System

Abstract

An object of a system according to an embodiment is to recommend an optimal meal based on a health condition of a user.SOLUTION: A system according to an embodiment includes an input unit, a cooperation unit, a reception unit, a generation unit, and a provision unit. The input unit inputs a record of daily meals. The cooperation unit grasps the health condition of the user in cooperation with the healthcare system application data. The reception unit receives an input of a meal or a food that the user wants to eat on that day. The generation unit analyzes the information input by the reception unit and recommends a meal based on the health management of the user. The providing unit provides the recommendation generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend meals based on a user's health status, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal meals based on the user's health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a linking unit, a receiving unit, a generating unit, and a providing unit. The input unit inputs daily meal records. The linking unit links with healthcare app data to understand the user's health condition. The receiving unit inputs the meals or ingredients the user wants to eat that day. The generating unit analyzes the information input by the receiving unit and recommends meals based on the user's health management. The providing unit provides the recommendations generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend optimal meals based on the health condition of 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 health management system according to an embodiment of the present invention is a system that recommends meals tailored to the user's health management by linking with daily food records and healthcare app data and inputting the meals and ingredients the user wants to eat that day into a generation AI. The health management system allows the user to input their daily food records, link with healthcare app data to understand the user's health status and goals, and input the meals and ingredients the user wants to eat that day into the generation AI, which analyzes the information and recommends meals tailored to the user's health management. For example, in a health management system, the user inputs their daily food records. For example, making the input easier by allowing users to select the meals. Next, the health management system links with healthcare app data to understand the user's health status and goals. For example, it acquires data such as weight, blood pressure, and blood sugar levels. Next, the health management system inputs the meals and ingredients the user wants to eat that day into the generation AI. The generation AI analyzes the input information and recommends meals tailored to the user's health management. For example, it suggests low-calorie meals for a user on a diet and high-protein meals for a user aiming to build muscle. This allows the health management system to easily select meals that match the user's health condition and goals. This allows the health management system to recommend meals that match the user's health condition and goals. For example, the user can easily select meals that match the user's health condition and goals.

[0029] A health management system according to an embodiment includes an input unit, a linking unit, a receiving unit, a generating unit, and a providing unit. The input unit allows a user to input a daily meal record. The user's daily meal record includes, but is not limited to, the type, amount, and duration of each meal. The input unit simplifies input by, for example, allowing for selection. The input unit also allows the user to record meals by voice input or image input. For example, the user can take a photo of the meal, and the contents of the meal can be automatically recognized using image analysis technology. The linking unit links with healthcare app data to understand the user's health condition. The healthcare app data includes, for example, data such as weight, blood pressure, and blood sugar level, but is not limited to, the example. The linking unit automatically acquires data from, for example, a healthcare app, to understand the user's health condition. The linking unit also allows the user to manually input health data. The receiving unit allows the user to input the meals and ingredients the user wants to eat that day. The meals and ingredients the user wants to eat that day include, for example, the name of a dish and the type of ingredient, but are not limited to, the example. The reception unit allows the user to input meals and ingredients by text or voice, for example. The generation unit uses a generation AI to analyze the information input by the reception unit and recommend meals based on the user's health management. The generation unit, for example, suggests low-calorie meals to a user on a diet and high-protein meals to a user aiming to build muscle. The generation unit can also suggest ingredients to avoid based on the user's health condition and goals. The provision unit provides the recommendation generated by the generation unit to the user. The provision unit provides the recommendation by, for example, a text message or a voice message. The provision unit can also provide the recommendation using a notification function of an application. As a result, the health management system according to the embodiment can recommend meals that match the user's health condition and goals.

[0030] The input unit allows the user to input a daily meal record using a selection system. Selection systems include, but are not limited to, pull-down menus and check boxes, for example. The input unit allows the user to select the type of meal from a pull-down menu, for example. The input unit also allows the user to select the amount of food eaten using check boxes. Furthermore, the input unit also allows the user to select meal times in a calendar format. This allows the user to easily input a meal record.

[0031] The linking unit can link with healthcare app data to acquire weight, blood pressure, and blood glucose level data. Healthcare app data includes, but is not limited to, data such as weight, blood pressure, and blood glucose level. For example, the linking unit automatically acquires weight data from the healthcare app. The linking unit can also automatically acquire blood pressure data from the healthcare app. Furthermore, the linking unit can automatically acquire blood glucose level data from the healthcare app. This allows the user's health condition to be understood in detail.

[0032] The reception unit allows the user to input the meals and ingredients that the user wants to eat that day. The meals and ingredients that the user wants to eat that day include, for example, the name of a dish, the type of ingredients, etc., but are not limited to these examples. The reception unit, for example, allows the user to input the meals and ingredients by text. The reception unit also allows the user to input the meals and ingredients by voice. Furthermore, the reception unit also allows the user to input the meals and ingredients by image. This allows the user to input meals and ingredients based on their wishes.

[0033] The generation unit analyzes the input information and can suggest low-calorie meals to a user on a diet and high-protein meals to a user aiming to build muscle. Low-calorie meals include, but are not limited to, salads, soups, grilled chicken, etc. For example, the generation unit can suggest salads to a user on a diet. The generation unit can also suggest soups to a user on a diet. The generation unit can also suggest grilled chicken to a user on a diet. High-protein meals include, but are not limited to, steaks, fish, tofu, etc. For example, the generation unit can suggest steaks to a user aiming to build muscle. The generation unit can also suggest fish to a user aiming to build muscle. The generation unit can also suggest tofu to a user aiming to build muscle. This makes it possible to suggest meals tailored to the user's health management.

[0034] The providing unit can provide the user with the recommendations generated by the generating unit. Recommendations include, but are not limited to, for example, types of meals and nutritional balance. The providing unit can provide the recommendations by, for example, text messages. The providing unit can also provide the recommendations by voice messages. Furthermore, the providing unit can also provide the recommendations using a notification function of an application. This allows the user to receive appropriate meal recommendations.

[0035] The input unit can analyze the user's past meal records and select an appropriate input method. Suitable input methods include, but are not limited to, voice input, text input, and image input. For example, the input unit can automatically display as candidates meal items that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest meal items to be input during a specific time period based on the user's past meal records. This makes it possible to provide the user with the optimal input method.

[0036] The input unit can filter the food record based on the user's current health condition and goals when inputting the food record. Examples of filtering include, but are not limited to, health condition indicators, goal types, and the like. For example, if the user is on a diet, the input unit can prioritize displaying low-calorie food items. Also, if the user is aiming to build muscle, the input unit can prioritize displaying high-protein food items. Furthermore, the input unit can filter ingredients that should be avoided according to the user's health condition. This allows the user to input a food record that is suited to the user's health condition and goals.

[0037] When inputting a meal record, the input unit can select an appropriate input means according to the user's input method. Suitable input means include, but are not limited to, voice input, text input, and image input. For example, the input unit provides a voice recognition function when the user desires voice input. Furthermore, the input unit can also provide an interface that allows for easy input when the user desires text input. Furthermore, the input unit can also provide a function that takes a photo of the meal and automatically analyzes it when the user desires image input. This makes it possible to provide the optimal input means according to the user's input method.

[0038] When inputting a meal record, the input unit can prioritize inputting relevant meal records based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the input unit can prioritize inputting local specialties of that area. Furthermore, when the user is traveling, the input unit can also prioritize inputting meal records from the travel destination. Furthermore, when the user is at home, the input unit can also prioritize inputting everyday meal records. This allows input of a meal record based on the user's geographical location information.

[0039] When inputting a meal record, the input unit can analyze the user's social media activity and input related meal records. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The input unit, for example, automatically inputs meals shared by the user on social media. The input unit can also analyze the content of posts by the user on social media and input related meal records. Furthermore, the input unit can also input related meal records by referring to the activities of the user's friends on social media. This makes it possible to input a meal record based on the user's social media activity.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting a meal record. The feedback includes, but is not limited to, for example, the user's ratings and comments. The input unit can, for example, preferentially provide the input method that the user has previously preferred. The input unit can also improve the input interface based on the user's past feedback. Furthermore, the input unit can also simplify the input procedure by referring to the user's past feedback. This makes it possible to provide an input method based on the user's past feedback.

[0041] When acquiring healthcare data, the linking unit can analyze the user's past health data and select the optimal acquisition method. Examples of the optimal acquisition method include, but are not limited to, the type of data and the frequency of acquisition. For example, the linking unit can automatically display health data that the user frequently acquired in the past as candidates. The linking unit can also preferentially suggest acquisition methods (voice, text, etc.) that the user has used in the past. Furthermore, the linking unit can predict and suggest data to be acquired in a specific time period based on the user's past health data. This allows healthcare data to be acquired in the optimal way for the user.

[0042] When acquiring healthcare data, the linking unit can select data based on the user's current health condition and goals. Examples of filtering include, but are not limited to, health condition indicators and goal types. For example, if the user is on a diet, the linking unit can prioritize acquiring data on weight and calorie consumption. Also, if the user is aiming to build muscle, the linking unit can prioritize acquiring data on muscle mass and protein intake. Furthermore, the linking unit can filter data that should be avoided depending on the user's health condition. This makes it possible to acquire healthcare data that is tailored to the user's health condition and goals.

[0043] When acquiring healthcare data, the linking unit can select the optimal acquisition means depending on the user's input method. Optimal acquisition means include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the linking unit can provide a voice recognition function. Furthermore, if the user desires text input, the linking unit can also provide an interface that allows for easy input. Furthermore, if the user desires image input, the linking unit can also provide a function to take a photo of the health data and automatically analyze it. This makes it possible to provide the optimal acquisition means depending on the user's input method.

[0044] When acquiring healthcare data, the linking unit can prioritize acquiring relevant data based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in a specific area, the linking unit can prioritize acquiring health data for that area. Furthermore, when the user is traveling, the linking unit can prioritize acquiring health data for the travel destination. Furthermore, when the user is at home, the linking unit can prioritize acquiring everyday health data. This allows healthcare data to be acquired based on the user's geographical location information.

[0045] When acquiring healthcare data, the linking unit can analyze the user's social media activity and acquire related data. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the linking unit automatically acquires health data shared by the user on social media. The linking unit can also analyze the content of the user's posts on social media to acquire related health data. Furthermore, the linking unit can also acquire related health data by referring to the activities of the user's friends on social media. This makes it possible to acquire healthcare data based on the user's social media activity.

[0046] The linking unit can customize the acquisition method by reflecting the user's past feedback when acquiring healthcare data. Examples of the feedback include, but are not limited to, user ratings and comments. For example, the linking unit can provide the acquisition method that the user has previously preferred preferentially. The linking unit can also improve the acquisition interface based on the user's past feedback. Furthermore, the linking unit can simplify the acquisition procedure by referring to the user's past feedback. This makes it possible to provide an acquisition method based on the user's past feedback.

[0047] The reception unit can analyze the user's past input history of meals and ingredients and select an appropriate input method. Suitable input methods include, but are not limited to, voice input, text input, and image input. For example, the reception unit can automatically display meals and ingredients that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest meals and ingredients that will be input during a specific time period based on the user's past input history of meals and ingredients. This makes it possible to provide the user with the optimal input method.

[0048] When inputting meals and ingredients, the reception unit can filter the meals and ingredients based on the user's current health condition and goals. Examples of filtering include, but are not limited to, health condition indicators and goal types. For example, if the user is on a diet, the reception unit can prioritize displaying low-calorie meals and ingredients. Also, if the user is aiming to build muscle, the reception unit can prioritize displaying high-protein meals and ingredients. Furthermore, the reception unit can filter out ingredients that should be avoided based on the user's health condition. This allows the user to input meals and ingredients that are suited to their health condition and goals.

[0049] When inputting food or ingredients, the reception unit can select an appropriate input means according to the user's input method. Suitable input means include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the reception unit provides a voice recognition function. Furthermore, if the user desires text input, the reception unit can also provide an interface that allows for easy input. Furthermore, if the user desires image input, the reception unit can also provide a function that takes a photo of the food or ingredients and automatically analyzes it. This makes it possible to provide the optimal input means according to the user's input method.

[0050] When inputting meals and ingredients, the reception unit can prioritize inputting related meals and ingredients based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific area, the reception unit can prioritize inputting local specialties of that area. Furthermore, if the user is traveling, the reception unit can also prioritize inputting meals and ingredients from the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting everyday meals and ingredients. This allows inputting meals and ingredients based on the user's geographical location information.

[0051] When inputting meals and ingredients, the reception unit can analyze the user's social media activity and input related meals and ingredients. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The reception unit, for example, automatically inputs meals and ingredients shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related meals and ingredients. Furthermore, the reception unit can input related meals and ingredients by referring to the activities of the user's friends on social media. This makes it possible to input meals and ingredients based on the user's social media activity.

[0052] The reception unit can adjust the input method by reflecting the user's past feedback when inputting meals and ingredients. Feedback includes, but is not limited to, user ratings and comments, for example. The reception unit can, for example, preferentially provide input methods that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. Furthermore, the reception unit can also simplify the input procedure by referring to the user's past feedback. This makes it possible to provide an input method based on the user's past feedback.

[0053] When generating recommendations, the generation unit can set the level of detail of the recommendations based on the user's health condition and goals. The level of detail includes, for example, the granularity of information, display items, and the like, but is not limited to these examples. For example, if the user is on a diet, the generation unit can recommend low-calorie meals in detail. Also, if the user is aiming to build muscle, the generation unit can recommend high-protein meals in detail. Furthermore, the generation unit can recommend ingredients to avoid in detail depending on the user's health condition. This makes it possible to provide detailed recommendations according to the user's health condition and goals.

[0054] When generating recommendations, the generation unit can apply different recommendation algorithms depending on the user's dietary history. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. For example, the generation unit can apply an algorithm that recommends similar meals based on meals that the user has previously preferred. The generation unit can also apply an algorithm that does not recommend ingredients that the user should avoid based on ingredients that the user has previously avoided. Furthermore, the generation unit can analyze the user's dietary history and apply an algorithm that recommends balanced meals. This makes it possible to provide optimal recommendations based on the user's dietary history.

[0055] When generating recommendations, the generation unit can improve the accuracy of the recommendations by referring to the user's past recommendation results. Recommendation accuracy includes, but is not limited to, for example, evaluation indexes and feedback reflection methods. For example, the generation unit generates similar recommendations based on recommendations that the user has accepted in the past. The generation unit can also not generate recommendations that should be avoided based on recommendations that the user has rejected in the past. Furthermore, the generation unit can analyze the user's past recommendation results and generate optimal recommendations. This makes it possible to provide highly accurate recommendations based on the user's past recommendation results.

[0056] When generating recommendations, the generator can prioritize the recommendations based on the timing of submission of the user's health data. Priorities include, but are not limited to, the importance of the health data and the timing of submission, for example. For example, the generator generates recommendations tailored to the user's latest health status based on health data recently submitted by the user. The generator can also generate recommendations tailored to the user's long-term health goals based on health data previously submitted by the user. Furthermore, the generator can provide recommendations at appropriate times based on the timing of submission of the user's health data. This allows recommendations to be provided in a priority order based on the timing of submission of the user's health data.

[0057] When generating recommendations, the generation unit can adjust the order of recommendations based on the relevance of the user's meals. Relevance includes, but is not limited to, for example, the type of meal and nutrient balance. For example, the generation unit provides recommendations in an optimal order based on the relevance of meals the user has eaten in the past. The generation unit can also analyze the user's meal history and provide recommendations in a balanced order. Furthermore, the generation unit can provide recommendations in an order of high relevance according to the user's health goals. This makes it possible to provide recommendations in an optimal order based on the relevance of the user's meals.

[0058] When generating a recommendation, the generation unit can set the use of technical terms for the recommendation according to the user's level of expertise. The level of expertise includes, but is not limited to, the user's occupation, educational background, etc. For example, if the user has technical expertise, the generation unit can provide a recommendation that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can provide a recommendation that explains things in simple terms. Furthermore, the generation unit can provide a recommendation that uses appropriate terms according to the user's level of expertise. This makes it possible to provide a recommendation using appropriate terms according to the user's level of expertise.

[0059] When providing a recommendation, the providing unit can select an appropriate providing method by referring to the user's past recommendation history. Optimal providing methods include, but are not limited to, notification methods and display formats, for example. The providing unit, for example, selects a similar providing method based on recommendations that the user has accepted in the past. The providing unit can also select a providing method to be avoided based on recommendations that the user has rejected in the past. Furthermore, the providing unit can analyze the user's past recommendation history and select an optimal providing method. This makes it possible to provide an optimal providing method based on the user's past recommendation history.

[0060] When providing recommendations, the providing unit can customize the provided content based on the user's current health condition and goals. Customization includes, but is not limited to, the user's health condition, goals, etc. For example, if the user is on a diet, the providing unit can provide low-calorie meals. Also, if the user is aiming to build muscle, the providing unit can provide high-protein meals. Furthermore, the providing unit can not provide ingredients that should be avoided depending on the user's health condition. This makes it possible to provide customized recommendations according to the user's health condition and goals.

[0061] The providing unit can improve the providing method by reflecting user feedback when providing recommendations. The feedback includes, but is not limited to, user ratings, comments, and the like. For example, the providing unit can provide a providing method that the user has previously preferred, with priority. The providing unit can also improve the providing interface based on the user's past feedback. Furthermore, the providing unit can also simplify the providing procedure by referring to the user's past feedback. This makes it possible to provide an improved providing method based on the user's feedback.

[0062] When providing recommendations, the providing unit can select an appropriate providing method based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific area, the providing unit can provide recommendations including local specialties. Furthermore, if the user is traveling, the providing unit can also provide recommendations including meals at the travel destination. Furthermore, if the user is at home, the providing unit can also provide recommendations including everyday meals. This makes it possible to provide an optimal providing method based on the user's geographical location information.

[0063] When providing recommendations, the providing unit can analyze the user's social media activity to suggest content to be offered. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. For example, the providing unit can provide recommendations based on meals shared by the user on social media. The providing unit can also analyze the content of posts by the user on social media to recommend related meals. Furthermore, the providing unit can also recommend related meals based on the activities of the user's friends on social media. This makes it possible to suggest content to be offered based on the user's social media activity.

[0064] When providing recommendations, the providing unit can adjust the providing method by reflecting the user's past feedback. The feedback includes, but is not limited to, for example, the user's ratings and comments. For example, the providing unit can preferentially provide a providing method that the user has previously preferred. The providing unit can also improve the providing interface based on the user's past feedback. Furthermore, the providing unit can also simplify the providing procedure by referring to the user's past feedback. This makes it possible to provide a customized providing method based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The health management system can also acquire the user's exercise data and reflect it in meal recommendations. For example, if the user runs, the calorie intake of the meal can be adjusted taking into account the calories burned. If the user does strength training, meals containing a lot of protein, which is necessary for muscle repair, can be suggested. Furthermore, if the user does yoga, meals containing ingredients with a relaxing effect can be suggested. This allows for more accurate meal recommendations based on the user's exercise data.

[0067] The health management system can also acquire a user's sleep data and reflect it in meal recommendations. For example, if a user is not getting enough sleep, it can suggest meals that are suitable for replenishing energy. Also, if a user has had deep sleep, it can suggest light meals. Furthermore, if a user has irregular sleep, it can suggest meals that are easy to digest. This makes it possible to provide meal recommendations based on the user's sleep data.

[0068] The health management system can also acquire a user's allergy data and reflect it in meal recommendations. For example, if a user is allergic to a specific ingredient, it can suggest meals that avoid that ingredient. Also, if a user has multiple allergies, it can suggest meals that take these into consideration. Furthermore, if a user is at high risk of allergies, it can suggest meals that include ingredients that are less likely to cause allergic reactions. This makes it possible to provide safe meal recommendations based on the user's allergy data.

[0069] The health management system can also acquire data on a user's food preferences and reflect this in meal recommendations. For example, if a user likes a particular dish, meals containing that dish can be suggested. If a user likes a particular ingredient, meals containing large amounts of that ingredient can be suggested. Furthermore, if a user likes a particular cooking method, meals using that cooking method can be suggested. This makes it possible to provide highly satisfying meal recommendations based on the user's food preference data.

[0070] The health management system can also analyze the user's meal recommendation history and reflect it in the next recommendation. For example, it can suggest similar meals based on recommendations the user has accepted in the past. It can also avoid suggesting meals that the user should avoid based on recommendations the user has rejected in the past. It can also analyze the user's meal recommendation history and suggest balanced meals. This allows it to provide optimal meal recommendations based on the user's meal recommendation history.

[0071] The health management system can also provide meal recommendations that include regional ingredients and dishes based on the user's geographical location information. For example, if the user is in a specific region, meals that include local specialties can be suggested. If the user is traveling, meals that are suitable for the user's travel destination can also be suggested. Furthermore, if the user is at home, regular meals can also be suggested. This allows for meal recommendations based on the user's geographical location information.

[0072] The health management system can also analyze a user's social media activity and provide related meal recommendations. For example, recommendations can be provided based on meals shared by the user on social media. It can also analyze the content of the user's social media posts to suggest related meals. It can also suggest related meals based on the activities of the user's friends on social media. This makes it possible to provide meal recommendations based on the user's social media activity.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The input unit allows the user to input a record of daily meals. The user's daily meal record may include, but is not limited to, the type of meal, the amount, and the time of the meal. The input unit can simplify input by, for example, making it optional. The input unit can also allow the user to record meals by voice input or image input. For example, the user can take a photo of the meal, and the contents of the meal can be automatically recognized using image analysis technology. Step 2: The linking unit links with healthcare app data to understand the user's health condition. Healthcare app data includes, but is not limited to, data such as weight, blood pressure, and blood sugar level. The linking unit, for example, automatically obtains data from the healthcare app to understand the user's health condition. The linking unit can also allow the user to manually input health data. Step 3: The reception unit inputs the meals and ingredients that the user wants to eat that day. The meals and ingredients that the user wants to eat that day include, but are not limited to, the name of the dish and the type of ingredients. The reception unit allows the user to input the meals and ingredients by text or voice, for example. Step 4: The generation unit uses the generation AI to analyze the information entered by the reception unit and recommend meals based on the user's health management. For example, the generation unit may suggest low-calorie meals to a user on a diet, or high-protein meals to a user aiming to build muscle. The generation unit can also suggest ingredients to avoid based on the user's health condition and goals. Step 5: The providing unit provides the recommendation generated by the generating unit to the user. For example, the providing unit provides the recommendation by a text message or a voice message. The providing unit can also provide the recommendation using a notification function of the application.

[0075] (Example 2) A health management system according to an embodiment of the present invention is a system that recommends meals tailored to the user's health management by linking with daily food records and healthcare app data and inputting the meals and ingredients the user wants to eat that day into a generation AI. The health management system allows the user to input their daily food records, link with healthcare app data to understand the user's health status and goals, and input the meals and ingredients the user wants to eat that day into the generation AI, which analyzes the information and recommends meals tailored to the user's health management. For example, in a health management system, the user inputs their daily food records. For example, making the input easier by allowing users to select the meals. Next, the health management system links with healthcare app data to understand the user's health status and goals. For example, it acquires data such as weight, blood pressure, and blood sugar levels. Next, the health management system inputs the meals and ingredients the user wants to eat that day into the generation AI. The generation AI analyzes the input information and recommends meals tailored to the user's health management. For example, it suggests low-calorie meals for a user on a diet and high-protein meals for a user aiming to build muscle. This allows the health management system to easily select meals that match the user's health condition and goals. This allows the health management system to recommend meals that match the user's health condition and goals. For example, the user can easily select meals that match the user's health condition and goals.

[0076] A health management system according to an embodiment includes an input unit, a linking unit, a receiving unit, a generating unit, and a providing unit. The input unit allows a user to input a daily meal record. The user's daily meal record includes, but is not limited to, the type, amount, and duration of each meal. The input unit simplifies input by, for example, allowing for selection. The input unit also allows the user to record meals by voice input or image input. For example, the user can take a photo of the meal, and the contents of the meal can be automatically recognized using image analysis technology. The linking unit links with healthcare app data to understand the user's health condition. The healthcare app data includes, for example, data such as weight, blood pressure, and blood sugar level, but is not limited to, the example. The linking unit automatically acquires data from, for example, a healthcare app, to understand the user's health condition. The linking unit also allows the user to manually input health data. The receiving unit allows the user to input the meals and ingredients the user wants to eat that day. The meals and ingredients the user wants to eat that day include, for example, the name of a dish and the type of ingredient, but are not limited to, the example. The reception unit allows the user to input meals and ingredients by text or voice, for example. The generation unit uses a generation AI to analyze the information input by the reception unit and recommend meals based on the user's health management. The generation unit, for example, suggests low-calorie meals to a user on a diet and high-protein meals to a user aiming to build muscle. The generation unit can also suggest ingredients to avoid based on the user's health condition and goals. The provision unit provides the recommendation generated by the generation unit to the user. The provision unit provides the recommendation by, for example, a text message or a voice message. The provision unit can also provide the recommendation using a notification function of an application. As a result, the health management system according to the embodiment can recommend meals that match the user's health condition and goals.

[0077] The input unit allows the user to input a daily meal record using a selection system. Selection systems include, but are not limited to, pull-down menus and check boxes, for example. The input unit allows the user to select the type of meal from a pull-down menu, for example. The input unit also allows the user to select the amount of food eaten using check boxes. Furthermore, the input unit also allows the user to select meal times in a calendar format. This allows the user to easily input a meal record.

[0078] The linking unit can link with healthcare app data to acquire weight, blood pressure, and blood glucose level data. Healthcare app data includes, but is not limited to, data such as weight, blood pressure, and blood glucose level. For example, the linking unit automatically acquires weight data from the healthcare app. The linking unit can also automatically acquire blood pressure data from the healthcare app. Furthermore, the linking unit can automatically acquire blood glucose level data from the healthcare app. This allows the user's health condition to be understood in detail.

[0079] The reception unit allows the user to input the meals and ingredients that the user wants to eat that day. The meals and ingredients that the user wants to eat that day include, for example, the name of a dish, the type of ingredients, etc., but are not limited to these examples. The reception unit, for example, allows the user to input the meals and ingredients by text. The reception unit also allows the user to input the meals and ingredients by voice. Furthermore, the reception unit also allows the user to input the meals and ingredients by image. This allows the user to input meals and ingredients based on their wishes.

[0080] The generation unit analyzes the input information and can suggest low-calorie meals to a user on a diet and high-protein meals to a user aiming to build muscle. Low-calorie meals include, but are not limited to, salads, soups, grilled chicken, etc. For example, the generation unit can suggest salads to a user on a diet. The generation unit can also suggest soups to a user on a diet. The generation unit can also suggest grilled chicken to a user on a diet. High-protein meals include, but are not limited to, steaks, fish, tofu, etc. For example, the generation unit can suggest steaks to a user aiming to build muscle. The generation unit can also suggest fish to a user aiming to build muscle. The generation unit can also suggest tofu to a user aiming to build muscle. This makes it possible to suggest meals tailored to the user's health management.

[0081] The providing unit can provide the user with the recommendations generated by the generating unit. Recommendations include, but are not limited to, for example, types of meals and nutritional balance. The providing unit can provide the recommendations by, for example, text messages. The providing unit can also provide the recommendations by voice messages. Furthermore, the providing unit can also provide the recommendations using a notification function of an application. This allows the user to receive appropriate meal recommendations.

[0082] The input unit can estimate the user's emotions and adjust the timing of inputting the meal record based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is feeling stressed, the input unit can prompt the user to input the meal record at a time when the user is able to relax. Furthermore, if the user is busy, the input unit can adjust the input timing so that the user can input the meal record in a short time. Furthermore, if the user is relaxed, the input unit can prompt the user to input a detailed meal record. This allows the meal record to be input at an appropriate time according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The input unit can analyze the user's past meal records and select an appropriate input method. Suitable input methods include, but are not limited to, voice input, text input, and image input. For example, the input unit can automatically display as candidates meal items that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest meal items to be input during a specific time period based on the user's past meal records. This makes it possible to provide the user with the optimal input method.

[0084] The input unit can filter the food record based on the user's current health condition and goals when inputting the food record. Examples of filtering include, but are not limited to, health condition indicators, goal types, and the like. For example, if the user is on a diet, the input unit can prioritize displaying low-calorie food items. Also, if the user is aiming to build muscle, the input unit can prioritize displaying high-protein food items. Furthermore, the input unit can filter ingredients that should be avoided according to the user's health condition. This allows the user to input a food record that is suited to the user's health condition and goals.

[0085] When inputting a meal record, the input unit can select an appropriate input means according to the user's input method. Suitable input means include, but are not limited to, voice input, text input, and image input. For example, the input unit provides a voice recognition function when the user desires voice input. Furthermore, the input unit can also provide an interface that allows for easy input when the user desires text input. Furthermore, the input unit can also provide a function that takes a photo of the meal and automatically analyzes it when the user desires image input. This makes it possible to provide the optimal input means according to the user's input method.

[0086] The input unit can estimate the user's emotions and determine the priority of the food records to be input based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is feeling stressed, the input unit can prioritize input of meals that have a relaxing effect. Furthermore, if the user is tired, the input unit can also prioritize input of meals that are nutritious. Furthermore, if the user is feeling energetic, the input unit can also prioritize input of balanced meals. This allows food records to be input in order of priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When inputting a meal record, the input unit can prioritize inputting relevant meal records based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the input unit can prioritize inputting local specialties of that area. Furthermore, when the user is traveling, the input unit can also prioritize inputting meal records from the travel destination. Furthermore, when the user is at home, the input unit can also prioritize inputting everyday meal records. This allows input of a meal record based on the user's geographical location information.

[0088] When inputting a meal record, the input unit can analyze the user's social media activity and input related meal records. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The input unit, for example, automatically inputs meals shared by the user on social media. The input unit can also analyze the content of posts by the user on social media and input related meal records. Furthermore, the input unit can also input related meal records by referring to the activities of the user's friends on social media. This makes it possible to input a meal record based on the user's social media activity.

[0089] The input unit can customize the input method by reflecting the user's past feedback when inputting a meal record. The feedback includes, but is not limited to, for example, the user's ratings and comments. The input unit can, for example, preferentially provide the input method that the user has previously preferred. The input unit can also improve the input interface based on the user's past feedback. Furthermore, the input unit can also simplify the input procedure by referring to the user's past feedback. This makes it possible to provide an input method based on the user's past feedback.

[0090] The linking unit can estimate the user's emotions and adjust the timing of healthcare data acquisition based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, and the like. For example, if the user is feeling stressed, the linking unit can acquire healthcare data during a time when the user is able to relax. Furthermore, if the user is busy, the linking unit can also adjust the acquisition timing so that the data can be acquired in a short time. Furthermore, if the user is relaxed, the linking unit can also prompt the user to acquire detailed healthcare data. This allows healthcare data to be acquired at an appropriate time according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When acquiring healthcare data, the linking unit can analyze the user's past health data and select the optimal acquisition method. Examples of the optimal acquisition method include, but are not limited to, the type of data and the frequency of acquisition. For example, the linking unit can automatically display health data that the user frequently acquired in the past as candidates. The linking unit can also preferentially suggest acquisition methods (voice, text, etc.) that the user has used in the past. Furthermore, the linking unit can predict and suggest data to be acquired in a specific time period based on the user's past health data. This allows healthcare data to be acquired in the optimal way for the user.

[0092] When acquiring healthcare data, the linking unit can select data based on the user's current health condition and goals. Examples of filtering include, but are not limited to, health condition indicators and goal types. For example, if the user is on a diet, the linking unit can prioritize acquiring data on weight and calorie consumption. Also, if the user is aiming to build muscle, the linking unit can prioritize acquiring data on muscle mass and protein intake. Furthermore, the linking unit can filter data that should be avoided depending on the user's health condition. This makes it possible to acquire healthcare data that is tailored to the user's health condition and goals.

[0093] When acquiring healthcare data, the linking unit can select the optimal acquisition means depending on the user's input method. Optimal acquisition means include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the linking unit can provide a voice recognition function. Furthermore, if the user desires text input, the linking unit can also provide an interface that allows for easy input. Furthermore, if the user desires image input, the linking unit can also provide a function to take a photo of the health data and automatically analyze it. This makes it possible to provide the optimal acquisition means depending on the user's input method.

[0094] The linking unit can estimate the user's emotions and determine the priority of healthcare data to be acquired based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is feeling stressed, the linking unit can prioritize acquiring stress level data. Furthermore, if the user is tired, the linking unit can prioritize acquiring sleep data. Furthermore, if the user is in good health, the linking unit can prioritize acquiring balanced health data. This allows healthcare data to be acquired in order of priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] When acquiring healthcare data, the linking unit can prioritize acquiring relevant data based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, when the user is in a specific area, the linking unit can prioritize acquiring health data for that area. Furthermore, when the user is traveling, the linking unit can prioritize acquiring health data for the travel destination. Furthermore, when the user is at home, the linking unit can prioritize acquiring everyday health data. This allows healthcare data to be acquired based on the user's geographical location information.

[0096] When acquiring healthcare data, the linking unit can analyze the user's social media activity and acquire related data. Social media activity includes, but is not limited to, for example, the content of posts and the number of likes. For example, the linking unit automatically acquires health data shared by the user on social media. The linking unit can also analyze the content of the user's posts on social media to acquire related health data. Furthermore, the linking unit can also acquire related health data by referring to the activities of the user's friends on social media. This makes it possible to acquire healthcare data based on the user's social media activity.

[0097] The linking unit can customize the acquisition method by reflecting the user's past feedback when acquiring healthcare data. Examples of the feedback include, but are not limited to, user ratings and comments. For example, the linking unit can provide the acquisition method that the user has previously preferred preferentially. The linking unit can also improve the acquisition interface based on the user's past feedback. Furthermore, the linking unit can simplify the acquisition procedure by referring to the user's past feedback. This makes it possible to provide an acquisition method based on the user's past feedback.

[0098] The reception unit can estimate the user's emotions and adjust the timing of inputting meals and ingredients based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is feeling stressed, the reception unit can prompt the user to input meals and ingredients at a time when the user is able to relax. Furthermore, if the user is busy, the reception unit can adjust the input timing so that the input can be completed in a short time. Furthermore, if the user is relaxed, the reception unit can prompt the user to input detailed meals and ingredients. This allows the user to input meals and ingredients at an appropriate time according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The reception unit can analyze the user's past input history of meals and ingredients and select an appropriate input method. Suitable input methods include, but are not limited to, voice input, text input, and image input. For example, the reception unit can automatically display meals and ingredients that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest meals and ingredients that will be input during a specific time period based on the user's past input history of meals and ingredients. This makes it possible to provide the user with the optimal input method.

[0100] When inputting meals and ingredients, the reception unit can filter the meals and ingredients based on the user's current health condition and goals. Examples of filtering include, but are not limited to, health condition indicators and goal types. For example, if the user is on a diet, the reception unit can prioritize displaying low-calorie meals and ingredients. Also, if the user is aiming to build muscle, the reception unit can prioritize displaying high-protein meals and ingredients. Furthermore, the reception unit can filter out ingredients that should be avoided based on the user's health condition. This allows the user to input meals and ingredients that are suited to their health condition and goals.

[0101] When inputting food or ingredients, the reception unit can select an appropriate input means according to the user's input method. Suitable input means include, but are not limited to, voice input, text input, and image input. For example, if the user desires voice input, the reception unit provides a voice recognition function. Furthermore, if the user desires text input, the reception unit can also provide an interface that allows for easy input. Furthermore, if the user desires image input, the reception unit can also provide a function that takes a photo of the food or ingredients and automatically analyzes it. This makes it possible to provide the optimal input means according to the user's input method.

[0102] The reception unit can estimate the user's emotions and determine the priority of meals and ingredients to be input based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is feeling stressed, the reception unit can prioritize inputting meals and ingredients that have a relaxing effect. Furthermore, if the user is tired, the reception unit can also prioritize inputting meals and ingredients that are nutritious. Furthermore, if the user is feeling energetic, the reception unit can also prioritize inputting balanced meals and ingredients. This allows meals and ingredients to be input in order of priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] When inputting meals and ingredients, the reception unit can prioritize inputting related meals and ingredients based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific area, the reception unit can prioritize inputting local specialties of that area. Furthermore, if the user is traveling, the reception unit can also prioritize inputting meals and ingredients from the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting everyday meals and ingredients. This allows inputting meals and ingredients based on the user's geographical location information.

[0104] When inputting meals and ingredients, the reception unit can analyze the user's social media activity and input related meals and ingredients. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The reception unit, for example, automatically inputs meals and ingredients shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related meals and ingredients. Furthermore, the reception unit can input related meals and ingredients by referring to the activities of the user's friends on social media. This makes it possible to input meals and ingredients based on the user's social media activity.

[0105] The reception unit can adjust the input method by reflecting the user's past feedback when inputting meals and ingredients. Feedback includes, but is not limited to, user ratings and comments, for example. The reception unit can, for example, preferentially provide input methods that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. Furthermore, the reception unit can also simplify the input procedure by referring to the user's past feedback. This makes it possible to provide an input method based on the user's past feedback.

[0106] The generation unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, and the like. For example, if the user is relaxed, the generation unit can generate recommendations that proceed at a leisurely pace. If the user is in a hurry, the generation unit can also generate recommendations that are concise and to the point. Furthermore, if the user is excited, the generation unit can also generate recommendations that add visually stimulating effects. This allows recommendations to be provided in an appropriate expression method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] When generating recommendations, the generation unit can set the level of detail of the recommendations based on the user's health condition and goals. The level of detail includes, for example, the granularity of information, display items, and the like, but is not limited to these examples. For example, if the user is on a diet, the generation unit can recommend low-calorie meals in detail. Also, if the user is aiming to build muscle, the generation unit can recommend high-protein meals in detail. Furthermore, the generation unit can recommend ingredients to avoid in detail depending on the user's health condition. This makes it possible to provide detailed recommendations according to the user's health condition and goals.

[0108] When generating recommendations, the generation unit can apply different recommendation algorithms depending on the user's dietary history. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. For example, the generation unit can apply an algorithm that recommends similar meals based on meals that the user has previously preferred. The generation unit can also apply an algorithm that does not recommend ingredients that the user should avoid based on ingredients that the user has previously avoided. Furthermore, the generation unit can analyze the user's dietary history and apply an algorithm that recommends balanced meals. This makes it possible to provide optimal recommendations based on the user's dietary history.

[0109] When generating recommendations, the generation unit can improve the accuracy of the recommendations by referring to the user's past recommendation results. Recommendation accuracy includes, but is not limited to, for example, evaluation indexes and feedback reflection methods. For example, the generation unit generates similar recommendations based on recommendations that the user has accepted in the past. The generation unit can also not generate recommendations that should be avoided based on recommendations that the user has rejected in the past. Furthermore, the generation unit can analyze the user's past recommendation results and generate optimal recommendations. This makes it possible to provide highly accurate recommendations based on the user's past recommendation results.

[0110] The generation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point recommendation. If the user is relaxed, the generation unit can also generate a longer recommendation with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a recommendation with a visually stimulating effect. This allows recommendations to be provided with an appropriate length depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] When generating recommendations, the generator can prioritize the recommendations based on the timing of submission of the user's health data. Priorities include, but are not limited to, the importance of the health data and the timing of submission, for example. For example, the generator generates recommendations tailored to the user's latest health status based on health data recently submitted by the user. The generator can also generate recommendations tailored to the user's long-term health goals based on health data previously submitted by the user. Furthermore, the generator can provide recommendations at appropriate times based on the timing of submission of the user's health data. This allows recommendations to be provided in a priority order based on the timing of submission of the user's health data.

[0112] When generating recommendations, the generation unit can adjust the order of recommendations based on the relevance of the user's meals. Relevance includes, but is not limited to, for example, the type of meal and nutrient balance. For example, the generation unit provides recommendations in an optimal order based on the relevance of meals the user has eaten in the past. The generation unit can also analyze the user's meal history and provide recommendations in a balanced order. Furthermore, the generation unit can provide recommendations in an order of high relevance according to the user's health goals. This makes it possible to provide recommendations in an optimal order based on the relevance of the user's meals.

[0113] When generating a recommendation, the generation unit can set the use of technical terms for the recommendation according to the user's level of expertise. The level of expertise includes, but is not limited to, the user's occupation, educational background, etc. For example, if the user has technical expertise, the generation unit can provide a recommendation that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can provide a recommendation that explains things in simple terms. Furthermore, the generation unit can provide a recommendation that uses appropriate terms according to the user's level of expertise. This makes it possible to provide a recommendation using appropriate terms according to the user's level of expertise.

[0114] The providing unit can estimate the user's emotions and adjust the method of providing recommendations based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, and the like. For example, if the user is relaxed, the providing unit can provide recommendations at a leisurely pace. If the user is in a hurry, the providing unit can also provide concise and to-the-point recommendations. Furthermore, if the user is excited, the providing unit can also provide recommendations with visually stimulating effects. This allows recommendations to be provided in an appropriate manner according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] When providing a recommendation, the providing unit can select an appropriate providing method by referring to the user's past recommendation history. Optimal providing methods include, but are not limited to, notification methods and display formats, for example. The providing unit, for example, selects a similar providing method based on recommendations that the user has accepted in the past. The providing unit can also select a providing method to be avoided based on recommendations that the user has rejected in the past. Furthermore, the providing unit can analyze the user's past recommendation history and select an optimal providing method. This makes it possible to provide an optimal providing method based on the user's past recommendation history.

[0116] When providing recommendations, the providing unit can customize the provided content based on the user's current health condition and goals. Customization includes, but is not limited to, the user's health condition, goals, etc. For example, if the user is on a diet, the providing unit can provide low-calorie meals. Also, if the user is aiming to build muscle, the providing unit can provide high-protein meals. Furthermore, the providing unit can not provide ingredients that should be avoided depending on the user's health condition. This makes it possible to provide customized recommendations according to the user's health condition and goals.

[0117] The providing unit can improve the providing method by reflecting user feedback when providing recommendations. The feedback includes, but is not limited to, user ratings, comments, and the like. For example, the providing unit can provide a providing method that the user has previously preferred, with priority. The providing unit can also improve the providing interface based on the user's past feedback. Furthermore, the providing unit can also simplify the providing procedure by referring to the user's past feedback. This makes it possible to provide an improved providing method based on the user's feedback.

[0118] The providing unit can estimate the user's emotions and determine the priority of providing recommendations based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, stress, etc. For example, if the user is feeling stressed, the providing unit can prioritize providing recommendations with a relaxing effect. Furthermore, if the user is tired, the providing unit can prioritize providing recommendations with high nutritional value. Furthermore, if the user is in good health, the providing unit can prioritize providing balanced recommendations. This allows recommendations to be provided in priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] When providing recommendations, the providing unit can select an appropriate providing method based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific area, the providing unit can provide recommendations including local specialties. Furthermore, if the user is traveling, the providing unit can also provide recommendations including meals at the travel destination. Furthermore, if the user is at home, the providing unit can also provide recommendations including everyday meals. This makes it possible to provide an optimal providing method based on the user's geographical location information.

[0120] When providing recommendations, the providing unit can analyze the user's social media activity to suggest content to be offered. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. For example, the providing unit can provide recommendations based on meals shared by the user on social media. The providing unit can also analyze the content of posts by the user on social media to recommend related meals. Furthermore, the providing unit can also recommend related meals based on the activities of the user's friends on social media. This makes it possible to suggest content to be offered based on the user's social media activity.

[0121] When providing recommendations, the providing unit can adjust the providing method by reflecting the user's past feedback. The feedback includes, but is not limited to, for example, the user's ratings and comments. For example, the providing unit can preferentially provide a providing method that the user has previously preferred. The providing unit can also improve the providing interface based on the user's past feedback. Furthermore, the providing unit can also simplify the providing procedure by referring to the user's past feedback. This makes it possible to provide a customized providing method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, linking unit, reception unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and the user inputs a record of their daily meals. The connection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and links with healthcare app data to understand the user's health condition. The reception unit is realized, for example, by the reception device 38 of the smart device 14, and the user inputs the meals and ingredients they want to eat that day. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends meals using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the recommendations to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described input unit, linking unit, reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and the user inputs a record of their daily meals. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and links with healthcare app data to understand the user's health condition. The reception unit is realized, for example, by the microphone 238 of the smart glasses 214, and the user inputs the meals and ingredients they want to eat that day. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends meals using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the recommendations to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-described input unit, linking unit, receiving unit, generation unit, and providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs a record of their daily meals. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and links with healthcare application data to understand the user's health condition. The receiving unit is realized, for example, by the microphone 238 of the headset-type terminal 314, and the user inputs the meals and ingredients they want to eat that day. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends meals using a generation AI. The providing unit is realized, for example, by the speaker 240 of the headset-type terminal 314, and provides the recommendations to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, linking unit, reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and the user inputs a record of their daily meals. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and links with healthcare application data to understand the user's health condition. The reception unit is realized, for example, by the microphone 238 of the robot 414, and the user inputs the meals and ingredients they want to eat that day. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends meals using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the recommendations to the user.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The health management system can also acquire the user's exercise data and reflect it in meal recommendations. For example, if the user runs, the calorie intake of the meal can be adjusted taking into account the calories burned. If the user does strength training, meals containing a lot of protein, which is necessary for muscle repair, can be suggested. Furthermore, if the user does yoga, meals containing ingredients with a relaxing effect can be suggested. This allows for more accurate meal recommendations based on the user's exercise data.

[0124] The health management system can also acquire a user's sleep data and reflect it in meal recommendations. For example, if a user is not getting enough sleep, it can suggest meals that are suitable for replenishing energy. Also, if a user has had deep sleep, it can suggest light meals. Furthermore, if a user has irregular sleep, it can suggest meals that are easy to digest. This makes it possible to provide meal recommendations based on the user's sleep data.

[0125] The health management system can also monitor a user's stress level and reflect this in meal recommendations. For example, if a user is in a high stress state, it can suggest meals containing ingredients that have a relaxing effect. If a user is in a low stress state, it can also suggest meals that are suitable for replenishing energy. Furthermore, if a user is in a moderate stress state, it can also suggest balanced meals. This makes it possible to provide meal recommendations based on the user's stress level.

[0126] The health management system can also acquire a user's allergy data and reflect it in meal recommendations. For example, if a user is allergic to a specific ingredient, it can suggest meals that avoid that ingredient. Also, if a user has multiple allergies, it can suggest meals that take these into consideration. Furthermore, if a user is at high risk of allergies, it can suggest meals that include ingredients that are less likely to cause allergic reactions. This makes it possible to provide safe meal recommendations based on the user's allergy data.

[0127] The health management system can also acquire data on a user's food preferences and reflect this in meal recommendations. For example, if a user likes a particular dish, meals containing that dish can be suggested. If a user likes a particular ingredient, meals containing large amounts of that ingredient can be suggested. Furthermore, if a user likes a particular cooking method, meals using that cooking method can be suggested. This makes it possible to provide highly satisfying meal recommendations based on the user's food preference data.

[0128] The health management system can also estimate the user's emotions and adjust the content of meal recommendations based on the estimated emotions. For example, if the user is happy, meals suitable for replenishing energy can be suggested. If the user is sad, meals containing ingredients that have a mood-boosting effect can be suggested. Furthermore, if the user is stressed, meals containing ingredients that have a relaxing effect can be suggested. This makes it possible to provide meal recommendations that correspond to the user's emotions.

[0129] The health management system can also analyze the user's meal recommendation history and reflect it in the next recommendation. For example, it can suggest similar meals based on recommendations the user has accepted in the past. It can also avoid suggesting meals that the user should avoid based on recommendations the user has rejected in the past. It can also analyze the user's meal recommendation history and suggest balanced meals. This allows it to provide optimal meal recommendations based on the user's meal recommendation history.

[0130] The health management system can also estimate the user's emotions and adjust the timing of meal recommendations based on the estimated emotions. For example, if the user is feeling stressed, meal recommendations can be provided for times when the user is able to relax. If the user is busy, meals that can be prepared in a short time can be suggested. Furthermore, if the user is relaxed, detailed meal recommendations can be provided. This allows meal recommendations to be provided at appropriate times according to the user's emotions.

[0131] The health management system can also provide meal recommendations that include regional ingredients and dishes based on the user's geographical location information. For example, if the user is in a specific region, meals that include local specialties can be suggested. If the user is traveling, meals that are suitable for the user's travel destination can also be suggested. Furthermore, if the user is at home, regular meals can also be suggested. This allows for meal recommendations based on the user's geographical location information.

[0132] The health management system can also analyze a user's social media activity and provide related meal recommendations. For example, recommendations can be provided based on meals shared by the user on social media. It can also analyze the content of the user's social media posts to suggest related meals. It can also suggest related meals based on the activities of the user's friends on social media. This makes it possible to provide meal recommendations based on the user's social media activity.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The input unit allows the user to input a record of daily meals. The user's daily meal record may include, but is not limited to, the type of meal, the amount, and the time of the meal. The input unit can simplify input by, for example, making it optional. The input unit can also allow the user to record meals by voice input or image input. For example, the user can take a photo of the meal, and the contents of the meal can be automatically recognized using image analysis technology. Step 2: The linking unit links with healthcare app data to understand the user's health condition. Healthcare app data includes, but is not limited to, data such as weight, blood pressure, and blood sugar level. The linking unit, for example, automatically obtains data from the healthcare app to understand the user's health condition. The linking unit can also allow the user to manually input health data. Step 3: The reception unit inputs the meals and ingredients that the user wants to eat that day. The meals and ingredients that the user wants to eat that day include, but are not limited to, the name of the dish and the type of ingredients. The reception unit allows the user to input the meals and ingredients by text or voice, for example. Step 4: The generation unit uses the generation AI to analyze the information entered by the reception unit and recommend meals based on the user's health management. For example, the generation unit may suggest low-calorie meals to a user on a diet, or high-protein meals to a user aiming to build muscle. The generation unit can also suggest ingredients to avoid based on the user's health condition and goals. Step 5: The providing unit provides the recommendation generated by the generating unit to the user. For example, the providing unit provides the recommendation by a text message or a voice message. The providing unit can also provide the recommendation using a notification function of the application.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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. an input section for inputting daily meal records; A linking section that links with healthcare app data to understand the user's health condition, a reception unit for allowing a user to input the meals or ingredients they want to eat that day; a generation unit that analyzes the information input by the reception unit and recommends meals based on the user's health management; a providing unit that provides the recommendations generated by the generating unit. A system characterized by:

2. The input unit Optionally enter your daily food records 2. The system of claim 1.

3. The linking unit is Link with healthcare app data to obtain weight, blood pressure, and blood sugar data 2. The system of claim 1.

4. The reception unit The user inputs the food and ingredients they want to eat that day.

2. The system of claim 1.

5. The generation unit The system analyzes the input information and suggests low-calorie meals for users on a diet and high-protein meals for users aiming to build muscle.

2. The system of claim 1.

6. The providing unit Providing the recommendation generated by the generation unit to the user 2. The system of claim 1.

7. The input unit Estimate the user's emotions and adjust the timing of inputting food records based on the estimated user emotions 2. The system of claim 1.

8. The input unit Analyze the user's past meal records and select the appropriate input method 2. The system of claim 1.

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