System

Abstract

To provide a system that advises users on appropriate meal content and effectively utilizes meal data.SOLUTION: A system comprises: a reception unit; an analysis unit; an advice unit; a collection unit; and a provision unit. The reception unit receives user profile information and past meal content. The analysis unit analyzes the information received by the reception unit and advises users on meal content based on the estimated daily energy requirement. The advice unit advises the users on meal content on the basis of the analysis results obtained by the analysis unit. The collection unit collects multiple pieces of meal data. The provision unit provides the data collected by the collection unit to retailers and restaurants.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to provide appropriate dietary advice to individual users, and there were also issues with limited means for effectively utilizing dietary data.

[0005] The system according to the embodiment aims to provide the user with advice on appropriate meal content and to make effective use of meal data. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an advice unit, a collection unit, and a provision unit. The reception unit receives a user's profile information and past meal contents. The analysis unit analyzes the information received by the reception unit and provides advice on meal contents based on an estimated daily energy requirement. The advice unit provides advice on meal contents based on the analysis results obtained by the analysis unit. The collection unit collects multiple meal data. The provision unit provides the data collected by the collection unit to retail stores and restaurants. Effect of the Invention

[0007] The system according to the embodiment can provide the user with advice on appropriate meal content and make effective use of meal data. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dietary guidance system according to the embodiment of the present invention is a system that manages the amount of food and nutritional balance, which are essential for dieting and bodybuilding. In this system, the user's profile information (age, sex, height, weight, activity level, etc.) and past meal contents are input, and AI analyzes this information to advise the meal contents based on the estimated daily energy requirement. For example, the system considers the balance of calories and nutrients that the user has taken in the past and specifically suggests the calories and nutrients that should be taken in the next meal. This advice serves as a guide for the user to continue a healthy diet. Furthermore, a huge amount of dietary data is provided to retailers and restaurants, and monetization is performed through the usage fee. Retailers and restaurants can control the amount of purchases based on the provided dietary data and reduce costs. For example, by understanding the period when the demand for a particular ingredient increases and the tendency of a particular menu to become popular, it is possible to reduce wasteful purchases and reduce food waste. This also contributes to solving the food waste problem on a global scale. This mechanism allows users to continue a healthy diet and allows retailers and restaurants to efficiently manage purchases. As a specific example, if the user has taken a high-calorie meal in the past, a low-calorie menu is suggested for the next meal. In addition, retailers can predict when demand for certain ingredients will be high and purchase the appropriate amount to reduce inventory waste. This allows the dietary advice system to support users in managing their healthy diet and contribute to reducing food waste.

[0029] The dietary guidance system according to the embodiment includes a reception unit, an analysis unit, an advice unit, a collection unit, and a provision unit. The reception unit receives the user's profile information and past meal contents. The user's profile information includes, for example, age, sex, height, weight, activity level, etc., but is not limited to such examples. The past meal contents include, for example, type of meal, intake amount, intake time, etc., but is not limited to such examples. The reception unit, for example, stores the profile information and past meal contents input by the user in a database. The analysis unit analyzes the information received by the reception unit and advises the meal contents based on the estimated daily energy requirement. The analysis unit, for example, performs calorie calculation and nutrient balance evaluation. The analysis unit uses AI to specifically suggest calories and nutrients to be taken in the next meal based on the user's profile information and past meal contents. The advice unit advises the meal contents based on the analysis result obtained by the analysis unit. The advice unit, for example, suggests a recommended meal menu and adjustment of the intake amount. The advice unit uses AI to provide the user with specific dietary advice. The collection unit collects multiple pieces of dietary data. The collection unit, for example, stores dietary details input by the user in a database. The collection unit uses AI to efficiently collect the dietary data of the user. The provision unit provides the data collected by the collection unit to retail stores and restaurants. The provision unit, for example, performs demand forecasting for specific ingredients and popularity analysis of menu items. The provision unit uses AI to analyze the collected data and provide it to retail stores and restaurants. As a result, the dietary guidance system according to the embodiment provides advice on dietary details based on the user's profile information and past dietary details, and collects and provides a huge amount of dietary data, thereby enabling efficient dietary management and reduction of food waste.

[0030] The reception unit receives the user's profile information and past meal contents. The user's profile information includes, but is not limited to, for example, age, sex, height, weight, activity level, etc. These pieces of information are often input when the user accesses the system for the first time. The user can easily input the profile information through a dedicated application or web interface. The past meal contents include, but are not limited to, for example, the type of meal, the amount of food consumed, and the time of food consumption. The user can manually input the daily meal contents or take and upload a photo, so that the system can automatically analyze the meal contents. The reception unit, for example, stores the profile information and past meal contents input by the user in a database. The database is built on the cloud and has security measures in place, so that the user's personal information is safely protected. Furthermore, the reception unit regularly backs up the data input by the user to prevent data loss or damage. This allows the reception unit to accurately accept the user's detailed profile information and past meal contents and provide data that serves as the basis for the entire system.

[0031] The analysis unit analyzes the information received by the reception unit and advises the user on the dietary content based on the estimated daily energy requirement. The analysis unit, for example, calculates calories and evaluates the balance of nutrients. Specifically, the analysis unit calculates the basal metabolic rate and calculates the estimated daily energy requirement based on the user's profile information such as age, sex, height, weight, and activity level. Furthermore, the analysis unit analyzes the past dietary content and evaluates the calorie intake and nutrient balance. The analysis unit uses AI to specifically suggest calories and nutrients to be taken in the next meal based on the user's profile information and past dietary content. The AI ​​uses a machine learning algorithm to learn the user's dietary patterns and nutrient intake tendencies and generate an optimal diet plan. For example, if the user has taken in too many calories in the past, the AI ​​suggests reducing calories in the next meal. In addition, if a specific nutrient is lacking, ingredients that contain a lot of that nutrient are recommended. This allows the analysis unit to provide specific dietary advice according to the user's health condition and goals. Furthermore, the analysis unit can continuously improve the analysis results based on the user's feedback and provide more accurate advice.

[0032] The advice unit advises the user on the meal contents based on the analysis results obtained by the analysis unit. The advice unit, for example, suggests a recommended meal menu and adjustment of the intake amount. Specifically, the advice unit suggests a specific meal menu to the user based on the calories and nutrients to be ingested in the next meal calculated by the analysis unit. The advice unit uses AI to provide specific meal advice to the user. The AI ​​refers to a huge meal database and generates an optimal menu taking into account the user's preferences and allergy information. For example, the advice unit suggests a menu that the user has liked to eat in the past or a menu that avoids ingredients that the user is allergic to. The advice unit also provides advice to adjust the amount and timing of meal intake according to the user's goals. For example, a low-calorie, nutritionally balanced menu is suggested for a user who is trying to lose weight, and a high-protein menu is suggested for a user who is trying to build muscle. As a result, the advice unit can provide specific meal advice according to the user's health condition and goals, and support the user's eating habits. Furthermore, the advice unit can continuously improve the advice content based on the user's feedback and provide more effective meal guidance.

[0033] The collection unit collects multiple meal data. For example, the collection unit stores the meal contents input by the user in a database. The user can easily input the daily meal contents through a dedicated application or web interface. The collection unit efficiently collects the user's meal data using AI. The AI ​​can analyze the meal photos uploaded by the user using image recognition technology and automatically recognize the type of meal and the amount of intake. This saves the user the trouble of manually inputting detailed meal contents. Furthermore, the collection unit regularly backs up the user's meal data to prevent data loss or damage. The collection unit centrally manages the user's meal data and makes it accessible to the analysis unit and advice unit. This allows the collection unit to efficiently and accurately collect meal data and improve the performance of the entire system. Furthermore, the collection unit can anonymize the user's meal data and collect data while protecting privacy. This allows the collection unit to collect a huge amount of meal data while protecting the user's privacy and improve the performance of the entire system.

[0034] The providing unit provides the data collected by the collecting unit to retail stores and restaurants. For example, the providing unit performs demand forecasting for specific ingredients and popularity analysis of menus. Specifically, the demand for specific ingredients is predicted based on the collected meal data, and the demand is provided to retail stores and restaurants. The providing unit uses AI to analyze the collected data and provides it to retail stores and restaurants. AI can analyze past meal data and trends to predict the demand for specific ingredients. For example, it can predict the demand for ingredients in a specific season or event, and provide it to retail stores and restaurants. The providing unit also analyzes the popularity of menus based on the collected data and provides it to restaurants. This allows restaurants to understand popular menus and use the information to improve menus and develop new menus. Furthermore, the providing unit also contributes to reducing food waste based on the collected data. For example, food waste can be minimized by purchasing an appropriate amount of ingredients based on the demand forecast. This allows the providing unit to effectively utilize the collected data and improve the business efficiency of retail stores and restaurants. Furthermore, the providing unit can anonymize the collected data and provide the data while protecting privacy. This enables the providing unit to effectively utilize the collected data while protecting the privacy of users, thereby improving the operational efficiency of retail stores and restaurants.

[0035] The reception unit may have a function for the user to input the meal contents by a photo. The reception unit, for example, has a function for the user to input the meal contents by a photo. Specific methods and standards for inputting the meal contents by a photo include, for example, image resolution, shooting angle, recognition accuracy, etc., but are not limited to such examples. The reception unit uses AI to analyze the photo of the meal contents taken by the user and saves the meal contents in a database. As a result, the user can input the meal contents by a photo, eliminating the trouble of inputting, and making it possible to grasp the meal contents accurately. For example, the user takes a photo of the meal with a smartphone and sends it to the reception unit through an application. The reception unit uses AI to analyze the meal contents from the photo and saves it in a database. As a result, the user can easily input the meal contents, and the system can collect accurate meal data.

[0036] The analysis unit can analyze the meal contents using image recognition technology. The analysis unit, for example, analyzes the meal contents using image recognition technology. Specific types and implementation methods of image recognition technology include, for example, deep learning, object detection algorithms, etc., but are not limited to such examples. The analysis unit uses AI to analyze the photos of the meal contents input by the user and stores the meal contents in a database. As a result, the analysis accuracy of the meal contents is improved by using image recognition technology. For example, the analysis unit uses deep learning to recognize ingredients from the photos of the meal contents and calculate calories and nutrients. The analysis unit can also analyze the types and amounts of ingredients from the photos of the meal contents using object detection algorithms. As a result, the photos of the meal contents input by the user can be accurately analyzed, and the system can collect accurate meal data.

[0037] The providing unit may include a prediction unit that predicts demand for a specific ingredient. The providing unit may include, for example, a prediction unit that predicts demand for a specific ingredient. Specific methods and standards for demand prediction include, for example, past sales data, seasonal fluctuations, trend analysis, and the like, but are not limited to such examples. The providing unit uses AI to analyze collected meal data and predicts demand for the specific ingredient. This allows retail stores and restaurants to perform efficient purchasing management by predicting the demand for the specific ingredient. For example, the providing unit predicts a time when demand for the specific ingredient will increase based on past sales data. The providing unit can also predict demand for the specific ingredient based on seasonal fluctuations and trend analysis. This allows retail stores and restaurants to reduce inventory waste and reduce costs by purchasing the appropriate amount.

[0038] The providing unit may include an analysis unit that performs a popularity analysis of the menu. The providing unit may include, for example, an analysis unit that performs a popularity analysis of the menu. Specific methods and criteria for the popularity analysis include, for example, sales volume, customer reviews, and the number of mentions on social media, but are not limited to such examples. The providing unit uses AI to analyze the collected meal data and perform a popularity analysis of the menu. This allows retail stores and restaurants to grasp and provide popular menus by analyzing the popularity of the menu. For example, the providing unit identifies popular menus based on sales volume. The providing unit may also analyze popular menus based on customer reviews and the number of mentions on social media. This allows retail stores and restaurants to grasp popular menus and provide menus efficiently.

[0039] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. Specific contents of the input history and analysis methods include, for example, past input data, input frequency, input patterns, etc., but are not limited to such examples. The reception unit uses AI to analyze the user's past input history and suggests an optimal input method. For example, profile information that the user frequently inputs in the past can be automatically displayed as a candidate. In addition, it is also possible to preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, it is also possible to predict and suggest profile information to be used in a specific time period from the user's past input history. As a result, it is possible to improve the efficiency of the user's input work by suggesting an optimal input method based on the past input history.

[0040] The reception unit can automatically acquire the user's current location information when inputting profile information to simplify input. For example, the reception unit automatically acquires the user's current location information when inputting profile information to simplify input. Specific methods of acquiring and using location information include, but are not limited to, GPS data, Wi-Fi location information, and accuracy of location information. The reception unit acquires the user's current location information using AI to simplify input of profile information. For example, when a user opens an app, the current location is automatically acquired and used to estimate the activity level. In addition, when a user enters profile information, the optimal input method can be proposed taking into account weather information from the current location. Furthermore, when a user uses an app while moving, the current location can be updated in real time and reflected in the estimation of the activity level. As a result, by automatically acquiring current location information, the input work can be simplified and accurate information can be provided.

[0041] The reception unit can automatically suggest an activity level by referring to the user's past movement history when the profile information is input. For example, the reception unit automatically suggests an activity level by referring to the user's past movement history when the profile information is input. Specific contents and analysis methods of the movement history include, for example, movement distance, movement time, and visited places, but are not limited to such examples. The reception unit uses AI to analyze the user's past movement history and suggests an optimal activity level. For example, the activity level is estimated based on places that the user has frequently visited in the past. In addition, the activity level can be predicted and suggested by the user on a specific day of the week or time period. Furthermore, the user's past movement patterns can be analyzed and an optimal activity level can be suggested. As a result, the activity level can be automatically suggested based on the past movement history, making it possible to set an accurate activity level.

[0042] The reception unit can automatically suggest an activity level by referring to the user's calendar information when the profile information is input. For example, the reception unit automatically suggests an activity level by referring to the user's calendar information when the profile information is input. Specific contents and usage methods of the calendar information include, for example, the type of schedule, the frequency of schedule, and the importance of schedule, but are not limited to such examples. The reception unit uses AI to refer to the user's calendar information and automatically suggests an activity level. For example, the activity level is automatically set by referring to a schedule registered in the user's calendar. In addition, the reception unit can also suggest an activity level related to a specific event from the user's calendar information. Furthermore, the reception unit can also suggest an optimal activity level according to the schedule based on the user's calendar information. As a result, by automatically suggesting an activity level based on the calendar information, it is possible to set an accurate activity level according to the user's schedule.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the meal contents during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the meal contents during the analysis. Specific evaluation criteria and methods for the importance of the meal contents include, but are not limited to, for example, calories, nutrient balance, and meal types. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the meal contents. For example, a detailed analysis is performed for main meals (breakfast, lunch, and dinner). A brief analysis can also be performed for snacks and light meals. Furthermore, if a specific nutrient is lacking, a detailed analysis of the nutrient can also be performed. In this way, by adjusting the level of detail of the analysis based on the importance of the meal contents, it is possible to appropriately provide information required by the user.

[0044] The analysis unit can apply different analysis algorithms depending on the meal category during analysis. For example, the analysis unit applies different analysis algorithms depending on the meal category during analysis. Specific classification methods and standards for meal categories include, but are not limited to, breakfast, lunch, dinner, snacks, etc., for example. The analysis unit uses AI to apply different analysis algorithms depending on the meal category. For example, different analysis algorithms are applied for each category, such as staple food, main dish, side dish, and dessert. Also, different analysis algorithms can be applied to high-calorie foods and low-calorie foods. Furthermore, different analysis algorithms can be applied to foods rich in specific nutrients. In this way, more accurate analysis results can be provided by applying different analysis algorithms depending on the meal category.

[0045] The analysis unit can determine the priority of analysis based on the time of submission of the meal contents during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of the meal contents during analysis. Specific evaluation criteria and methods for the submission time include, for example, the submission date and time, the submission frequency, and the timing of submission, but are not limited to such examples. The analysis unit uses AI to determine the priority of analysis based on the time of submission of the meal contents. For example, the most recent meal contents are analyzed with priority. In addition, meal contents submitted in a specific time period can be analyzed with priority. Furthermore, meal contents specified by the user can be analyzed with priority. In this way, by determining the priority of analysis based on the time of submission of the meal contents, important information for the user can be quickly provided.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the meal contents during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the meal contents during analysis. Specific evaluation criteria and methods for the relevance include, for example, commonality of ingredients, similarity of cooking methods, common points of nutrients, etc., but are not limited to such examples. The analysis unit adjusts the order of analysis based on the relevance of the meal contents using AI. For example, meal contents of the same category are analyzed together. Also, meal contents related to a specific nutrient can be analyzed together. Furthermore, meal contents related to the user's health goal can be preferentially analyzed. As a result, by adjusting the order of analysis based on the relevance of the meal contents, information highly relevant to the user can be provided preferentially.

[0047] The advice unit can adjust the level of detail of the advice based on the importance of the meal contents when giving advice. For example, the advice unit adjusts the level of detail of the advice based on the importance of the meal contents when giving advice. Specific evaluation criteria and methods for the importance of the meal contents include, for example, calories, nutrient balance, and meal types, but are not limited to such examples. The advice unit adjusts the level of detail of the advice based on the importance of the meal contents using AI. For example, detailed advice is provided for main meals (breakfast, lunch, and dinner). Also, brief advice can be provided for snacks and light meals. Furthermore, if a specific nutrient is lacking, detailed advice on that nutrient can be provided. In this way, by adjusting the level of detail of the advice based on the importance of the meal contents, it is possible to appropriately provide information required by the user.

[0048] The advice unit can apply different advice algorithms depending on the meal category when giving advice. For example, the advice unit applies different advice algorithms depending on the meal category when giving advice. Specific classification methods and standards for meal categories include, but are not limited to, breakfast, lunch, dinner, snacks, etc., for example. The advice unit uses AI to apply different advice algorithms depending on the meal category. For example, different advice algorithms are applied for each category, such as staple food, main dish, side dish, and dessert. Also, different advice algorithms can be applied to high-calorie foods and low-calorie foods. Furthermore, different advice algorithms can be applied to foods rich in specific nutrients. In this way, more accurate advice can be provided by applying different advice algorithms depending on the meal category.

[0049] The advice unit can determine the priority of advice based on the submission time of meal contents when giving advice. The advice unit, for example, determines the priority of advice based on the submission time of meal contents when giving advice. Specific evaluation criteria and methods for the submission time include, for example, the submission date and time, the submission frequency, and the submission timing, but are not limited to such examples. The advice unit uses AI to determine the priority of advice based on the submission time of meal contents. For example, the most recent meal contents can be given priority. In addition, it is also possible to give priority to advice on meal contents submitted in a specific time period. Furthermore, it is also possible to give priority to advice on meal contents specified by the user. In this way, by determining the priority of advice based on the submission time of meal contents, it is possible to quickly provide information that is important to the user.

[0050] The advice unit can adjust the order of advice based on the relevance of meal contents when giving advice. The advice unit, for example, adjusts the order of advice based on the relevance of meal contents when giving advice. Specific evaluation criteria and methods for relevance include, for example, commonality of ingredients, similarity of cooking methods, commonality of nutrients, etc., but are not limited to such examples. The advice unit adjusts the order of advice based on the relevance of meal contents using AI. For example, advice is given on meal contents of the same category together. Also, advice can be given on meal contents related to specific nutrients together. Furthermore, advice can be given preferentially on meal contents related to the user's health goal. As a result, by adjusting the order of advice based on the relevance of meal contents, information highly relevant to the user can be provided preferentially.

[0051] The collection unit can analyze the user's past meal history and select an appropriate collection method when collecting meal data. For example, when collecting meal data, the collection unit analyzes the user's past meal history and selects the optimal collection method. Specific selection criteria and methods for the collection method include, for example, manual input, photo input, voice input, etc., but are not limited to such examples. The collection unit uses AI to analyze the user's past meal history and select the optimal collection method. For example, the collection method is selected based on the meals that the user frequently ate in the past. In addition, a method for collecting at a specific time period can be selected from the user's past meal history. Furthermore, the user's past meal history can be analyzed and the most efficient collection method can be selected. As a result, efficient data collection is possible by selecting the optimal collection method based on the past meal history.

[0052] The collection unit can filter the dietary data based on the user's current health condition and goals when collecting the dietary data. For example, the collection unit filters the dietary data based on the user's current health condition and goals when collecting the dietary data. Specific evaluation criteria and methods for the health condition include, for example, weight, blood pressure, blood sugar level, etc., but are not limited to such examples. Specific setting methods and criteria for goals include, for example, weight loss goals, muscle building goals, health maintenance goals, etc., but are not limited to such examples. The collection unit uses AI to filter the dietary data based on the user's current health condition and goals. For example, if the user is on a diet, low-calorie diet data is preferentially collected. In addition, if the user is aiming to build muscle, high-protein diet data can also be preferentially collected. Furthermore, diet data rich in specific nutrients can also be preferentially collected according to the user's health condition. This makes it possible to collect data according to the user's health condition and goals.

[0053] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting meal data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting meal data. Specific methods of acquiring and using geographical location information include, but are not limited to, GPS data, Wi-Fi location information, and accuracy of location information. The collection unit uses AI to preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is in a specific area, meal data of that area is preferentially collected. In addition, when the user is traveling, meal data of the travel destination can also be preferentially collected. Furthermore, based on the geographical location information of the user, highly relevant meal data can also be preferentially collected. As a result, highly relevant data can be efficiently collected by considering the geographical location information.

[0054] The collection unit can analyze the user's social media activity and collect related data when collecting meal data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting meal data. Specific analysis methods and criteria for social media activity include, but are not limited to, for example, the content of posts, the number of likes, and the number of comments. The collection unit uses AI to analyze the user's social media activity and collect related data. For example, the collection unit collects related data based on the meal content shared by the user on social media. In addition, the collection unit can also collect related data based on the meal content of accounts that the user follows on social media. Furthermore, the collection unit can analyze the user's social media activity and prioritize collection of highly relevant meal data. This allows efficient data collection by collecting highly relevant data based on social media activity.

[0055] The providing unit can adjust the level of detail of the provision based on the importance of the meal data at the time of provision. For example, the providing unit adjusts the level of detail of the provision based on the importance of the meal data at the time of provision. Specific methods and standards for adjusting the level of detail of the provision include, for example, the level of detail of the data, the amount of information to be displayed, and the like, but are not limited to such examples. The providing unit adjusts the level of detail of the provision based on the importance of the meal data using AI. For example, detailed data is provided for main meals (breakfast, lunch, dinner). Also, concise data can be provided for snacks and light meals. Furthermore, if a specific nutrient is lacking, detailed data on the nutrient can be provided. As a result, by adjusting the level of detail of the provision based on the importance of the meal data, it is possible to appropriately provide information required by the user.

[0056] The providing unit can apply different providing algorithms depending on the category of the meal data at the time of providing. For example, the providing unit applies different providing algorithms depending on the category of the meal data at the time of providing. Specific types and implementation methods of the providing algorithm include, for example, rule-based, machine learning-based, and the like, but are not limited to such examples. The providing unit uses AI to apply different providing algorithms depending on the category of the meal data. For example, different providing algorithms are applied for each category such as staple food, main dish, side dish, and dessert. Also, different providing algorithms can be applied to high-calorie foods and low-calorie foods. Furthermore, different providing algorithms can be applied to foods rich in specific nutrients. This makes it possible to provide more accurate data by applying different providing algorithms depending on the category of the meal data.

[0057] The providing unit can determine the priority of provision based on the time of submission of the meal data at the time of provision. For example, the providing unit determines the priority of provision based on the time of submission of the meal data at the time of provision. Specific methods and criteria for determining the priority of provision include, for example, the time of submission, importance, relevance, etc., but are not limited to such examples. The providing unit uses AI to determine the priority of provision based on the time of submission of the meal data. For example, the most recent meal data is provided preferentially. Also, meal data submitted in a specific time period can be provided preferentially. Furthermore, meal data specified by the user can be provided preferentially. In this way, by determining the priority of provision based on the time of submission of the meal data, it is possible to quickly provide information that is important to the user.

[0058] The providing unit can adjust the order of provision based on the relevance of the dietary data at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of the dietary data at the time of provision. Specific adjustment methods and standards for the order of provision include, for example, a method of prioritizing highly relevant information, but are not limited to such examples. The providing unit adjusts the order of provision based on the relevance of the dietary data using AI. For example, dietary data of the same category is provided together. Dietary data related to a specific nutrient can also be provided together. Furthermore, dietary data related to the user's health goal can be provided preferentially. As a result, by adjusting the order of provision based on the relevance of the dietary data, information highly relevant to the user can be provided preferentially.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit may have a voice input function when inputting the user's meal contents. For example, by inputting the meal contents by voice, the user can save time and quickly collect data. Specific methods and standards for voice input include, but are not limited to, voice recognition technology, noise canceling technology, and voice command settings. The reception unit uses AI to analyze the user's voice and store the meal contents in a database. This allows the user to easily input the meal contents and the system to collect accurate meal data. For example, when the user says "I had toast and coffee for breakfast" into the smartphone, the reception unit analyzes the contents and stores them in a database. In addition, voice input allows the user to input the meal contents without using their hands, improving convenience.

[0061] The analysis unit can have a function of evaluating the freshness of ingredients included in a photo of a meal when analyzing the user's meal contents. For example, by analyzing the color and shape of ingredients and evaluating their freshness, the user can be advised to use fresh ingredients. Specific methods and standards for evaluating freshness include, but are not limited to, changes in color, shape, and texture. The analysis unit uses AI to evaluate the freshness of ingredients from photos of meals and stores the results in a database. This allows the user to continue eating healthy meals by using fresh ingredients. For example, the analysis unit evaluates whether the color of tomatoes is vivid, and advises the user that "fresh tomatoes are used" if the freshness is high. Also, if the freshness is low, the analysis unit can advise the user that "the freshness of the tomatoes has decreased. Choose fresh ones."

[0062] When providing the dietary data of the user, the providing unit can provide advice taking into consideration the absorption rate of nutrients in the diet. For example, if the absorption rate of a specific nutrient is improved by ingesting it together with other nutrients, the combination can be suggested. Specific evaluation criteria and methods for the absorption rate of nutrients include, but are not limited to, a combination of vitamin C and iron, a combination of calcium and vitamin D, and the like. The providing unit uses AI to analyze the dietary data of the user and provide advice taking into consideration the absorption rate of nutrients. This allows the user to efficiently ingest nutrients and continue a healthy diet. For example, the providing unit suggests that when the user ingests foodstuffs that are rich in iron, the user ingests foodstuffs that are rich in vitamin C at the same time. It can also suggest that when ingesting foodstuffs that are rich in calcium, the user ingests foodstuffs that are rich in vitamin D at the same time.

[0063] When providing the user's dietary data, the provision unit can provide advice that takes into account the environmental factors of the meal. For example, the provision unit can suggest optimal dietary content by considering the place and time of eating the meal, the activity during the meal, etc. Specific evaluation criteria and methods for environmental factors include, for example, the brightness of the place where the meal is eaten, the presence or absence of music during the meal, the time of eating the meal, etc., but are not limited to such examples. The provision unit uses AI to analyze the user's dietary data and environmental factors and suggests optimal dietary content. This allows the user to eat an optimal meal according to the environment and continue to eat a healthy diet. For example, when the user eats a meal in a quiet place, the provision unit can suggest ingredients that have a relaxing effect. In addition, when the user eats a meal during a busy time, the provision unit can also suggest ingredients that are easily ingested and have a good nutritional balance.

[0064] When providing the user's dietary data, the provision unit can provide advice that takes into account the cultural background of the meal. For example, traditional ingredients and dishes can be suggested based on the user's place of origin and culture. Specific evaluation criteria and methods for the cultural background include, but are not limited to, the user's place of origin, religious dietary restrictions, traditional dishes, etc. The provision unit uses AI to analyze the user's dietary data and cultural background and suggests optimal meal content. This allows the user to eat an optimal meal according to the culture and continue to eat a healthy diet. For example, if the user is from Japan, the provision unit can suggest traditional ingredients and dishes of Japanese cuisine. Also, if the user is a vegetarian, the provision unit can suggest dishes centered on plant-based ingredients.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit receives the user's profile information and past meal contents. The user's profile information includes, for example, age, sex, height, weight, activity level, etc. The past meal contents include, for example, type of food, intake amount, intake time, etc. The reception unit saves the profile information and past meal contents input by the user in a database. Step 2: The analysis unit analyzes the information received by the reception unit and provides dietary advice based on the estimated daily energy requirement. The analysis unit calculates calories and evaluates nutrient balance, and uses AI to provide specific suggestions on calories and nutrients to be consumed in the next meal based on the user's profile information and past meal content. Step 3: The advice unit advises the user on meal content based on the analysis results obtained by the analysis unit. The advice unit proposes recommended meal menus and adjustments to intake amounts, and provides specific dietary advice to the user using AI. Step 4: The collection unit collects multiple meal data. The collection unit stores the meal details entered by the user in a database and efficiently collects the user's meal data using AI. Step 5: The provision unit provides the data collected by the collection unit to retailers and restaurants. The provision unit predicts demand for specific ingredients and analyzes the popularity of menu items, analyzes the collected data using AI, and provides the data to retailers and restaurants.

[0067] (Example 2) The dietary guidance system according to the embodiment of the present invention is a system that manages the amount of food and nutritional balance, which are essential for dieting and bodybuilding. In this system, the user's profile information (age, sex, height, weight, activity level, etc.) and past meal contents are input, and AI analyzes this information to advise the meal contents based on the estimated daily energy requirement. For example, the system considers the balance of calories and nutrients that the user has taken in the past and specifically suggests the calories and nutrients that should be taken in the next meal. This advice serves as a guide for the user to continue a healthy diet. Furthermore, a huge amount of dietary data is provided to retailers and restaurants, and monetization is performed through the usage fee. Retailers and restaurants can control the amount of purchases based on the provided dietary data and reduce costs. For example, by understanding the period when the demand for a particular ingredient increases and the tendency of a particular menu to become popular, it is possible to reduce wasteful purchases and reduce food waste. This also contributes to solving the food waste problem on a global scale. This mechanism allows users to continue a healthy diet and allows retailers and restaurants to efficiently manage purchases. As a specific example, if the user has taken a high-calorie meal in the past, a low-calorie menu is suggested for the next meal. In addition, retailers can predict when demand for certain ingredients will be high and purchase the appropriate amount to reduce inventory waste. This allows the dietary advice system to support users in managing their healthy diet and contribute to reducing food waste.

[0068] The dietary guidance system according to the embodiment includes a reception unit, an analysis unit, an advice unit, a collection unit, and a provision unit. The reception unit receives the user's profile information and past meal contents. The user's profile information includes, for example, age, sex, height, weight, activity level, etc., but is not limited to such examples. The past meal contents include, for example, type of meal, intake amount, intake time, etc., but is not limited to such examples. The reception unit, for example, stores the profile information and past meal contents input by the user in a database. The analysis unit analyzes the information received by the reception unit and advises the meal contents based on the estimated daily energy requirement. The analysis unit, for example, performs calorie calculation and nutrient balance evaluation. The analysis unit uses AI to specifically suggest calories and nutrients to be taken in the next meal based on the user's profile information and past meal contents. The advice unit advises the meal contents based on the analysis result obtained by the analysis unit. The advice unit, for example, suggests a recommended meal menu and adjustment of the intake amount. The advice unit uses AI to provide the user with specific dietary advice. The collection unit collects multiple pieces of dietary data. The collection unit, for example, stores dietary details input by the user in a database. The collection unit uses AI to efficiently collect the dietary data of the user. The provision unit provides the data collected by the collection unit to retail stores and restaurants. The provision unit, for example, performs demand forecasting for specific ingredients and popularity analysis of menu items. The provision unit uses AI to analyze the collected data and provide it to retail stores and restaurants. As a result, the dietary guidance system according to the embodiment provides advice on dietary details based on the user's profile information and past dietary details, and collects and provides a huge amount of dietary data, thereby enabling efficient dietary management and reduction of food waste.

[0069] The reception unit receives the user's profile information and past meal contents. The user's profile information includes, but is not limited to, for example, age, sex, height, weight, activity level, etc. These pieces of information are often input when the user accesses the system for the first time. The user can easily input the profile information through a dedicated application or web interface. The past meal contents include, but are not limited to, for example, the type of meal, the amount of food consumed, and the time of food consumption. The user can manually input the daily meal contents or take and upload a photo, so that the system can automatically analyze the meal contents. The reception unit, for example, stores the profile information and past meal contents input by the user in a database. The database is built on the cloud and has security measures in place, so that the user's personal information is safely protected. Furthermore, the reception unit regularly backs up the data input by the user to prevent data loss or damage. This allows the reception unit to accurately accept the user's detailed profile information and past meal contents and provide data that serves as the basis for the entire system.

[0070] The analysis unit analyzes the information received by the reception unit and advises the user on the dietary content based on the estimated daily energy requirement. The analysis unit, for example, calculates calories and evaluates the balance of nutrients. Specifically, the analysis unit calculates the basal metabolic rate and calculates the estimated daily energy requirement based on the user's profile information such as age, sex, height, weight, and activity level. Furthermore, the analysis unit analyzes the past dietary content and evaluates the calorie intake and nutrient balance. The analysis unit uses AI to specifically suggest calories and nutrients to be taken in the next meal based on the user's profile information and past dietary content. The AI ​​uses a machine learning algorithm to learn the user's dietary patterns and nutrient intake tendencies and generate an optimal diet plan. For example, if the user has taken in too many calories in the past, the AI ​​suggests reducing calories in the next meal. In addition, if a specific nutrient is lacking, ingredients that contain a lot of that nutrient are recommended. This allows the analysis unit to provide specific dietary advice according to the user's health condition and goals. Furthermore, the analysis unit can continuously improve the analysis results based on the user's feedback and provide more accurate advice.

[0071] The advice unit advises the user on the meal contents based on the analysis results obtained by the analysis unit. The advice unit, for example, suggests a recommended meal menu and adjustment of the intake amount. Specifically, the advice unit suggests a specific meal menu to the user based on the calories and nutrients to be ingested in the next meal calculated by the analysis unit. The advice unit uses AI to provide specific meal advice to the user. The AI ​​refers to a huge meal database and generates an optimal menu taking into account the user's preferences and allergy information. For example, the advice unit suggests a menu that the user has liked to eat in the past or a menu that avoids ingredients that the user is allergic to. The advice unit also provides advice to adjust the amount and timing of meal intake according to the user's goals. For example, a low-calorie, nutritionally balanced menu is suggested for a user who is trying to lose weight, and a high-protein menu is suggested for a user who is trying to build muscle. As a result, the advice unit can provide specific meal advice according to the user's health condition and goals, and support the user's eating habits. Furthermore, the advice unit can continuously improve the advice content based on the user's feedback and provide more effective meal guidance.

[0072] The collection unit collects multiple meal data. For example, the collection unit stores the meal contents input by the user in a database. The user can easily input the daily meal contents through a dedicated application or web interface. The collection unit efficiently collects the user's meal data using AI. The AI ​​can analyze the meal photos uploaded by the user using image recognition technology and automatically recognize the type of meal and the amount of intake. This saves the user the trouble of manually inputting detailed meal contents. Furthermore, the collection unit regularly backs up the user's meal data to prevent data loss or damage. The collection unit centrally manages the user's meal data and makes it accessible to the analysis unit and advice unit. This allows the collection unit to efficiently and accurately collect meal data and improve the performance of the entire system. Furthermore, the collection unit can anonymize the user's meal data and collect data while protecting privacy. This allows the collection unit to collect a huge amount of meal data while protecting the user's privacy and improve the performance of the entire system.

[0073] The providing unit provides the data collected by the collecting unit to retail stores and restaurants. For example, the providing unit performs demand forecasting for specific ingredients and popularity analysis of menus. Specifically, the demand for specific ingredients is predicted based on the collected meal data, and the demand is provided to retail stores and restaurants. The providing unit uses AI to analyze the collected data and provides it to retail stores and restaurants. AI can analyze past meal data and trends to predict the demand for specific ingredients. For example, it can predict the demand for ingredients in a specific season or event, and provide it to retail stores and restaurants. The providing unit also analyzes the popularity of menus based on the collected data and provides it to restaurants. This allows restaurants to understand popular menus and use the information to improve menus and develop new menus. Furthermore, the providing unit also contributes to reducing food waste based on the collected data. For example, food waste can be minimized by purchasing an appropriate amount of ingredients based on the demand forecast. This allows the providing unit to effectively utilize the collected data and improve the business efficiency of retail stores and restaurants. Furthermore, the providing unit can anonymize the collected data and provide the data while protecting privacy. This enables the providing unit to effectively utilize the collected data while protecting the privacy of users, thereby improving the operational efficiency of retail stores and restaurants.

[0074] The reception unit may have a function for the user to input the meal contents by a photo. The reception unit, for example, has a function for the user to input the meal contents by a photo. Specific methods and standards for inputting the meal contents by a photo include, for example, image resolution, shooting angle, recognition accuracy, etc., but are not limited to such examples. The reception unit uses AI to analyze the photo of the meal contents taken by the user and saves the meal contents in a database. As a result, the user can input the meal contents by a photo, eliminating the trouble of inputting, and making it possible to grasp the meal contents accurately. For example, the user takes a photo of the meal with a smartphone and sends it to the reception unit through an application. The reception unit uses AI to analyze the meal contents from the photo and saves it in a database. As a result, the user can easily input the meal contents, and the system can collect accurate meal data.

[0075] The analysis unit can analyze the meal contents using image recognition technology. The analysis unit, for example, analyzes the meal contents using image recognition technology. Specific types and implementation methods of image recognition technology include, for example, deep learning, object detection algorithms, etc., but are not limited to such examples. The analysis unit uses AI to analyze the photos of the meal contents input by the user and stores the meal contents in a database. As a result, the analysis accuracy of the meal contents is improved by using image recognition technology. For example, the analysis unit uses deep learning to recognize ingredients from the photos of the meal contents and calculate calories and nutrients. The analysis unit can also analyze the types and amounts of ingredients from the photos of the meal contents using object detection algorithms. As a result, the photos of the meal contents input by the user can be accurately analyzed, and the system can collect accurate meal data.

[0076] The providing unit may include a prediction unit that predicts demand for a specific ingredient. The providing unit may include, for example, a prediction unit that predicts demand for a specific ingredient. Specific methods and standards for demand prediction include, for example, past sales data, seasonal fluctuations, trend analysis, and the like, but are not limited to such examples. The providing unit uses AI to analyze collected meal data and predicts demand for the specific ingredient. This allows retail stores and restaurants to perform efficient purchasing management by predicting the demand for the specific ingredient. For example, the providing unit predicts a time when demand for the specific ingredient will increase based on past sales data. The providing unit can also predict demand for the specific ingredient based on seasonal fluctuations and trend analysis. This allows retail stores and restaurants to reduce inventory waste and reduce costs by purchasing the appropriate amount.

[0077] The providing unit may include an analysis unit that performs a popularity analysis of the menu. The providing unit may include, for example, an analysis unit that performs a popularity analysis of the menu. Specific methods and criteria for the popularity analysis include, for example, sales volume, customer reviews, and the number of mentions on social media, but are not limited to such examples. The providing unit uses AI to analyze the collected meal data and perform a popularity analysis of the menu. This allows retail stores and restaurants to grasp and provide popular menus by analyzing the popularity of the menu. For example, the providing unit identifies popular menus based on sales volume. The providing unit may also analyze popular menus based on customer reviews and the number of mentions on social media. This allows retail stores and restaurants to grasp popular menus and provide menus efficiently.

[0078] The reception unit can estimate the user's emotion and change the input method of the profile information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input method of the profile information based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The reception unit uses AI to estimate the user's emotion and adjusts the input method based on the emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. In addition, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to allow the user to quickly input profile information. In this way, by adjusting the input method according to the user's emotion, the user's stress can be reduced and the input work can be performed smoothly. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. Specific contents of the input history and analysis methods include, for example, past input data, input frequency, input patterns, etc., but are not limited to such examples. The reception unit uses AI to analyze the user's past input history and suggests an optimal input method. For example, profile information that the user frequently inputs in the past can be automatically displayed as a candidate. In addition, it is also possible to preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, it is also possible to predict and suggest profile information to be used in a specific time period from the user's past input history. As a result, it is possible to improve the efficiency of the user's input work by suggesting an optimal input method based on the past input history.

[0080] The reception unit can automatically acquire the user's current location information when inputting profile information to simplify input. For example, the reception unit automatically acquires the user's current location information when inputting profile information to simplify input. Specific methods of acquiring and using location information include, but are not limited to, GPS data, Wi-Fi location information, and accuracy of location information. The reception unit acquires the user's current location information using AI to simplify input of profile information. For example, when a user opens an app, the current location is automatically acquired and used to estimate the activity level. In addition, when a user enters profile information, the optimal input method can be proposed taking into account weather information from the current location. Furthermore, when a user uses an app while moving, the current location can be updated in real time and reflected in the estimation of the activity level. As a result, by automatically acquiring current location information, the input work can be simplified and accurate information can be provided.

[0081] The reception unit can estimate the user's emotion and change the design of the input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the design of the input interface based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The reception unit uses AI to estimate the user's emotion and adjusts the design of the interface based on the emotion. For example, when the user is nervous, an interface with calm colors is provided to reduce visual stress. Also, when the user is enjoying himself, an interface with bright colors can be provided to make input work enjoyable. Furthermore, when the user is tired, a simple and highly visible interface can be provided to make input work easier. As a result, the design of the interface can be adjusted according to the user's emotion, making the user's input work comfortable. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The reception unit can automatically suggest an activity level by referring to the user's past movement history when the profile information is input. For example, the reception unit automatically suggests an activity level by referring to the user's past movement history when the profile information is input. Specific contents and analysis methods of the movement history include, for example, movement distance, movement time, and visited places, but are not limited to such examples. The reception unit uses AI to analyze the user's past movement history and suggests an optimal activity level. For example, the activity level is estimated based on places that the user has frequently visited in the past. In addition, the activity level can be predicted and suggested by the user on a specific day of the week or time period. Furthermore, the user's past movement patterns can be analyzed and an optimal activity level can be suggested. As a result, the activity level can be automatically suggested based on the past movement history, making it possible to set an accurate activity level.

[0083] The reception unit can automatically suggest an activity level by referring to the user's calendar information when the profile information is input. For example, the reception unit automatically suggests an activity level by referring to the user's calendar information when the profile information is input. Specific contents and usage methods of the calendar information include, for example, the type of schedule, the frequency of schedule, and the importance of schedule, but are not limited to such examples. The reception unit uses AI to refer to the user's calendar information and automatically suggests an activity level. For example, the activity level is automatically set by referring to a schedule registered in the user's calendar. In addition, the reception unit can also suggest an activity level related to a specific event from the user's calendar information. Furthermore, the reception unit can also suggest an optimal activity level according to the schedule based on the user's calendar information. As a result, by automatically suggesting an activity level based on the calendar information, it is possible to set an accurate activity level according to the user's schedule.

[0084] The analysis unit can estimate the user's emotion and change the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The analysis unit uses AI to estimate the user's emotion and adjusts the expression method of the analysis based on the emotion. For example, when the user is relaxed, a detailed analysis result is provided. Also, when the user is in a hurry, a concise analysis result that covers the main points can be provided. Furthermore, when the user is excited, an analysis result with a visually stimulating effect can be provided. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the meal contents during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the meal contents during the analysis. Specific evaluation criteria and methods for the importance of the meal contents include, but are not limited to, for example, calories, nutrient balance, and meal types. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the meal contents. For example, a detailed analysis is performed for main meals (breakfast, lunch, and dinner). A brief analysis can also be performed for snacks and light meals. Furthermore, if a specific nutrient is lacking, a detailed analysis of the nutrient can also be performed. In this way, by adjusting the level of detail of the analysis based on the importance of the meal contents, it is possible to appropriately provide information required by the user.

[0086] The analysis unit can apply different analysis algorithms depending on the meal category during analysis. For example, the analysis unit applies different analysis algorithms depending on the meal category during analysis. Specific classification methods and standards for meal categories include, but are not limited to, breakfast, lunch, dinner, snacks, etc., for example. The analysis unit uses AI to apply different analysis algorithms depending on the meal category. For example, different analysis algorithms are applied for each category, such as staple food, main dish, side dish, and dessert. Also, different analysis algorithms can be applied to high-calorie foods and low-calorie foods. Furthermore, different analysis algorithms can be applied to foods rich in specific nutrients. In this way, more accurate analysis results can be provided by applying different analysis algorithms depending on the meal category.

[0087] The analysis unit can estimate the user's emotion and change the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The analysis unit uses AI to estimate the user's emotion and adjusts the length of the analysis based on the emotion. For example, if the user is in a hurry, a short analysis result that is to the point can be provided. Also, if the user is relaxed, a longer analysis result including detailed explanations can be provided. Furthermore, if the user is excited, an analysis result with a visually stimulating effect can be provided. In this way, by adjusting the length of the analysis according to the user's emotion, it is possible to provide an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The analysis unit can determine the priority of analysis based on the time of submission of the meal contents during analysis. The analysis unit, for example, determines the priority of analysis based on the time of submission of the meal contents during analysis. Specific evaluation criteria and methods for the submission time include, for example, the submission date and time, the submission frequency, and the timing of submission, but are not limited to such examples. The analysis unit uses AI to determine the priority of analysis based on the time of submission of the meal contents. For example, the most recent meal contents are analyzed with priority. In addition, meal contents submitted in a specific time period can be analyzed with priority. Furthermore, meal contents specified by the user can be analyzed with priority. In this way, by determining the priority of analysis based on the time of submission of the meal contents, important information for the user can be quickly provided.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the meal contents during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the meal contents during analysis. Specific evaluation criteria and methods for the relevance include, for example, commonality of ingredients, similarity of cooking methods, common points of nutrients, etc., but are not limited to such examples. The analysis unit adjusts the order of analysis based on the relevance of the meal contents using AI. For example, meal contents of the same category are analyzed together. Also, meal contents related to a specific nutrient can be analyzed together. Furthermore, meal contents related to the user's health goal can be preferentially analyzed. As a result, by adjusting the order of analysis based on the relevance of the meal contents, information highly relevant to the user can be provided preferentially.

[0090] The advice unit can estimate the user's emotion and change the way of expressing advice based on the estimated user's emotion. The advice unit, for example, estimates the user's emotion and adjusts the way of expressing advice based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The advice unit uses AI to estimate the user's emotion and adjusts the way of expressing advice based on the emotion. For example, when the user is relaxed, detailed advice is provided. Also, when the user is in a hurry, concise advice that focuses on the main points can be provided. Furthermore, when the user is excited, advice with visually stimulating effects can be provided. In this way, by adjusting the way of expressing advice according to the user's emotion, it is possible to provide advice that is easy for the user to understand. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The advice unit can adjust the level of detail of the advice based on the importance of the meal contents when giving advice. For example, the advice unit adjusts the level of detail of the advice based on the importance of the meal contents when giving advice. Specific evaluation criteria and methods for the importance of the meal contents include, for example, calories, nutrient balance, and meal types, but are not limited to such examples. The advice unit adjusts the level of detail of the advice based on the importance of the meal contents using AI. For example, detailed advice is provided for main meals (breakfast, lunch, and dinner). Also, brief advice can be provided for snacks and light meals. Furthermore, if a specific nutrient is lacking, detailed advice on that nutrient can be provided. In this way, by adjusting the level of detail of the advice based on the importance of the meal contents, it is possible to appropriately provide information required by the user.

[0092] The advice unit can apply different advice algorithms depending on the meal category when giving advice. For example, the advice unit applies different advice algorithms depending on the meal category when giving advice. Specific classification methods and standards for meal categories include, but are not limited to, breakfast, lunch, dinner, snacks, etc., for example. The advice unit uses AI to apply different advice algorithms depending on the meal category. For example, different advice algorithms are applied for each category, such as staple food, main dish, side dish, and dessert. Also, different advice algorithms can be applied to high-calorie foods and low-calorie foods. Furthermore, different advice algorithms can be applied to foods rich in specific nutrients. In this way, more accurate advice can be provided by applying different advice algorithms depending on the meal category.

[0093] The advice unit can estimate the user's emotion and change the length of the advice based on the estimated user's emotion. The advice unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated user's emotion. Specific methods and standards for estimating the emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The advice unit uses AI to estimate the user's emotion and adjusts the length of the advice based on the emotion. For example, if the user is in a hurry, short advice that is to the point is provided. Also, if the user is relaxed, longer advice including detailed explanations can be provided. Furthermore, if the user is excited, advice with visually stimulating effects can be provided. In this way, by adjusting the length of the advice according to the user's emotion, it is possible to provide the most suitable advice for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The advice unit can determine the priority of advice based on the submission time of meal contents when giving advice. The advice unit, for example, determines the priority of advice based on the submission time of meal contents when giving advice. Specific evaluation criteria and methods for the submission time include, for example, the submission date and time, the submission frequency, and the submission timing, but are not limited to such examples. The advice unit uses AI to determine the priority of advice based on the submission time of meal contents. For example, the most recent meal contents can be given priority. In addition, it is also possible to give priority to advice on meal contents submitted in a specific time period. Furthermore, it is also possible to give priority to advice on meal contents specified by the user. In this way, by determining the priority of advice based on the submission time of meal contents, it is possible to quickly provide information that is important to the user.

[0095] The advice unit can adjust the order of advice based on the relevance of meal contents when giving advice. The advice unit, for example, adjusts the order of advice based on the relevance of meal contents when giving advice. Specific evaluation criteria and methods for relevance include, for example, commonality of ingredients, similarity of cooking methods, commonality of nutrients, etc., but are not limited to such examples. The advice unit adjusts the order of advice based on the relevance of meal contents using AI. For example, advice is given on meal contents of the same category together. Also, advice can be given on meal contents related to specific nutrients together. Furthermore, advice can be given preferentially on meal contents related to the user's health goal. As a result, by adjusting the order of advice based on the relevance of meal contents, information highly relevant to the user can be provided preferentially.

[0096] The collection unit can estimate the user's emotion and change the collection timing of the meal data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the meal data based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The collection unit uses AI to estimate the user's emotion and adjusts the collection timing based on the emotion. For example, when the user is relaxed, meal data is collected periodically. Also, when the user is in a hurry, meal data can be collected quickly. Furthermore, when the user is excited, a collection method that adds a visually stimulating effect can be provided. This enables efficient data collection by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The collection unit can analyze the user's past meal history and select an appropriate collection method when collecting meal data. For example, when collecting meal data, the collection unit analyzes the user's past meal history and selects the optimal collection method. Specific selection criteria and methods for the collection method include, for example, manual input, photo input, voice input, etc., but are not limited to such examples. The collection unit uses AI to analyze the user's past meal history and select the optimal collection method. For example, the collection method is selected based on the meals that the user frequently ate in the past. In addition, a method for collecting at a specific time period can be selected from the user's past meal history. Furthermore, the user's past meal history can be analyzed and the most efficient collection method can be selected. As a result, efficient data collection is possible by selecting the optimal collection method based on the past meal history.

[0098] The collection unit can filter the dietary data based on the user's current health condition and goals when collecting the dietary data. For example, the collection unit filters the dietary data based on the user's current health condition and goals when collecting the dietary data. Specific evaluation criteria and methods for the health condition include, for example, weight, blood pressure, blood sugar level, etc., but are not limited to such examples. Specific setting methods and criteria for goals include, for example, weight loss goals, muscle building goals, health maintenance goals, etc., but are not limited to such examples. The collection unit uses AI to filter the dietary data based on the user's current health condition and goals. For example, if the user is on a diet, low-calorie diet data is preferentially collected. In addition, if the user is aiming to build muscle, high-protein diet data can also be preferentially collected. Furthermore, diet data rich in specific nutrients can also be preferentially collected according to the user's health condition. This makes it possible to collect data according to the user's health condition and goals.

[0099] The collection unit can estimate the user's emotion and set a priority order for the meal data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines a priority order for the meal data to be collected based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The collection unit uses AI to estimate the user's emotion and determines a priority order for the meal data to be collected based on the emotion. For example, when the user is relaxed, detailed meal data is preferentially collected. Also, when the user is in a hurry, concise meal data that covers the main points can be preferentially collected. Furthermore, when the user is excited, meal data with visually stimulating effects can be preferentially collected. This enables efficient data collection by determining the priority order of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting meal data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting meal data. Specific methods of acquiring and using geographical location information include, but are not limited to, GPS data, Wi-Fi location information, and accuracy of location information. The collection unit uses AI to preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is in a specific area, meal data of that area is preferentially collected. In addition, when the user is traveling, meal data of the travel destination can also be preferentially collected. Furthermore, based on the geographical location information of the user, highly relevant meal data can also be preferentially collected. As a result, highly relevant data can be efficiently collected by considering the geographical location information.

[0101] The collection unit can analyze the user's social media activity and collect related data when collecting meal data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting meal data. Specific analysis methods and criteria for social media activity include, but are not limited to, for example, the content of posts, the number of likes, and the number of comments. The collection unit uses AI to analyze the user's social media activity and collect related data. For example, the collection unit collects related data based on the meal content shared by the user on social media. In addition, the collection unit can also collect related data based on the meal content of accounts that the user follows on social media. Furthermore, the collection unit can analyze the user's social media activity and prioritize collection of highly relevant meal data. This allows efficient data collection by collecting highly relevant data based on social media activity.

[0102] The providing unit can estimate the user's emotion and set a priority order for the data to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority order for the data to be provided based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The providing unit uses AI to estimate the user's emotion and determines the priority order for the data to be provided based on the emotion. For example, when the user is relaxed, detailed data is provided preferentially. In addition, when the user is in a hurry, concise data that covers the main points can also be provided preferentially. Furthermore, when the user is excited, data with visually stimulating effects can also be provided preferentially. This enables efficient data provision by determining the priority order of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The providing unit can adjust the level of detail of the provision based on the importance of the meal data at the time of provision. For example, the providing unit adjusts the level of detail of the provision based on the importance of the meal data at the time of provision. Specific methods and standards for adjusting the level of detail of the provision include, for example, the level of detail of the data, the amount of information to be displayed, and the like, but are not limited to such examples. The providing unit adjusts the level of detail of the provision based on the importance of the meal data using AI. For example, detailed data is provided for main meals (breakfast, lunch, dinner). Also, concise data can be provided for snacks and light meals. Furthermore, if a specific nutrient is lacking, detailed data on the nutrient can be provided. As a result, by adjusting the level of detail of the provision based on the importance of the meal data, it is possible to appropriately provide information required by the user.

[0104] The providing unit can apply different providing algorithms depending on the category of the meal data at the time of providing. For example, the providing unit applies different providing algorithms depending on the category of the meal data at the time of providing. Specific types and implementation methods of the providing algorithm include, for example, rule-based, machine learning-based, and the like, but are not limited to such examples. The providing unit uses AI to apply different providing algorithms depending on the category of the meal data. For example, different providing algorithms are applied for each category such as staple food, main dish, side dish, and dessert. Also, different providing algorithms can be applied to high-calorie foods and low-calorie foods. Furthermore, different providing algorithms can be applied to foods rich in specific nutrients. This makes it possible to provide more accurate data by applying different providing algorithms depending on the category of the meal data.

[0105] The providing unit can estimate the user's emotion and change the display method of the data to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the data to be provided based on the estimated user's emotion. Specific methods and standards for estimating emotion include, for example, facial expression recognition, voice analysis, text analysis, etc., but are not limited to such examples. The providing unit uses AI to estimate the user's emotion and adjusts the display method based on the emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide data that is easy for the user to understand by adjusting the display method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The providing unit can determine the priority of provision based on the time of submission of the meal data at the time of provision. For example, the providing unit determines the priority of provision based on the time of submission of the meal data at the time of provision. Specific methods and criteria for determining the priority of provision include, for example, the time of submission, importance, relevance, etc., but are not limited to such examples. The providing unit uses AI to determine the priority of provision based on the time of submission of the meal data. For example, the most recent meal data is provided preferentially. Also, meal data submitted in a specific time period can be provided preferentially. Furthermore, meal data specified by the user can be provided preferentially. In this way, by determining the priority of provision based on the time of submission of the meal data, it is possible to quickly provide information that is important to the user.

[0107] The providing unit can adjust the order of provision based on the relevance of the dietary data at the time of provision. For example, the providing unit adjusts the order of provision based on the relevance of the dietary data at the time of provision. Specific adjustment methods and standards for the order of provision include, for example, a method of prioritizing highly relevant information, but are not limited to such examples. The providing unit adjusts the order of provision based on the relevance of the dietary data using AI. For example, dietary data of the same category is provided together. Dietary data related to a specific nutrient can also be provided together. Furthermore, dietary data related to the user's health goal can be provided preferentially. As a result, by adjusting the order of provision based on the relevance of the dietary data, information highly relevant to the user can be provided preferentially.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit may have a voice input function when inputting the user's meal contents. For example, by inputting the meal contents by voice, the user can save time and quickly collect data. Specific methods and standards for voice input include, but are not limited to, voice recognition technology, noise canceling technology, and voice command settings. The reception unit uses AI to analyze the user's voice and store the meal contents in a database. This allows the user to easily input the meal contents and the system to collect accurate meal data. For example, when the user says "I had toast and coffee for breakfast" into the smartphone, the reception unit analyzes the contents and stores them in a database. In addition, voice input allows the user to input the meal contents without using their hands, improving convenience.

[0110] The analysis unit can have a function of evaluating the freshness of ingredients included in a photo of a meal when analyzing the user's meal contents. For example, by analyzing the color and shape of ingredients and evaluating their freshness, the user can be advised to use fresh ingredients. Specific methods and standards for evaluating freshness include, but are not limited to, changes in color, shape, and texture. The analysis unit uses AI to evaluate the freshness of ingredients from photos of meals and stores the results in a database. This allows the user to continue eating healthy meals by using fresh ingredients. For example, the analysis unit evaluates whether the color of tomatoes is vivid, and advises the user that "fresh tomatoes are used" if the freshness is high. Also, if the freshness is low, the analysis unit can advise the user that "the freshness of the tomatoes has decreased. Choose fresh ones."

[0111] When providing the dietary data of the user, the providing unit can provide advice taking into consideration the absorption rate of nutrients in the diet. For example, if the absorption rate of a specific nutrient is improved by ingesting it together with other nutrients, the combination can be suggested. Specific evaluation criteria and methods for the absorption rate of nutrients include, but are not limited to, a combination of vitamin C and iron, a combination of calcium and vitamin D, and the like. The providing unit uses AI to analyze the dietary data of the user and provide advice taking into consideration the absorption rate of nutrients. This allows the user to efficiently ingest nutrients and continue a healthy diet. For example, the providing unit suggests that when the user ingests foodstuffs that are rich in iron, the user ingests foodstuffs that are rich in vitamin C at the same time. It can also suggest that when ingesting foodstuffs that are rich in calcium, the user ingests foodstuffs that are rich in vitamin D at the same time.

[0112] When providing the user's dietary data, the provision unit can provide advice that takes into account the environmental factors of the meal. For example, the provision unit can suggest optimal dietary content by considering the place and time of eating the meal, the activity during the meal, etc. Specific evaluation criteria and methods for environmental factors include, for example, the brightness of the place where the meal is eaten, the presence or absence of music during the meal, the time of eating the meal, etc., but are not limited to such examples. The provision unit uses AI to analyze the user's dietary data and environmental factors and suggests optimal dietary content. This allows the user to eat an optimal meal according to the environment and continue to eat a healthy diet. For example, when the user eats a meal in a quiet place, the provision unit can suggest ingredients that have a relaxing effect. In addition, when the user eats a meal during a busy time, the provision unit can also suggest ingredients that are easily ingested and have a good nutritional balance.

[0113] When providing the user's dietary data, the provision unit can provide advice that takes into account the cultural background of the meal. For example, traditional ingredients and dishes can be suggested based on the user's place of origin and culture. Specific evaluation criteria and methods for the cultural background include, but are not limited to, the user's place of origin, religious dietary restrictions, traditional dishes, etc. The provision unit uses AI to analyze the user's dietary data and cultural background and suggests optimal meal content. This allows the user to eat an optimal meal according to the culture and continue to eat a healthy diet. For example, if the user is from Japan, the provision unit can suggest traditional ingredients and dishes of Japanese cuisine. Also, if the user is a vegetarian, the provision unit can suggest dishes centered on plant-based ingredients.

[0114] The reception unit can estimate the user's emotion and change the input method of the meal contents based on the estimated user's emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to allow the user to input the meal contents quickly. In this way, the input method can be adjusted according to the user's emotion, thereby reducing the user's stress and allowing the input work to be performed smoothly. The estimation of emotion is realized, for example, using facial expression recognition, voice analysis, text analysis, etc. As a result, an optimal input method according to the user's emotion can be provided.

[0115] The analysis unit can estimate the user's emotion and change the way the analysis is expressed based on the estimated user's emotion. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result with the main points can be provided. If the user is excited, an analysis result with a visually stimulating effect can be provided. This makes it possible to provide an analysis result that is easy for the user to understand by adjusting the way the analysis is expressed according to the user's emotion. The emotion estimation is realized using, for example, facial expression recognition, voice analysis, text analysis, etc. This makes it possible to provide an optimal analysis result according to the user's emotion.

[0116] The advice unit can estimate the user's emotion and change the way of expressing advice based on the estimated user's emotion. For example, when the user is relaxed, detailed advice can be provided. Also, when the user is in a hurry, concise advice that focuses on the main points can be provided. Furthermore, when the user is excited, advice with visually stimulating effects can be provided. In this way, by adjusting the way of expressing advice according to the user's emotion, it is possible to provide advice that is easy for the user to understand. The emotion estimation is realized using, for example, facial expression recognition, voice analysis, text analysis, etc. In this way, it is possible to provide optimal advice according to the user's emotion.

[0117] The collection unit can estimate the user's emotions and change the timing of collecting meal data based on the estimated user's emotions. For example, when the user is relaxed, meal data can be collected periodically. Also, when the user is in a hurry, meal data can be collected quickly. Furthermore, when the user is excited, a collection method that adds a visually stimulating effect can be provided. This allows efficient data collection by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using, for example, facial expression recognition, voice analysis, text analysis, etc. This makes it possible to provide the optimal collection timing according to the user's emotions.

[0118] The providing unit can estimate the user's emotion and set the priority order of data to be provided based on the estimated user's emotion. For example, when the user is relaxed, detailed data can be provided preferentially. Also, when the user is in a hurry, concise data with the main points can be provided preferentially. Furthermore, when the user is excited, data with visually stimulating effects can be provided preferentially. This enables efficient data provision by determining the priority order of data according to the user's emotion. The emotion estimation is realized, for example, using facial expression recognition, voice analysis, text analysis, etc. This allows optimal data to be provided according to the user's emotion.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit receives the user's profile information and past meal contents. The user's profile information includes, for example, age, sex, height, weight, activity level, etc. The past meal contents include, for example, type of food, intake amount, intake time, etc. The reception unit saves the profile information and past meal contents input by the user in a database. Step 2: The analysis unit analyzes the information received by the reception unit and provides dietary advice based on the estimated daily energy requirement. The analysis unit calculates calories and evaluates nutrient balance, and uses AI to provide specific suggestions on calories and nutrients to be consumed in the next meal based on the user's profile information and past meal content. Step 3: The advice unit advises the user on meal content based on the analysis results obtained by the analysis unit. The advice unit proposes recommended meal menus and adjustments to intake amounts, and provides specific dietary advice to the user using AI. Step 4: The collection unit collects multiple meal data. The collection unit stores the meal details entered by the user in a database and efficiently collects the user's meal data using AI. Step 5: The provision unit provides the data collected by the collection unit to retailers and restaurants. The provision unit predicts demand for specific ingredients and analyzes the popularity of menu items, analyzes the collected data using AI, and provides the data to retailers and restaurants.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, collection unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives the user's profile information and past meal contents. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and advises the meal contents. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and advises the meal contents based on the analysis results. The collection unit is realized, for example, by the control unit 46A of the smart device 14, and collects multiple meal data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the collected data to retail stores and restaurants. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, collection 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 reception unit is realized by the control unit 46A of the smart glasses 214, and receives the user's profile information and past meal contents. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and advises the meal contents. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and advises the meal contents based on the analysis results. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214, and collects multiple meal data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the collected data to retail stores and restaurants. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, collection unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives the user's profile information and past meal contents. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and advises the meal contents. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and advises the meal contents based on the analysis results. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and collects multiple meal data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the collected data to retail stores and restaurants. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0173] Each of the multiple elements including the above-mentioned reception unit, analysis unit, advice unit, collection 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 reception unit is realized by the control unit 46A of the robot 414, and receives the user's profile information and past meal contents. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information and provides advice on meal contents. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides advice on meal contents based on the analysis results. The collection unit is realized, for example, by the control unit 46A of the robot 414, and collects multiple meal data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the collected data to retail stores and restaurants. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A reception unit that receives user profile information and past meal contents; an analysis unit that analyzes the information received by the reception unit and provides advice on meal contents based on an estimated daily energy requirement; an advice unit that gives advice on meal contents based on the analysis result obtained by the analysis unit; A collection unit that collects a plurality of meal data; A providing unit that provides the data collected by the collecting unit to retail stores and restaurants. A system characterized by: (Appendix 2) The reception unit is Equipped with a function that allows users to input meal details using photos 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing food content using image recognition technology 2. The system of claim 1, (Appendix 4) The providing unit is Equipped with a forecasting unit that forecasts demand for specific ingredients 2. The system of claim 1, (Appendix 5) The providing unit is Equipped with an analysis section that analyzes the popularity of menu items 2. The system of claim 1, (Appendix 6) The reception unit is Inferring a user's emotions and changing the way profile information is entered based on the estimated user's emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 8) The reception unit is Simplify profile entry by automatically capturing the user's current location when filling out profile information 2. The system of claim 1, (Appendix 9) The reception unit is Estimating user emotions and changing the design of the input interface based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The reception unit is When entering profile information, the system automatically suggests activity levels based on the user's past movement history. 2. The system of claim 1, (Appendix 11) The reception unit is When filling out profile information, the app will look at your calendar information to automatically suggest your activity level 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and change the way analytics is presented based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the level of detail of the analysis is adjusted based on the importance of the meal contents. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the food category. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and change the length of the parsing based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, analysis priorities will be determined based on when meal details were submitted. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the order of analysis is adjusted based on the relevance of dietary content 2. The system of claim 1, (Appendix 18) The advice unit, Infer the user's emotions and change the way advice is presented based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The advice unit, When giving advice, adjust the level of detail of the advice based on the importance of the dietary content 2. The system of claim 1, (Appendix 20) The advice unit, When providing advice, different advice algorithms are applied depending on the food category. 2. The system of claim 1, (Appendix 21) The advice unit, Estimate the user's emotions and change the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 22) The advice unit, When providing advice, the priority of advice is determined based on the timing of the submission of dietary information. 2. The system of claim 1, (Appendix 23) The advice unit, When giving advice, adjust the order of advice based on the relevance of dietary content 2. The system of claim 1, (Appendix 24) The collecting unit includes: The user's emotions are estimated, and the timing of collection of meal data is changed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The collecting unit includes: When collecting food data, analyze the user's past food history and select the appropriate collection method. 2. The system of claim 1, (Appendix 26) The collecting unit includes: As food data is collected, it is filtered based on the user's current health status and goals. 2. The system of claim 1, (Appendix 27) The collecting unit includes: Estimate the user's emotions and prioritize the food data to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 28) The collecting unit includes: When collecting meal data, the system takes into account the user's geographic location information to prioritize collection of relevant data. 2. The system of claim 1, (Appendix 29) The collecting unit includes: When collecting meal data, the company analyzes users' social media activity to collect related data. 2. The system of claim 1, (Appendix 30) The providing unit is Infer user sentiment and prioritize data to be provided based on the inferred user sentiment 2. The system of claim 1, (Appendix 31) The providing unit is At the time of delivery, adjust the detail of the delivery based on the importance of the meal data 2. The system of claim 1, (Appendix 32) The providing unit is At the time of serving, different serving algorithms are applied depending on the category of meal data. 2. The system of claim 1, (Appendix 33) The providing unit is Infer user sentiment and change the way data is presented based on the inferred sentiment 2. The system of claim 1, (Appendix 34) The providing unit is At the time of delivery, meal delivery will be prioritized based on the timing of meal data submission. 2. The system of claim 1, (Appendix 35) The providing unit is At the time of serving, adjust the serving order based on the relevance of meal data 2. The system of claim 1, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives user profile information and past meal contents; an analysis unit that analyzes the information received by the reception unit and provides advice on meal contents based on an estimated daily energy requirement; an advice unit that gives advice on meal contents based on the analysis result obtained by the analysis unit; A collection unit that collects a plurality of meal data; A providing unit that provides the data collected by the collecting unit to retail stores and restaurants. A system characterized by:

2. The reception unit is Equipped with a function that allows users to input meal details using photos 2. The system of claim 1 .

3. The analysis unit is Analyzing food content using image recognition technology 2. The system of claim 1 .

4. The providing unit is Equipped with a forecasting unit that forecasts demand for specific ingredients 2. The system of claim 1 .

5. The providing unit is Equipped with an analysis section that analyzes the popularity of menu items 2. The system of claim 1 .

6. The reception unit is Inferring a user's emotions and changing the way profile information is entered based on the estimated user's emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1 .

8. The reception unit is Simplify profile entry by automatically capturing the user's current location when filling out profile information 2. The system of claim 1 .

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