflects them in the meal record. Users can also record by entering text or taking photos. For example, if a user inputs by voice, "I had toast and coffee for breakfast," the generation AI analyzes the details and reflects them in the meal record. Step 2: The meal report generator generates a meal report based on the meal details recorded by the meal recording unit. For example, the generation AI analyzes the meal details for the day and creates a detailed meal report including calorie intake, nutritional balance, and areas for improvement. Based on the meal details entered by the user, the generation AI evaluates nutrient deficiencies and balance and generates a report. For example, it may include specific indications such as "Today's meal is lacking in vitamin C." Step 3: In the nutritionist supervision department, a nutritionist supervises the dietary report generated by the dietary report generation department and provides improvement advice to the user. For example, the nutritionist checks the report generated by the generation AI and makes corrections or additional advice as necessary. For example, advice such as "Try including fruits rich in vitamin C in tomorrow's meal" is provided.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A meal recording unit that records meals using a generation AI; a meal report generation unit that generates a meal report based on the meal details recorded by the meal recording unit; a dietitian supervision unit in which a dietitian supervises the diet report generated by the diet report generation unit and provides improvement advice to the user. A system characterized by:

2. The meal recording unit Analyzing the user's voice input and recording the meal contents.

2. The system of claim 1.

3. The meal report generation unit The meal contents are automatically completed by referring to the user's past meal history.

2. The system of claim 1.

4. The meal report generation unit Referencing the user's past health data to provide a personalized diet report 2. The system of claim 1.

5. The nutritionist supervision department Nutritionists can add comments and advice in real time to the dietary report created by the AI.

2. The system of claim 1.

6. The meal report generation unit Analyzing the emotional state of the user and providing feedback to elicit positive emotions 2. The system of claim 1.

7. The meal report generation unit Reflecting the user's emotional state and assessing their emotional well-being 2. The system of claim 1.

8. The nutritionist supervision department Understanding the emotional state of the user and providing advice that takes emotions into consideration 2. The system of claim 1.

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