System

Abstract

An object of a system according to an embodiment is to provide an optimal diet plan for an individual user.SOLUTION: A system includes a data collection part, an analysis part, and a provision part. The data collection unit collects physical data, lifestyle habits, and dietary preferences of a user. The analysis unit generates a diet plan optimal for the user based on the data collected by the data collection unit. The provision unit provides the diet plan generated by the analysis unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide an optimal diet plan for each individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal diet plan for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects a user's physical data, lifestyle habits, and dietary preferences. The analysis unit generates an optimal diet plan for the user based on the data collected by the data collection unit. The provision unit provides the user with the diet plan generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal diet plan for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The diet advice system according to the embodiment of the present invention is a system in which a generation AI analyzes user data and provides an optimal diet plan, thereby enabling the diet advice system to provide a personalized diet plan to the user.

[0029] A diet advice system according to an embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects a user's physical data, lifestyle habits, and dietary preferences. For example, the data collection unit collects physical data such as height, weight, age, and gender based on prompts input by the user. The data collection unit can also collect lifestyle data such as the user's exercise frequency, sleep time, and stress level. The data collection unit also collects the user's dietary preferences, such as favorite foods, disliked foods, and allergies. For example, the data collection unit converts the data input by the user into a format that is easy for the generation AI to analyze. The analysis unit generates an optimal diet plan for the user based on the data collected by the data collection unit. For example, the analysis unit allows the generation AI to propose a meal plan that takes into account calorie intake and nutritional balance in accordance with the user's goals. The analysis unit also allows the generation AI to provide advice on the frequency and type of exercise for the user. For example, the generation AI generates an optimal exercise plan based on the user's data. The provision unit provides the user with the diet plan generated by the analysis unit. For example, the provision unit specifically indicates a daily meal menu and exercise plan. The providing unit can also provide a feedback function that allows the user to record progress according to the diet plan. For example, the providing unit collects the user's weight change, meal records, and exercise status, and the generation AI adjusts the diet plan as appropriate. This allows the diet advice system according to the embodiment to provide the user with a personalized diet plan. For example, the user can adjust their diet and exercise according to the advice of the generation AI and maintain a healthy weight. Furthermore, a user who wants to consume specific nutrients can eat a balanced diet based on the suggestions of the generation AI.

[0030] The data collection unit analyzes photos of meals taken by the user and automatically recognizes ingredients and nutrients. The data collection unit, for example, analyzes photos of meals taken by the user and builds a system that automatically recognizes ingredients and nutrients. For example, it uses image recognition technology to identify the types and amounts of ingredients. The data collection unit also analyzes photos of meals and automatically calculates calorie and nutrient information. For example, it compares the types and amounts of ingredients with a calorie and nutrient database. The data collection unit also uploads photos of meals taken by the user to the cloud and analyzes them on the server side. For example, it uses a highly accurate image recognition algorithm to identify ingredients and provide nutritional information. This makes it easier to record meals by analyzing photos of meals taken by the user and automatically recognizing ingredients and nutrients.

[0031] The data collection unit automatically collects a user's lifestyle data from a wearable device such as a smartwatch or fitness tracker. The data collection unit builds a system that automatically collects a user's heart rate, step count, and sleep data from, for example, a smartwatch or fitness tracker. For example, it connects to the device using Bluetooth or Wi-Fi. The data collection unit also stores the data collected from the wearable device in the cloud, and the generation AI analyzes the data. For example, it adjusts a diet plan based on daily exercise volume and sleep patterns. The data collection unit also monitors the user's lifestyle data in real time and sends an alert if an abnormality is detected. For example, it notifies users if they have continued to exercise less or sleep less. This makes data collection more efficient by automatically collecting the user's lifestyle data from the wearable device.

[0032] The data collection unit uses voice input to enable the user to easily record their meals and exercise. For example, the data collection unit builds a system that allows the user to input details of meals and exercise by voice. For example, it uses voice recognition technology to convert what the user says into text data. The data collection unit also develops an app that uses voice input to enable the user to easily record the details of meals and types of exercise. For example, simply saying "I ate bread and eggs for breakfast" completes the recording. The data collection unit also provides a function that allows the user to easily record the start and end of exercise using voice input. For example, simply saying "I'm going to start jogging" starts the exercise recording. This reduces the burden on the user by making it easy to record meals and exercise using voice input.

[0033] The data collection unit collects data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions. The data collection unit, for example, builds a database on ingredients and eating habits from different cultural spheres or regions and uses it to collect user data. For example, it collects information on regional ingredients and dishes. The data collection unit also analyzes regional eating habits and ingredient usage and reflects this in user data collection. For example, it registers ingredients and dishes that are commonly eaten in a particular region in the database. The data collection unit also collects data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions, and the generation AI generates diet plans based on that data. For example, it provides plans that take into account the food culture of each region. This allows for the provision of more personalized diet plans by collecting data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions.

[0034] The analysis unit analyzes the user's genetic information and generates a diet plan that takes genetic factors into consideration. The analysis unit, for example, analyzes the user's genetic information and builds a system that generates a diet plan that takes genetic factors into consideration. For example, the analysis unit suggests specific dietary restrictions to a user who is genetically prone to obesity. The analysis unit also generates a meal plan that takes into consideration the optimal nutritional balance for the user based on the genetic information. For example, for a user who is genetically prone to a deficiency in a specific nutrient, the analysis unit suggests a meal that supplements that nutrient. The analysis unit also analyzes the user's genetic information and generates a plan to maximize the effectiveness of exercise. For example, for a user who is genetically prone to building muscle, the analysis unit suggests effective strength training. In this way, by analyzing the user's genetic information and generating a diet plan that takes genetic factors into consideration, a more personalized plan can be provided.

[0035] The analysis unit analyzes the user's past diet history and optimizes the plan by taking into account factors that led to success or failure. The analysis unit, for example, builds a system that analyzes the user's past diet history and identifies factors that led to success or failure. For example, it analyzes past food records and exercise history. The analysis unit also generates a plan that strengthens factors that led to success and avoids factors that led to failure based on the past diet history. For example, it re-suggests meals and exercises that were effective in the past. The analysis unit also analyzes the user's past diet history and provides personalized advice. For example, it avoids diet methods that failed in the past and optimizes the plan based on methods that were successful. In this way, a more effective diet plan can be provided by analyzing the user's past diet history and optimizing the plan by taking into account factors that led to success or failure.

[0036] The analysis unit suggests meals and exercises according to the season and weather based on the user data. For example, the analysis unit builds a system that suggests meal plans according to the season and weather based on the user data. For example, in summer, it suggests a plan that emphasizes cold meals and hydration. The analysis unit also suggests an optimal exercise plan for the user based on weather data. For example, it suggests exercises that can be done indoors on rainy days and outdoor exercises on sunny days. The analysis unit also generates an optimal meal plan for the user, taking into account seasonal nutritional needs. For example, it suggests foods that are high in vitamin D in winter. This makes it possible to provide a more effective diet plan by suggesting meals and exercises according to the season and weather based on the user data.

[0037] The analysis unit generates a group diet plan that can be done together with family or friends based on the user data. The analysis unit, for example, builds a system that generates a group diet plan that can be done together with family and friends based on the user data. For example, it suggests joint exercise and meal plans. The analysis unit also supports the user in achieving goals together with family and friends through the group diet plan. For example, it sets group challenges and joint goals. The analysis unit also suggests meal plans that can be done together with family and friends based on the user data. For example, it provides healthy recipes that the whole family can enjoy. In this way, motivation can be increased by generating a group diet plan that can be done together with family and friends based on the user data.

[0038] The providing unit provides recipe videos and exercise videos tailored to the user's preferences. The providing unit, for example, builds a system that provides recipe videos tailored to the user's preferences. For example, it suggests videos of cooking using the user's favorite ingredients. The providing unit also provides exercise videos tailored to the user's exercise preferences. For example, it suggests videos of exercises that interest the user, such as yoga or Pilates. The providing unit also customizes the recipe videos and exercise videos according to the user's preferences. For example, it adjusts the content of the videos based on user feedback. In this way, by providing recipe videos and exercise videos tailored to the user's preferences, user satisfaction can be increased.

[0039] The providing unit visualizes the progress of the diet plan, allowing the user to intuitively understand how far the user is progressing toward the goal. The providing unit, for example, builds a system that visualizes the progress of the diet plan. For example, it displays the user's progress using graphs and charts. The providing unit also visualizes the progress so that the user can intuitively understand how far the user is progressing toward the goal. For example, it displays the remaining steps until the goal is achieved. The providing unit also updates the progress of the diet plan in real time, allowing the user to always keep up to date with the latest situation. For example, it displays weight changes and exercise implementation status in real time. In this way, the progress of the diet plan is visualized, allowing the user to intuitively understand how far the user is progressing toward the goal, thereby maintaining motivation.

[0040] The providing unit provides a function to automatically add ingredients selected by a user to an online shopping cart. The providing unit, for example, builds a system that automatically adds ingredients selected by a user to an online shopping cart. For example, when a user selects a recipe, the ingredients are automatically added to the cart. The providing unit also works in conjunction with an online shopping site to enable the user to easily purchase the ingredients selected by the user. For example, when a user selects a recipe, the corresponding ingredients are added to the cart. The providing unit also generates an optimal online shopping cart based on the ingredients selected by the user. For example, the providing unit selects ingredients based on the user's preferences and budget and adds them to the cart. This simplifies the purchasing of ingredients by providing a function to automatically add ingredients selected by a user to an online shopping cart.

[0041] The providing unit builds an online community in which users can participate and promotes interaction with other users. The providing unit, for example, develops a system for building an online community in which users can participate and promoting interaction with other users. For example, users can share their diet progress or exchange advice. The providing unit also enables users to encourage each other and maintain their motivation through the online community. For example, the providing unit provides group chats and forums. The providing unit also hosts online events and challenges in which users can participate and promote interaction with other users. For example, the providing unit hosts joint exercise challenges and meal plan sharing events. In this way, an online community in which users can participate and promotion of interaction with other users can help maintain motivation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit can also analyze photos of a user's meals and automatically recognize ingredients and nutrients. For example, a system can be built that analyzes photos of meals taken by a user and automatically recognizes ingredients and nutrients. For example, image recognition technology can be used to identify the types and amounts of ingredients. The data collection unit can also analyze photos of meals and automatically calculate calorie and nutrient information. For example, the type and amount of ingredients can be compared with a calorie and nutrient database. The data collection unit can also upload photos of meals taken by a user to the cloud and perform analysis on the server side. For example, a highly accurate image recognition algorithm can be used to identify ingredients and provide nutritional information. This makes it easier to record meals by analyzing photos of a user's meals and automatically recognizing ingredients and nutrients.

[0044] The data collection unit can also automatically collect a user's lifestyle data from a wearable device such as a smartwatch or fitness tracker. For example, a system can be built that automatically collects a user's heart rate, step count, and sleep data from a smartwatch or fitness tracker. For example, it can connect to the device using Bluetooth or Wi-Fi. The data collection unit can also store the data collected from the wearable device in the cloud, and the generation AI can analyze the data. For example, it can adjust a diet plan based on daily exercise volume and sleep patterns. The data collection unit can also monitor the user's lifestyle data in real time and send an alert if an abnormality is detected. For example, it can notify the user if they have been lacking exercise or sleep for a long time. This makes data collection more efficient by automatically collecting a user's lifestyle data from a wearable device.

[0045] The data collection unit can also use voice input to make it easy for users to record their meals and exercise. For example, a system can be built that allows users to input details of meals and exercise by voice. For example, speech recognition technology can be used to convert what the user says into text data. The data collection unit can also develop an app that uses voice input to allow users to easily record the details of meals and types of exercise. For example, simply saying "I ate bread and eggs for breakfast" completes the recording. The data collection unit can also provide a function that allows users to easily record the start and end of exercise using voice input. For example, simply saying "I'm going to start jogging" starts the exercise recording. This reduces the burden on users by making it easy to record meals and exercise using voice input.

[0046] The data collection unit can also collect data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions. For example, a database on ingredients and eating habits from different cultural spheres or regions can be built and used to collect user data. For example, information on regional ingredients and dishes can be collected. The data collection unit can also analyze regional eating habits and ingredient usage and reflect this in user data collection. For example, ingredients and dishes commonly eaten in a particular region can be registered in the database. The data collection unit can also collect data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions, and the generation AI can generate diet plans based on that data. For example, a plan that takes into account the food culture of each region can be provided. This allows for the provision of more personalized diet plans by collecting data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions.

[0047] The analysis unit can also analyze the user's genetic information and generate a diet plan that takes genetic factors into consideration. For example, a system can be constructed that analyzes the user's genetic information and generates a diet plan that takes genetic factors into consideration. For example, a specific dietary restriction can be proposed to a user who is genetically prone to obesity. The analysis unit can also generate a meal plan that takes into consideration the optimal nutritional balance for the user based on the genetic information. For example, a meal that supplements a specific nutrient can be proposed to a user who is genetically prone to deficiency in that nutrient. The analysis unit can also analyze the user's genetic information and generate a plan to maximize the effectiveness of exercise. For example, effective strength training can be proposed to a user who is genetically predisposed to having difficulty building muscle. In this way, a more personalized plan can be provided by analyzing the user's genetic information and generating a diet plan that takes genetic factors into consideration.

[0048] The analysis unit can also analyze the user's past diet history and optimize the plan by taking into account factors that led to success or failure. For example, a system can be constructed that analyzes the user's past diet history and identifies factors that led to success or failure. For example, past food records and exercise history can be analyzed. The analysis unit can also generate a plan that strengthens factors that led to success and avoids factors that led to failure based on the past diet history. For example, it can re-suggest meals and exercises that were effective in the past. The analysis unit can also analyze the user's past diet history and provide personalized advice. For example, it can avoid diet methods that failed in the past and optimize the plan based on methods that were successful. In this way, a more effective diet plan can be provided by analyzing the user's past diet history and optimizing the plan by taking into account factors that led to success or failure.

[0049] The analysis unit can also suggest meals and exercises according to the season and weather based on the user data. For example, a system can be built that suggests meal plans according to the season and weather based on the user data. For example, in the summer, a plan that emphasizes cold meals and hydration is suggested. The analysis unit can also suggest an optimal exercise plan for the user based on weather data. For example, indoor exercises are suggested on rainy days, and outdoor exercises are suggested on sunny days. The analysis unit can also generate an optimal meal plan for the user, taking into account seasonal nutritional needs. For example, foods high in vitamin D are suggested in winter. This makes it possible to provide a more effective diet plan by suggesting meals and exercises according to the season and weather based on the user data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit collects the user's physical data, lifestyle habits, and dietary preferences. For example, the data collection unit collects physical data such as height, weight, age, and gender based on prompts entered by the user. The data collection unit can also collect lifestyle data such as the user's exercise frequency, sleep duration, and stress level. Furthermore, the data collection unit collects the user's dietary preferences, such as favorite foods, disliked foods, and allergies. For example, the data collection unit converts the data entered by the user into a format that is easy for the generation AI to analyze. Step 2: The analysis unit generates an optimal diet plan for the user based on the data collected by the data collection unit. For example, the analysis unit allows the generation AI to propose a meal plan that takes into account calorie intake and nutritional balance in accordance with the user's goals. The analysis unit also allows the generation AI to provide advice on the frequency and type of exercise the user should do. For example, the generation AI generates an optimal exercise plan based on the user's data. Step 3: The providing unit provides the user with the diet plan generated by the analysis unit. For example, the providing unit may specifically indicate a daily meal menu and exercise plan. The providing unit may also provide a feedback function that allows the user to record progress according to the diet plan. For example, the providing unit may collect the user's weight change, meal records, and exercise status, and the generation AI may adjust the diet plan as appropriate.

[0052] (Example 2) The diet advice system according to the embodiment of the present invention is a system in which a generation AI analyzes user data and provides an optimal diet plan, thereby enabling the diet advice system to provide a personalized diet plan to the user.

[0053] A diet advice system according to an embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit collects a user's physical data, lifestyle habits, and dietary preferences. For example, the data collection unit collects physical data such as height, weight, age, and gender based on prompts input by the user. The data collection unit can also collect lifestyle data such as the user's exercise frequency, sleep time, and stress level. The data collection unit also collects the user's dietary preferences, such as favorite foods, disliked foods, and allergies. For example, the data collection unit converts the data input by the user into a format that is easy for the generation AI to analyze. The analysis unit generates an optimal diet plan for the user based on the data collected by the data collection unit. For example, the analysis unit allows the generation AI to propose a meal plan that takes into account calorie intake and nutritional balance in accordance with the user's goals. The analysis unit also allows the generation AI to provide advice on the frequency and type of exercise for the user. For example, the generation AI generates an optimal exercise plan based on the user's data. The provision unit provides the user with the diet plan generated by the analysis unit. For example, the provision unit specifically indicates a daily meal menu and exercise plan. The providing unit can also provide a feedback function that allows the user to record progress according to the diet plan. For example, the providing unit collects the user's weight change, meal records, and exercise status, and the generation AI adjusts the diet plan as appropriate. This allows the diet advice system according to the embodiment to provide the user with a personalized diet plan. For example, the user can adjust their diet and exercise according to the advice of the generation AI and maintain a healthy weight. Furthermore, a user who wants to consume specific nutrients can eat a balanced diet based on the suggestions of the generation AI.

[0054] The data collection unit estimates the user's emotional state in real time and adjusts the timing and method of data collection based on the emotional state. The data collection unit, for example, analyzes the user's facial expressions and voice tone to estimate the emotional state in real time. For example, it detects the user's emotions using a camera or microphone and refrains from collecting data when the user is under high stress. The data collection unit also collects data during times when the user is relaxed based on the emotion estimation data. For example, it sends a notification encouraging the user to record their meals during relaxation time at night. The data collection unit also adjusts the method of data collection according to the user's emotional state. For example, if the user is tired, it collects data in the form of simple questions. This allows for more accurate data collection by adjusting the timing and method of data collection according to the user's emotional state.

[0055] The data collection unit analyzes photos of meals taken by the user and automatically recognizes ingredients and nutrients. The data collection unit, for example, analyzes photos of meals taken by the user and builds a system that automatically recognizes ingredients and nutrients. For example, it uses image recognition technology to identify the types and amounts of ingredients. The data collection unit also analyzes photos of meals and automatically calculates calorie and nutrient information. For example, it compares the types and amounts of ingredients with a calorie and nutrient database. The data collection unit also uploads photos of meals taken by the user to the cloud and analyzes them on the server side. For example, it uses a highly accurate image recognition algorithm to identify ingredients and provide nutritional information. This makes it easier to record meals by analyzing photos of meals taken by the user and automatically recognizing ingredients and nutrients.

[0056] The data collection unit automatically collects a user's lifestyle data from a wearable device such as a smartwatch or fitness tracker. The data collection unit builds a system that automatically collects a user's heart rate, step count, and sleep data from, for example, a smartwatch or fitness tracker. For example, it connects to the device using Bluetooth or Wi-Fi. The data collection unit also stores the data collected from the wearable device in the cloud, and the generation AI analyzes the data. For example, it adjusts a diet plan based on daily exercise volume and sleep patterns. The data collection unit also monitors the user's lifestyle data in real time and sends an alert if an abnormality is detected. For example, it notifies users if they have continued to exercise less or sleep less. This makes data collection more efficient by automatically collecting the user's lifestyle data from the wearable device.

[0057] The data collection unit uses voice input to enable the user to easily record their meals and exercise. For example, the data collection unit builds a system that allows the user to input details of meals and exercise by voice. For example, it uses voice recognition technology to convert what the user says into text data. The data collection unit also develops an app that uses voice input to enable the user to easily record the details of meals and types of exercise. For example, simply saying "I ate bread and eggs for breakfast" completes the recording. The data collection unit also provides a function that allows the user to easily record the start and end of exercise using voice input. For example, simply saying "I'm going to start jogging" starts the exercise recording. This reduces the burden on the user by making it easy to record meals and exercise using voice input.

[0058] The data collection unit collects data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions. The data collection unit, for example, builds a database on ingredients and eating habits from different cultural spheres or regions and uses it to collect user data. For example, it collects information on regional ingredients and dishes. The data collection unit also analyzes regional eating habits and ingredient usage and reflects this in user data collection. For example, it registers ingredients and dishes that are commonly eaten in a particular region in the database. The data collection unit also collects data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions, and the generation AI generates diet plans based on that data. For example, it provides plans that take into account the food culture of each region. This allows for the provision of more personalized diet plans by collecting data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions.

[0059] The data collection unit uses the emotion estimation function to analyze the emotions of a user when entering data and provides positive feedback to increase the motivation to enter data. The data collection unit, for example, uses the emotion estimation function to analyze emotions when a user enters data and builds a system that provides positive feedback. For example, it analyzes facial expressions and tone of voice when entering data. The data collection unit also provides positive feedback to the user when entering data based on the emotion estimation data. For example, it displays an encouraging message such as "Great record!" The data collection unit also provides feedback to increase the motivation to enter data depending on the user's emotional state. For example, if negative emotions are detected, it displays an encouraging message or advice. In this way, the emotion estimation function can be used to analyze emotions when entering data and provide positive feedback to increase the motivation to enter data.

[0060] The analysis unit analyzes the user's emotional data and suggests meals and exercises according to the emotions. The analysis unit, for example, builds a system that analyzes the user's emotional data and suggests meals and exercises according to the emotions. For example, when stress is high, meals and exercises that have a relaxing effect are suggested. The analysis unit also generates a meal plan based on the emotional data to match the user's mood. For example, when positive emotions are strong, meals that increase energy are suggested. The analysis unit also adjusts the type and intensity of exercise according to the user's emotional state. For example, when the user is tired, light stretching or yoga is suggested, and when the user is energetic, high-intensity exercise is suggested. In this way, by analyzing the user's emotional data and suggesting meals and exercises according to the emotions, a more effective diet plan can be provided.

[0061] The analysis unit analyzes the user's genetic information and generates a diet plan that takes genetic factors into consideration. The analysis unit, for example, analyzes the user's genetic information and builds a system that generates a diet plan that takes genetic factors into consideration. For example, the analysis unit suggests specific dietary restrictions to a user who is genetically prone to obesity. The analysis unit also generates a meal plan that takes into consideration the optimal nutritional balance for the user based on the genetic information. For example, for a user who is genetically prone to a deficiency in a specific nutrient, the analysis unit suggests a meal that supplements that nutrient. The analysis unit also analyzes the user's genetic information and generates a plan to maximize the effectiveness of exercise. For example, for a user who is genetically prone to building muscle, the analysis unit suggests effective strength training. In this way, by analyzing the user's genetic information and generating a diet plan that takes genetic factors into consideration, a more personalized plan can be provided.

[0062] The analysis unit analyzes the user's past diet history and optimizes the plan by taking into account factors that led to success or failure. The analysis unit, for example, builds a system that analyzes the user's past diet history and identifies factors that led to success or failure. For example, it analyzes past food records and exercise history. The analysis unit also generates a plan that strengthens factors that led to success and avoids factors that led to failure based on the past diet history. For example, it re-suggests meals and exercises that were effective in the past. The analysis unit also analyzes the user's past diet history and provides personalized advice. For example, it avoids diet methods that failed in the past and optimizes the plan based on methods that were successful. In this way, a more effective diet plan can be provided by analyzing the user's past diet history and optimizing the plan by taking into account factors that led to success or failure.

[0063] The analysis unit suggests meals and exercises according to the season and weather based on the user data. For example, the analysis unit builds a system that suggests meal plans according to the season and weather based on the user data. For example, in summer, it suggests a plan that emphasizes cold meals and hydration. The analysis unit also suggests an optimal exercise plan for the user based on weather data. For example, it suggests exercises that can be done indoors on rainy days and outdoor exercises on sunny days. The analysis unit also generates an optimal meal plan for the user, taking into account seasonal nutritional needs. For example, it suggests foods that are high in vitamin D in winter. This makes it possible to provide a more effective diet plan by suggesting meals and exercises according to the season and weather based on the user data.

[0064] The analysis unit generates a group diet plan that can be done together with family or friends based on the user data. The analysis unit, for example, builds a system that generates a group diet plan that can be done together with family and friends based on the user data. For example, it suggests joint exercise and meal plans. The analysis unit also supports the user in achieving goals together with family and friends through the group diet plan. For example, it sets group challenges and joint goals. The analysis unit also suggests meal plans that can be done together with family and friends based on the user data. For example, it provides healthy recipes that the whole family can enjoy. In this way, motivation can be increased by generating a group diet plan that can be done together with family and friends based on the user data.

[0065] The analysis unit uses the emotion estimation function to analyze how the user feels about the diet plan and adjusts the content of the plan. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes how the user feels about the diet plan. For example, if the user has strong positive emotions about the plan, the content of the plan is strengthened. The analysis unit also adjusts the content of the diet plan based on the user's emotion data. For example, if negative emotions are detected, the analysis unit lowers the difficulty of the plan. The analysis unit also monitors the user's emotions about the diet plan in real time based on the emotion estimation data and adjusts the plan as appropriate. For example, the analysis unit flexibly changes the plan in response to changes in the user's emotions. In this way, by analyzing the user's emotions using the emotion estimation function and adjusting the content of the plan, user satisfaction can be increased.

[0066] The providing unit monitors the user's emotional state in real time and provides encouragement or advice according to the emotion. The providing unit, for example, builds a system that monitors the user's emotional state in real time and provides encouragement or advice according to the emotion. For example, when the user is feeling down, it sends an encouraging message. The providing unit also provides advice to increase the user's motivation based on the emotion monitoring data. For example, when the user is feeling strongly positive, it suggests further challenges. The providing unit also adjusts the content of the diet plan according to the user's emotional state. For example, when stress is high, it suggests exercise that has a relaxing effect. In this way, the user's motivation can be maintained by monitoring the user's emotional state in real time and providing encouragement or advice according to the emotion.

[0067] The providing unit provides recipe videos and exercise videos tailored to the user's preferences. The providing unit, for example, builds a system that provides recipe videos tailored to the user's preferences. For example, it suggests videos of cooking using the user's favorite ingredients. The providing unit also provides exercise videos tailored to the user's exercise preferences. For example, it suggests videos of exercises that interest the user, such as yoga or Pilates. The providing unit also customizes the recipe videos and exercise videos according to the user's preferences. For example, it adjusts the content of the videos based on user feedback. In this way, by providing recipe videos and exercise videos tailored to the user's preferences, user satisfaction can be increased.

[0068] The providing unit visualizes the progress of the diet plan, allowing the user to intuitively understand how far the user is progressing toward the goal. The providing unit, for example, builds a system that visualizes the progress of the diet plan. For example, it displays the user's progress using graphs and charts. The providing unit also visualizes the progress so that the user can intuitively understand how far the user is progressing toward the goal. For example, it displays the remaining steps until the goal is achieved. The providing unit also updates the progress of the diet plan in real time, allowing the user to always keep up to date with the latest situation. For example, it displays weight changes and exercise implementation status in real time. In this way, the progress of the diet plan is visualized, allowing the user to intuitively understand how far the user is progressing toward the goal, thereby maintaining motivation.

[0069] The providing unit provides a function to automatically add ingredients selected by a user to an online shopping cart. The providing unit, for example, builds a system that automatically adds ingredients selected by a user to an online shopping cart. For example, when a user selects a recipe, the ingredients are automatically added to the cart. The providing unit also works in conjunction with an online shopping site to enable the user to easily purchase the ingredients selected by the user. For example, when a user selects a recipe, the corresponding ingredients are added to the cart. The providing unit also generates an optimal online shopping cart based on the ingredients selected by the user. For example, the providing unit selects ingredients based on the user's preferences and budget and adds them to the cart. This simplifies the purchasing of ingredients by providing a function to automatically add ingredients selected by a user to an online shopping cart.

[0070] The providing unit builds an online community in which users can participate and promotes interaction with other users. The providing unit, for example, develops a system for building an online community in which users can participate and promoting interaction with other users. For example, users can share their diet progress or exchange advice. The providing unit also enables users to encourage each other and maintain their motivation through the online community. For example, the providing unit provides group chats and forums. The providing unit also hosts online events and challenges in which users can participate and promote interaction with other users. For example, the providing unit hosts joint exercise challenges and meal plan sharing events. In this way, an online community in which users can participate and promotion of interaction with other users can help maintain motivation.

[0071] The providing unit uses the emotion estimation function to analyze how the user feels about the diet plan and adjusts the content of the plan. The providing unit, for example, uses the emotion estimation function to build a system that analyzes how the user feels about the diet plan. For example, if the user has strong positive emotions about the plan, the content of the plan is strengthened. The providing unit also adjusts the content of the diet plan based on the user's emotion data. For example, if negative emotions are detected, the difficulty of the plan is lowered. The providing unit also monitors the user's emotions about the diet plan in real time based on the emotion estimation data and adjusts the plan as appropriate. For example, the plan is flexibly changed in response to changes in the user's emotions. In this way, by using the emotion estimation function to analyze the user's emotions and adjusting the content of the plan, user satisfaction can be increased.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data collection unit can also analyze photos of a user's meals and automatically recognize ingredients and nutrients. For example, a system can be built that analyzes photos of meals taken by a user and automatically recognizes ingredients and nutrients. For example, image recognition technology can be used to identify the types and amounts of ingredients. The data collection unit can also analyze photos of meals and automatically calculate calorie and nutrient information. For example, the type and amount of ingredients can be compared with a calorie and nutrient database. The data collection unit can also upload photos of meals taken by a user to the cloud and perform analysis on the server side. For example, a highly accurate image recognition algorithm can be used to identify ingredients and provide nutritional information. This makes it easier to record meals by analyzing photos of a user's meals and automatically recognizing ingredients and nutrients.

[0074] The data collection unit can also automatically collect a user's lifestyle data from a wearable device such as a smartwatch or fitness tracker. For example, a system can be built that automatically collects a user's heart rate, step count, and sleep data from a smartwatch or fitness tracker. For example, it can connect to the device using Bluetooth or Wi-Fi. The data collection unit can also store the data collected from the wearable device in the cloud, and the generation AI can analyze the data. For example, it can adjust a diet plan based on daily exercise volume and sleep patterns. The data collection unit can also monitor the user's lifestyle data in real time and send an alert if an abnormality is detected. For example, it can notify the user if they have been lacking exercise or sleep for a long time. This makes data collection more efficient by automatically collecting a user's lifestyle data from a wearable device.

[0075] The data collection unit can also use voice input to make it easy for users to record their meals and exercise. For example, a system can be built that allows users to input details of meals and exercise by voice. For example, speech recognition technology can be used to convert what the user says into text data. The data collection unit can also develop an app that uses voice input to allow users to easily record the details of meals and types of exercise. For example, simply saying "I ate bread and eggs for breakfast" completes the recording. The data collection unit can also provide a function that allows users to easily record the start and end of exercise using voice input. For example, simply saying "I'm going to start jogging" starts the exercise recording. This reduces the burden on users by making it easy to record meals and exercise using voice input.

[0076] The data collection unit can also collect data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions. For example, a database on ingredients and eating habits from different cultural spheres or regions can be built and used to collect user data. For example, information on regional ingredients and dishes can be collected. The data collection unit can also analyze regional eating habits and ingredient usage and reflect this in user data collection. For example, ingredients and dishes commonly eaten in a particular region can be registered in the database. The data collection unit can also collect data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions, and the generation AI can generate diet plans based on that data. For example, a plan that takes into account the food culture of each region can be provided. This allows for the provision of more personalized diet plans by collecting data on regional ingredients and eating habits to accommodate users from different cultural spheres or regions.

[0077] The data collection unit can also use the emotion estimation function to analyze the emotions of a user when entering data and provide positive feedback to increase the motivation to enter data. For example, a system can be constructed in which the emotion estimation function is used to analyze the emotions of a user when entering data and provide positive feedback. For example, the emotion estimation function is used to analyze the user's facial expressions and tone of voice when entering data. The data collection unit can also provide positive feedback to the user when entering data based on the emotion estimation data. For example, an encouraging message such as "Great record!" is displayed. The data collection unit can also provide feedback to increase the motivation to enter data depending on the user's emotional state. For example, if a negative emotion is detected, an encouraging message or advice is displayed. In this way, the emotion estimation function can be used to analyze the user's emotions when entering data and provide positive feedback to increase the user's motivation to enter data.

[0078] The analysis unit can also analyze the user's emotional data and suggest diet and exercise according to the emotion. For example, a system can be constructed that analyzes the user's emotional data and suggests diet and exercise according to the emotion. For example, when stress is high, diet and exercise that have a relaxing effect can be suggested. The analysis unit can also generate a meal plan based on the emotional data to match the user's mood. For example, when positive emotions are strong, diet that increases energy can be suggested. The analysis unit can also adjust the type and intensity of exercise according to the user's emotional state. For example, when the user is tired, light stretching or yoga can be suggested, and when the user is energetic, high-intensity exercise can be suggested. In this way, by analyzing the user's emotional data and suggesting diet and exercise according to the emotion, a more effective diet plan can be provided.

[0079] The analysis unit can also analyze the user's genetic information and generate a diet plan that takes genetic factors into consideration. For example, a system can be constructed that analyzes the user's genetic information and generates a diet plan that takes genetic factors into consideration. For example, a specific dietary restriction can be proposed to a user who is genetically prone to obesity. The analysis unit can also generate a meal plan that takes into consideration the optimal nutritional balance for the user based on the genetic information. For example, a meal that supplements a specific nutrient can be proposed to a user who is genetically prone to deficiency in that nutrient. The analysis unit can also analyze the user's genetic information and generate a plan to maximize the effectiveness of exercise. For example, effective strength training can be proposed to a user who is genetically predisposed to having difficulty building muscle. In this way, a more personalized plan can be provided by analyzing the user's genetic information and generating a diet plan that takes genetic factors into consideration.

[0080] The analysis unit can also analyze the user's past diet history and optimize the plan by taking into account factors that led to success or failure. For example, a system can be constructed that analyzes the user's past diet history and identifies factors that led to success or failure. For example, past food records and exercise history can be analyzed. The analysis unit can also generate a plan that strengthens factors that led to success and avoids factors that led to failure based on the past diet history. For example, it can re-suggest meals and exercises that were effective in the past. The analysis unit can also analyze the user's past diet history and provide personalized advice. For example, it can avoid diet methods that failed in the past and optimize the plan based on methods that were successful. In this way, a more effective diet plan can be provided by analyzing the user's past diet history and optimizing the plan by taking into account factors that led to success or failure.

[0081] The analysis unit can also suggest meals and exercises according to the season and weather based on the user data. For example, a system can be built that suggests meal plans according to the season and weather based on the user data. For example, in the summer, a plan that emphasizes cold meals and hydration is suggested. The analysis unit can also suggest an optimal exercise plan for the user based on weather data. For example, indoor exercises are suggested on rainy days, and outdoor exercises are suggested on sunny days. The analysis unit can also generate an optimal meal plan for the user, taking into account seasonal nutritional needs. For example, foods high in vitamin D are suggested in winter. This makes it possible to provide a more effective diet plan by suggesting meals and exercises according to the season and weather based on the user data.

[0082] The analysis unit can also use the emotion estimation function to analyze how the user feels about the diet plan and adjust the content of the plan. For example, a system can be built using the emotion estimation function to analyze how the user feels about the diet plan. For example, if the user has strong positive emotions about the plan, the content of the plan can be strengthened. The analysis unit can also adjust the content of the diet plan based on the user's emotion data. For example, if negative emotions are detected, the difficulty of the plan can be lowered. The analysis unit can also monitor the user's emotions about the diet plan in real time based on the emotion estimation data and adjust the plan as appropriate. For example, the plan can be flexibly changed according to changes in the user's emotions. In this way, by using the emotion estimation function to analyze the user's emotions and adjusting the content of the plan, user satisfaction can be increased.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data collection unit collects the user's physical data, lifestyle habits, and dietary preferences. For example, the data collection unit collects physical data such as height, weight, age, and gender based on prompts entered by the user. The data collection unit can also collect lifestyle data such as the user's exercise frequency, sleep duration, and stress level. Furthermore, the data collection unit collects the user's dietary preferences, such as favorite foods, disliked foods, and allergies. For example, the data collection unit converts the data entered by the user into a format that is easy for the generation AI to analyze. Step 2: The analysis unit generates an optimal diet plan for the user based on the data collected by the data collection unit. For example, the analysis unit allows the generation AI to propose a meal plan that takes into account calorie intake and nutritional balance in accordance with the user's goals. The analysis unit also allows the generation AI to provide advice on the frequency and type of exercise the user should do. For example, the generation AI generates an optimal exercise plan based on the user's data. Step 3: The providing unit provides the user with the diet plan generated by the analysis unit. For example, the providing unit may specifically indicate a daily meal menu and exercise plan. The providing unit may also provide a feedback function that allows the user to record progress according to the diet plan. For example, the providing unit may collect the user's weight change, meal records, and exercise status, and the generation AI may adjust the diet plan as appropriate.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] 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.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 data collection unit that collects the user's physical data, lifestyle habits, and dietary preferences; an analysis unit that generates an optimal diet plan for a user based on the data collected by the data collection unit; a providing unit that provides the user with the diet plan generated by the analysis unit. A system characterized by:

2. The data collection unit Estimating the user's emotional state in real time and adjusting the timing and method of data collection based on the emotional state.

2. The system of claim 1.

3. The data collection unit The user can easily record their meals and exercise using voice input.

2. The system of claim 1.

4. The analysis unit Analyzing the emotional data of the user and making suggestions about diet and exercise according to the emotional data 2. The system of claim 1.

5. The providing unit Monitoring the emotional state of the user in real time and providing the encouragement or advice according to the emotion.

2. The system of claim 1.

6. The data collection unit Analyze photos of the user's meals and automatically recognize ingredients and nutrients.

2. The system of claim 1.

7. The analysis unit Analyzing the genetic information of the user and generating the diet plan taking genetic factors into consideration 2. The system of claim 1.

8. The providing unit Visualize the progress of the diet plan, allowing the user to intuitively understand how far they are moving toward their goal.

2. The system of claim 1.

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