system

Abstract

The system according to the embodiment aims to automate nutritional management and meal plan proposals, and to purchase ingredients at the optimal price. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, a suggestion unit, and an acquisition unit. The analysis unit analyzes nutritional information. The generation unit automatically generates a menu based on the information analyzed by the analysis unit. The suggestion unit proposes a meal plan based on the menu generated by the generation unit. The acquisition unit acquires location information. The analysis unit analyzes price information based on the location information acquired by the acquisition unit. The suggestion unit suggests the cheapest store based on the price information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that proposing nutritional management and meal plans places a heavy burden on nutritionists and makes it difficult to purchase ingredients at the optimal price.

[0005] The system according to the embodiment aims to automate nutritional management and meal plan proposals, and to purchase ingredients at the optimal price. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a suggestion unit, and an acquisition unit. The analysis unit analyzes nutritional information. The generation unit automatically generates a menu based on the information analyzed by the analysis unit. The suggestion unit proposes a meal plan based on the menu generated by the generation unit. The acquisition unit acquires location information. The analysis unit analyzes price information based on the location information acquired by the acquisition unit. The suggestion unit suggests the cheapest store based on the price information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment automates nutritional management and meal plan proposals, allowing you to purchase ingredients at the best price. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A nutrition management system according to an embodiment of the present invention automates nutrition management tasks in school and hospital cafeterias using AI, reducing the workload of dietitians. This system allows patients with dietary restrictions, such as diabetes, and dieters to easily manage their nutritional balance. It also has a function that suggests restaurants with the lowest prices based on location and price information. For example, in school and hospital cafeterias, nutritionists spend a great deal of time and effort managing the nutritional balance of menus. By utilizing AI, the nutritional management task of menus can be automated, reducing the burden on dietitians. Specifically, AI analyzes the nutritional information of each ingredient and automatically generates balanced menus. For example, AI can propose nutritionally balanced menus based on information such as calories, protein, fat, and vitamins of ingredients. Next, AI can support patients with dietary restrictions, such as diabetes, and dieters in managing their nutritional balance. Users set goals based on their dietary restrictions, and AI proposes appropriate meal plans based on those goals. For example, AI can propose a meal plan for diabetic patients to manage their blood sugar levels, and the user can follow the plan. The system also has a function that suggests the store with the lowest price based on location and price information. When a user enters their current location, the AI ​​analyzes the price information of nearby stores and suggests the cheapest store. For example, if a user wants to buy a specific ingredient, the AI ​​can suggest the store that offers the cheapest price for that ingredient, allowing the user to shop efficiently. This system reduces the workload of nutritionists and makes it easier for people with dietary restrictions to manage their nutritional balance. By suggesting the lowest price store, the system also makes shopping more efficient for users. As a result, the nutrition management system reduces the workload of nutritionists and makes it easier for people with dietary restrictions to manage their nutritional balance. By suggesting the lowest price store, the system also makes shopping more efficient for users.

[0029] A nutrition management system according to an embodiment includes an analysis unit, a generation unit, a proposal unit, an acquisition unit, an analysis unit, and a proposal unit. The analysis unit analyzes nutritional information. The nutritional information includes, but is not limited to, calories, vitamins, and minerals. For example, the analysis unit acquires and analyzes nutritional information for each ingredient from a database. The analysis unit can also analyze the nutritional information based on ingredient information entered by a user. For example, the analysis unit analyzes information such as calories, protein, fat, and vitamins of ingredients and evaluates nutritional balance. The generation unit automatically generates a menu based on the information analyzed by the analysis unit. For example, the generation unit uses an algorithm to automatically generate a nutritionally balanced menu. The generation unit can also adjust the menu according to the user's dietary restrictions and preferences. For example, the generation unit generates a low-carbohydrate menu for diabetics and a low-calorie menu for a user on a diet. The proposal unit proposes a meal plan based on the menu generated by the generation unit. For example, the proposal unit proposes a meal plan according to the user's dietary restrictions and health condition. The suggestion unit can also adjust the meal plan taking into account the user's preferences and allergy information. For example, the suggestion unit can suggest a meal plan that excludes ingredients to which the user has allergies. The acquisition unit acquires location information. The acquisition unit acquires the user's current location, for example, using GPS data or Wi-Fi location information. The acquisition unit can also acquire location information manually entered by the user. The analysis unit analyzes price information based on the location information acquired by the acquisition unit. For example, the analysis unit acquires and analyzes price information of nearby stores from a database. The analysis unit can also acquire and analyze online price information. The suggestion unit suggests the cheapest store based on the price information analyzed by the analysis unit. For example, the suggestion unit suggests a store offering the lowest price for an ingredient the user wants to purchase. The suggestion unit can also suggest the store closest to the user's current location. This reduces the workload of nutritionists and enables people with dietary restrictions to easily manage their nutritional balance. Furthermore, suggesting the lowest-priced store can improve the user's shopping efficiency.

[0030] The analysis unit can analyze the nutritional information of each ingredient. For example, the analysis unit obtains and analyzes the nutritional information of each ingredient from a database. For example, the analysis unit analyzes information such as the calories, vitamins, and minerals of an ingredient. The analysis unit can also analyze the nutritional information based on ingredient information input by a user. For example, the analysis unit evaluates the nutritional balance based on the calorie information of an ingredient input by a user. By analyzing the nutritional information of each ingredient, a nutritionally balanced menu can be generated. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the nutritional information of an ingredient and analyze the nutritional information using an AI model that evaluates the nutritional balance.

[0031] The generation unit can automatically generate nutritious menus. The generation unit uses, for example, an algorithm that automatically generates nutritionally balanced menus. For example, the generation unit generates nutritionally balanced menus based on information such as the calories, vitamins, and minerals of ingredients. The generation unit can also adjust the menus according to the user's dietary restrictions and preferences. For example, the generation unit generates low-carbohydrate menus for diabetics and low-calorie menus for users on diets. This automatically generates nutritionally balanced menus, reducing the burden on nutritionists. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate menus using an AI model that generates menus based on nutritional information as input.

[0032] The suggestion unit can propose a meal plan tailored to the user's dietary restrictions. The suggestion unit can propose, for example, a meal plan based on the user's dietary restrictions and health condition. For example, the suggestion unit can propose a low-carbohydrate meal plan for diabetics or a low-calorie meal plan for a user on a diet. The suggestion unit can also adjust the meal plan taking into account the user's preferences and allergy information. For example, the suggestion unit can propose a meal plan that excludes ingredients to which the user is allergic. This allows for proposing a meal plan tailored to the user's dietary restrictions, thereby meeting individual needs. Some or all of the above-described processing by the suggestion unit can be performed, for example, using AI, or can be performed without AI. For example, the suggestion unit can propose a meal plan using an AI model that inputs the user's dietary restriction information and suggests a meal plan.

[0033] The acquisition unit can acquire the user's current location. The acquisition unit acquires the user's current location using, for example, GPS data or Wi-Fi location information. For example, the acquisition unit acquires GPS data from the user's smartphone and identifies the current location. The acquisition unit can also acquire the user's current location using Wi-Fi location information. For example, the acquisition unit identifies the user's current location based on information about surrounding Wi-Fi access points. By acquiring the user's current location, suggestions based on the location information become possible. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs GPS data or Wi-Fi location information and identifies the current location.

[0034] The analysis unit can perform a detailed analysis of the price information of nearby stores. For example, the analysis unit acquires and analyzes the price information of nearby stores from a database. For example, the analysis unit identifies the cheapest store based on the store's price data. The analysis unit can also acquire and analyze online price information. For example, the analysis unit acquires and analyzes price information from an online price comparison site. By analyzing the price information of nearby stores, the analysis unit can suggest the cheapest store. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the price information using an AI model that inputs price information and identifies the cheapest store.

[0035] The suggestion unit can suggest the store with the lowest price. For example, the suggestion unit suggests a store that offers the lowest price for ingredients that the user wants to purchase. For example, the suggestion unit identifies and suggests the cheapest store based on ingredient information input by the user. The suggestion unit can also suggest the store closest to the user's current location. For example, the suggestion unit identifies and suggests the nearest store based on the user's current location. This suggests the cheapest store, thereby improving the efficiency of the user's shopping. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest stores using an AI model that inputs price information and location information and identifies the cheapest store.

[0036] The analysis unit can perform analysis based on the origin and production method of each ingredient in addition to the nutritional information of each ingredient. For example, the analysis unit can perform analysis based on the origin and production method of each ingredient in addition to the nutritional information of each ingredient. For example, the analysis unit analyzes fluctuations in nutritional value based on the origin information of the ingredient. The analysis unit can also analyze nutritional information taking into account the production method of the ingredient (organic cultivation, pesticide-free cultivation, etc.). Furthermore, the analysis unit can analyze fluctuations in nutritional value taking into account the transportation distance of the ingredient. In this way, more accurate nutritional information can be provided by taking into account the origin and production method of the ingredient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs origin information and production method of the ingredient and analyzes nutritional information.

[0037] When analyzing the nutritional information of ingredients, the analysis unit can perform the analysis based on seasonal variations in nutritional value. The analysis unit analyzes the nutritional information of ingredients, for example, based on seasonal nutritional value variation data. For example, the analysis unit analyzes nutritional value variations taking into account seasonal harvest times. The analysis unit can also analyze nutritional value variations taking into account seasonal climatic conditions. This makes it possible to provide more accurate nutritional information by taking into account seasonal variations in nutritional value. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs seasonal nutritional value variation data and analyzes nutritional information.

[0038] The analysis unit can analyze allergen information of ingredients in addition to nutritional information of each ingredient. For example, the analysis unit analyzes allergen information of ingredients in addition to nutritional information of each ingredient. For example, the analysis unit analyzes allergen information of ingredients in addition to nutritional information of each ingredient. For example, the analysis unit analyzes allergy risk based on the allergen information of ingredients. The analysis unit can also analyze nutritional balance taking into account the allergen information of ingredients. Furthermore, the analysis unit can suggest alternative ingredients based on the allergen information of ingredients. In this way, allergy risk can be reduced by analyzing the allergen information of ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs allergen information of ingredients and analyzes allergy risk.

[0039] When analyzing the nutritional information of ingredients, the analysis unit can perform a detailed analysis based on the storage method and cooking method of the ingredients. The analysis unit, for example, analyzes fluctuations in nutritional value taking into account the storage method of the ingredients (refrigerated, frozen, etc.). For example, the analysis unit analyzes fluctuations in nutritional value of ingredients stored in the refrigerator. The analysis unit can also analyze fluctuations in nutritional value taking into account the cooking method of the ingredients (baking, boiling, etc.). Furthermore, the analysis unit can analyze fluctuations in nutritional value taking into account the storage period of the ingredients. In this way, more accurate nutritional information can be provided by taking into account the storage method and cooking method of the ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes nutritional information using the storage method and cooking method of the ingredients as input.

[0040] The generation unit can take into account the user's past meal history when automatically generating a nutritionally balanced menu. The generation unit, for example, proposes a nutritionally balanced menu based on the user's past meal history. For example, the generation unit proposes a menu that includes the user's favorite ingredients based on the user's past meal history. The generation unit can also analyze the user's past meal history and propose a menu that corrects an imbalance in nutritional balance. This makes it possible to propose a more appropriate menu by taking the user's past meal history into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate a menu using an AI model that generates a menu using the user's past meal history as input.

[0041] When generating a menu, the generation unit can take into account the availability and price of ingredients. For example, the generation unit can consider the availability of ingredients and propose a menu using seasonal ingredients. For example, the generation unit generates a menu based on the supply situation for each season. The generation unit can also propose a cost-effective menu based on ingredient price information. Furthermore, the generation unit can consider the inventory status of ingredients and propose a waste-free menu. In this way, a waste-free menu can be proposed by considering the availability and price of ingredients. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a menu using an AI model that generates a menu using ingredient availability and price information as input.

[0042] The generation unit can take into account the user's dietary preferences and allergy information when automatically generating a nutritionally balanced menu. The generation unit, for example, proposes a menu including the user's favorite ingredients based on the user's dietary preferences. For example, the generation unit generates a nutritionally balanced menu based on the user's favorite ingredients. The generation unit can also propose a safe menu by taking into account the user's allergy information. Furthermore, the generation unit can propose a balanced menu based on the user's dietary preferences and allergy information. This makes it possible to propose a more appropriate menu by taking the user's dietary preferences and allergy information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate a menu using an AI model that generates a menu using the user's dietary preferences and allergy information as input.

[0043] When generating a menu, the generation unit can generate the menu taking into consideration the user's meal times and meal frequency. The generation unit, for example, considers the user's meal times and proposes a menu that can be consumed at an appropriate time. For example, the generation unit generates a menu based on the user's breakfast, lunch, and dinner times. The generation unit can also consider the user's meal frequency and propose a balanced menu. Furthermore, the generation unit can also propose an optimal menu based on the user's meal times and frequency. In this way, a more appropriate menu can be proposed by considering the user's meal times and frequency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a menu using an AI model that generates a menu using the user's meal times and frequency as input.

[0044] The suggestion unit can take into consideration the user's health condition and doctor's instructions when proposing a meal plan tailored to the user's dietary restrictions. The suggestion unit, for example, proposes an appropriate meal plan based on the user's health condition. For example, the suggestion unit proposes a nutritionally balanced meal plan based on the user's health checkup results. The suggestion unit can also propose a plan tailored to the user's dietary restrictions by taking into consideration the doctor's instructions. Furthermore, the suggestion unit can propose an optimal meal plan based on the user's health condition and doctor's instructions. This makes it possible to propose a more appropriate meal plan by taking the user's health condition and doctor's instructions into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose a meal plan using an AI model that inputs the user's health condition and doctor's instructions and proposes a meal plan.

[0045] The suggestion unit can include cooking methods and recipes for ingredients in the suggested meal plan. For example, the suggestion unit can include detailed cooking methods in the suggested meal plan. For example, the suggestion unit can provide recipes that describe in detail the cooking steps for ingredients. The suggestion unit can also include recipes that are easy to make. Furthermore, the suggestion unit can also include recipes that take nutritional balance into consideration. In this way, by including cooking methods and recipes for ingredients, the user can easily prepare meals. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can suggest a meal plan using an AI model that inputs cooking methods and recipes for ingredients and suggests a meal plan.

[0046] The suggestion unit can take into account the user's lifestyle habits and amount of exercise when proposing a meal plan tailored to the user's dietary restrictions. The suggestion unit, for example, proposes an appropriate meal plan based on the user's lifestyle habits. For example, the suggestion unit proposes a nutritionally balanced meal plan based on the user's lifestyle pattern. The suggestion unit can also propose a nutritionally balanced meal plan by taking into account the user's amount of exercise. Furthermore, the suggestion unit can also propose an optimal meal plan based on the user's lifestyle habits and amount of exercise. This makes it possible to propose a more appropriate meal plan by taking the user's lifestyle habits and amount of exercise into consideration. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose a meal plan using an AI model that inputs the user's lifestyle habits and amount of exercise and proposes a meal plan.

[0047] The suggestion unit can include information on food storage methods and purchasing locations in the proposed meal plan. For example, the suggestion unit can include food storage methods in the proposed meal plan. For example, the suggestion unit can provide detailed information on refrigerating and freezing food storage methods. The suggestion unit can also include purchasing location information in the proposed meal plan. For example, the suggestion unit can provide a list of food purchasing locations to enable the user to purchase food efficiently. Furthermore, the suggestion unit can provide information on storage methods and purchasing locations in an integrated manner. In this way, by including information on food storage methods and purchasing locations, the user can efficiently manage food ingredients. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input information on food storage methods and purchasing locations and use an AI model to suggest a meal plan.

[0048] The acquisition unit can take the user's past movement history into consideration when acquiring the user's current location. The acquisition unit, for example, predicts and acquires the current location based on the user's past movement history. For example, the acquisition unit analyzes the user's past movement patterns and acquires optimal location information. The acquisition unit can also acquire the current location by referring to the user's past movement history. In this way, more accurate location information can be acquired by considering the user's past movement history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs the user's past movement history and predicts the current location.

[0049] When acquiring location information, the acquisition unit can select an acquisition method taking into account the remaining battery level of the user's device. For example, when the device's remaining battery level is low, the acquisition unit selects a low-power location information acquisition method. For example, when the device's remaining battery level is low, the acquisition unit refrains from using GPS and selects a location information acquisition method using Wi-Fi or Bluetooth (registered trademark). Furthermore, when the device's remaining battery level is sufficient, the acquisition unit can select a high-precision location information acquisition method. For example, when the device's remaining battery level is sufficient, the acquisition unit selects a high-precision location information acquisition method using GPS. Furthermore, the frequency of location information acquisition can be adjusted according to the device's remaining battery level. This allows for efficient acquisition of location information by taking into account the remaining battery level of the user's device. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can acquire location information using an AI model that inputs the device's remaining battery level and selects the optimal location information acquisition method.

[0050] When acquiring location information, the acquisition unit can select the optimal acquisition method taking into consideration the type and settings of the user's device. For example, when using a smartphone, the acquisition unit selects a location information acquisition method using GPS. For example, the acquisition unit acquires accurate location information using the smartphone's GPS function. Furthermore, when using a tablet, the acquisition unit can select a location information acquisition method using Wi-Fi. For example, the acquisition unit acquires location information using the tablet's Wi-Fi function. Furthermore, when using a smartwatch, the acquisition unit can select a location information acquisition method using Bluetooth. This allows for efficient acquisition of location information by taking into consideration the type and settings of the user's device. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs the type and settings of the device and selects the optimal location information acquisition method.

[0051] When acquiring location information, the acquisition unit can select an acquisition method taking into consideration the user's current activity status and schedule. For example, when the user is moving, the acquisition unit acquires location information in real time. For example, the acquisition unit detects that the user is moving and acquires location information in real time. Furthermore, when the user is taking a break, the acquisition unit can reduce the frequency of acquiring location information. For example, the acquisition unit detects that the user is taking a break and reduces the frequency of acquiring location information. Furthermore, the acquisition unit can acquire location information at the optimal timing taking into consideration the user's schedule. This allows for efficient acquisition of location information by taking into consideration the user's activity status and schedule. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs the user's activity status and schedule and selects the optimal location information acquisition method.

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

[0053] The analysis unit can also analyze the user's dietary history and evaluate nutritional balance based on past eating patterns. For example, the analysis unit retrieves nutritional information on ingredients the user has previously consumed from a database and analyzes it. The analysis unit can also identify imbalances in nutritional balance based on the user's dietary history and suggest areas for improvement. Furthermore, the analysis unit can take the user's dietary history into consideration and suggest ingredients to supplement nutrients that were previously lacking. This allows for more personalized nutritional management by taking the user's past dietary history into consideration.

[0054] The generation unit can also generate menus taking into account the user's exercise amount and activity level in addition to dietary restrictions. For example, the generation unit receives as input the type and frequency of the user's daily exercise and calculates the appropriate calorie intake based on that. The generation unit can also suggest menus that take into account the user's energy expenditure according to the user's activity level. Furthermore, the generation unit can generate menus that include nutrients needed after a specific exercise. This allows for more appropriate nutritional management by taking into account the user's exercise amount and activity level.

[0055] The acquisition unit can acquire location information by taking into account the user's past movement patterns in addition to the user's current location. For example, the acquisition unit records places frequently visited by the user in a database and analyzes the user's past movement patterns. The acquisition unit can also suggest the closest store to the user's current location based on the location information of stores the user has previously visited. Furthermore, the acquisition unit can also suggest an efficient route by taking into account the user's past movement patterns. This makes it possible to acquire more accurate location information by taking into account the user's past movement patterns.

[0056] The generation unit can also generate a menu taking into account the allergen information of ingredients in addition to the user's dietary restrictions. For example, the generation unit can exclude ingredients to which the user is allergic from the database to generate a safe menu. The generation unit can also suggest alternative ingredients based on the allergen information. Furthermore, the generation unit can also generate a nutritionally balanced menu taking into account the allergen information. This makes it possible to suggest a safe and appropriate menu by taking into account the user's allergy information.

[0057] The acquisition unit can also select a location information acquisition method taking into account the remaining battery power of the user's device. For example, if the device's battery power is low, the acquisition unit can select a low-power location information acquisition method. Alternatively, if the device's battery power is sufficient, the acquisition unit can select a high-precision location information acquisition method. Furthermore, the acquisition unit can adjust the frequency of location information acquisition according to the device's remaining battery power. This allows for efficient location information acquisition by taking into account the device's remaining battery power.

[0058] The suggestion unit can also suggest a meal plan taking into consideration the user's dietary restrictions, doctor's instructions, and health checkup results. For example, the suggestion unit can suggest a nutritionally balanced meal plan based on the user's health checkup results. The suggestion unit can also suggest a meal plan that restricts specific nutrients, taking into consideration the doctor's instructions. Furthermore, the suggestion unit can suggest an optimal meal plan based on the user's health condition and doctor's instructions. This makes it possible to suggest a more appropriate meal plan by taking into consideration the user's health condition and doctor's instructions.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The analysis unit analyzes the nutritional information. Nutritional information includes calories, vitamins, minerals, etc. The analysis unit retrieves and analyzes the nutritional information for each ingredient from the database. It can also analyze nutritional information based on ingredient information entered by the user. For example, it analyzes information such as calories, protein, lipids, and vitamins of ingredients to evaluate their nutritional balance. Step 2: The generation unit automatically generates a menu based on the information analyzed by the analysis unit. The generation unit uses an algorithm to automatically generate a nutritionally balanced menu. The generation unit can also adjust the menu according to the user's dietary restrictions and preferences. For example, it can generate a low-carbohydrate menu for diabetics or a low-calorie menu for users on a diet. Step 3: The suggestion unit proposes a meal plan based on the menu generated by the generation unit. The suggestion unit proposes a meal plan that takes into account the user's dietary restrictions and health condition. The suggestion unit can also adjust the meal plan taking into account the user's preferences and allergy information. For example, the suggestion unit proposes a meal plan that excludes ingredients to which the user is allergic. Step 4: The acquisition unit acquires location information. The acquisition unit acquires the user's current location using GPS data or Wi-Fi location information. It can also acquire location information manually entered by the user. Step 5: The analysis unit analyzes the price information based on the location information acquired by the acquisition unit. The analysis unit acquires and analyzes price information of nearby stores from a database. It can also acquire and analyze online price information. Step 6: The suggestion unit suggests the cheapest store based on the price information analyzed by the analysis unit. The suggestion unit suggests the store that offers the lowest price for the ingredients the user wants to purchase. It can also suggest the store that is closest to the user's current location.

[0061] (Example 2) A nutrition management system according to an embodiment of the present invention automates nutrition management tasks in school and hospital cafeterias using AI, reducing the workload of dietitians. This system allows patients with dietary restrictions, such as diabetes, and dieters to easily manage their nutritional balance. It also has a function that suggests restaurants with the lowest prices based on location and price information. For example, in school and hospital cafeterias, nutritionists spend a great deal of time and effort managing the nutritional balance of menus. By utilizing AI, the nutritional management task of menus can be automated, reducing the burden on dietitians. Specifically, AI analyzes the nutritional information of each ingredient and automatically generates balanced menus. For example, AI can propose nutritionally balanced menus based on information such as calories, protein, fat, and vitamins of ingredients. Next, AI can support patients with dietary restrictions, such as diabetes, and dieters in managing their nutritional balance. Users set goals based on their dietary restrictions, and AI proposes appropriate meal plans based on those goals. For example, AI can propose a meal plan for diabetic patients to manage their blood sugar levels, and the user can follow the plan. The system also has a function that suggests the store with the lowest price based on location and price information. When a user enters their current location, the AI ​​analyzes the price information of nearby stores and suggests the cheapest store. For example, if a user wants to buy a specific ingredient, the AI ​​can suggest the store that offers the cheapest price for that ingredient, allowing the user to shop efficiently. This system reduces the workload of nutritionists and makes it easier for people with dietary restrictions to manage their nutritional balance. By suggesting the lowest price store, the system also makes shopping more efficient for users. As a result, the nutrition management system reduces the workload of nutritionists and makes it easier for people with dietary restrictions to manage their nutritional balance. By suggesting the lowest price store, the system also makes shopping more efficient for users.

[0062] A nutrition management system according to an embodiment includes an analysis unit, a generation unit, a proposal unit, an acquisition unit, an analysis unit, and a proposal unit. The analysis unit analyzes nutritional information. The nutritional information includes, but is not limited to, calories, vitamins, and minerals. For example, the analysis unit acquires and analyzes nutritional information for each ingredient from a database. The analysis unit can also analyze the nutritional information based on ingredient information entered by a user. For example, the analysis unit analyzes information such as calories, protein, fat, and vitamins of ingredients and evaluates nutritional balance. The generation unit automatically generates a menu based on the information analyzed by the analysis unit. For example, the generation unit uses an algorithm to automatically generate a nutritionally balanced menu. The generation unit can also adjust the menu according to the user's dietary restrictions and preferences. For example, the generation unit generates a low-carbohydrate menu for diabetics and a low-calorie menu for a user on a diet. The proposal unit proposes a meal plan based on the menu generated by the generation unit. For example, the proposal unit proposes a meal plan according to the user's dietary restrictions and health condition. The suggestion unit can also adjust the meal plan taking into account the user's preferences and allergy information. For example, the suggestion unit can suggest a meal plan that excludes ingredients to which the user has allergies. The acquisition unit acquires location information. The acquisition unit acquires the user's current location, for example, using GPS data or Wi-Fi location information. The acquisition unit can also acquire location information manually entered by the user. The analysis unit analyzes price information based on the location information acquired by the acquisition unit. For example, the analysis unit acquires and analyzes price information of nearby stores from a database. The analysis unit can also acquire and analyze online price information. The suggestion unit suggests the cheapest store based on the price information analyzed by the analysis unit. For example, the suggestion unit suggests a store offering the lowest price for an ingredient the user wants to purchase. The suggestion unit can also suggest the store closest to the user's current location. This reduces the workload of nutritionists and enables people with dietary restrictions to easily manage their nutritional balance. Furthermore, suggesting the lowest-priced store can improve the user's shopping efficiency.

[0063] The analysis unit can analyze the nutritional information of each ingredient. For example, the analysis unit obtains and analyzes the nutritional information of each ingredient from a database. For example, the analysis unit analyzes information such as the calories, vitamins, and minerals of an ingredient. The analysis unit can also analyze the nutritional information based on ingredient information input by a user. For example, the analysis unit evaluates the nutritional balance based on the calorie information of an ingredient input by a user. By analyzing the nutritional information of each ingredient, a nutritionally balanced menu can be generated. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the nutritional information of an ingredient and analyze the nutritional information using an AI model that evaluates the nutritional balance.

[0064] The generation unit can automatically generate nutritious menus. The generation unit uses, for example, an algorithm that automatically generates nutritionally balanced menus. For example, the generation unit generates nutritionally balanced menus based on information such as the calories, vitamins, and minerals of ingredients. The generation unit can also adjust the menus according to the user's dietary restrictions and preferences. For example, the generation unit generates low-carbohydrate menus for diabetics and low-calorie menus for users on diets. This automatically generates nutritionally balanced menus, reducing the burden on nutritionists. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate menus using an AI model that generates menus based on nutritional information as input.

[0065] The suggestion unit can propose a meal plan tailored to the user's dietary restrictions. The suggestion unit can propose, for example, a meal plan based on the user's dietary restrictions and health condition. For example, the suggestion unit can propose a low-carbohydrate meal plan for diabetics or a low-calorie meal plan for a user on a diet. The suggestion unit can also adjust the meal plan taking into account the user's preferences and allergy information. For example, the suggestion unit can propose a meal plan that excludes ingredients to which the user is allergic. This allows for proposing a meal plan tailored to the user's dietary restrictions, thereby meeting individual needs. Some or all of the above-described processing by the suggestion unit can be performed, for example, using AI, or can be performed without AI. For example, the suggestion unit can propose a meal plan using an AI model that inputs the user's dietary restriction information and suggests a meal plan.

[0066] The acquisition unit can acquire the user's current location. The acquisition unit acquires the user's current location using, for example, GPS data or Wi-Fi location information. For example, the acquisition unit acquires GPS data from the user's smartphone and identifies the current location. The acquisition unit can also acquire the user's current location using Wi-Fi location information. For example, the acquisition unit identifies the user's current location based on information about surrounding Wi-Fi access points. By acquiring the user's current location, suggestions based on the location information become possible. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs GPS data or Wi-Fi location information and identifies the current location.

[0067] The analysis unit can perform a detailed analysis of the price information of nearby stores. For example, the analysis unit acquires and analyzes the price information of nearby stores from a database. For example, the analysis unit identifies the cheapest store based on the store's price data. The analysis unit can also acquire and analyze online price information. For example, the analysis unit acquires and analyzes price information from an online price comparison site. By analyzing the price information of nearby stores, the analysis unit can suggest the cheapest store. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the price information using an AI model that inputs price information and identifies the cheapest store.

[0068] The suggestion unit can suggest the store with the lowest price. For example, the suggestion unit suggests a store that offers the lowest price for ingredients that the user wants to purchase. For example, the suggestion unit identifies and suggests the cheapest store based on ingredient information input by the user. The suggestion unit can also suggest the store closest to the user's current location. For example, the suggestion unit identifies and suggests the nearest store based on the user's current location. This suggests the cheapest store, thereby improving the efficiency of the user's shopping. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest stores using an AI model that inputs price information and location information and identifies the cheapest store.

[0069] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis priority can be changed and analysis can start with simple ingredients. Also, if the user is relaxed, analysis of ingredients with detailed nutritional information can be prioritized. Furthermore, if the user is in a hurry, analysis focusing on major nutrients can be prioritized. This reduces the burden on the user by adjusting the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform analysis using an AI model that inputs user emotion data and adjusts the analysis priority.

[0070] The analysis unit can perform analysis based on the origin and production method of each ingredient in addition to the nutritional information of each ingredient. For example, the analysis unit can perform analysis based on the origin and production method of each ingredient in addition to the nutritional information of each ingredient. For example, the analysis unit analyzes fluctuations in nutritional value based on the origin information of the ingredient. The analysis unit can also analyze nutritional information taking into account the production method of the ingredient (organic cultivation, pesticide-free cultivation, etc.). Furthermore, the analysis unit can analyze fluctuations in nutritional value taking into account the transportation distance of the ingredient. In this way, more accurate nutritional information can be provided by taking into account the origin and production method of the ingredient. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs origin information and production method of the ingredient and analyzes nutritional information.

[0071] When analyzing the nutritional information of ingredients, the analysis unit can perform the analysis based on seasonal variations in nutritional value. The analysis unit analyzes the nutritional information of ingredients, for example, based on seasonal nutritional value variation data. For example, the analysis unit analyzes nutritional value variations taking into account seasonal harvest times. The analysis unit can also analyze nutritional value variations taking into account seasonal climatic conditions. This makes it possible to provide more accurate nutritional information by taking into account seasonal variations in nutritional value. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs seasonal nutritional value variation data and analyzes nutritional information.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed. This reduces the burden on the user by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform analysis using an AI model that inputs user emotion data and adjusts the display method of the analysis results.

[0073] The analysis unit can analyze allergen information of ingredients in addition to nutritional information of each ingredient. For example, the analysis unit analyzes allergen information of ingredients in addition to nutritional information of each ingredient. For example, the analysis unit analyzes allergen information of ingredients in addition to nutritional information of each ingredient. For example, the analysis unit analyzes allergy risk based on the allergen information of ingredients. The analysis unit can also analyze nutritional balance taking into account the allergen information of ingredients. Furthermore, the analysis unit can suggest alternative ingredients based on the allergen information of ingredients. In this way, allergy risk can be reduced by analyzing the allergen information of ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs allergen information of ingredients and analyzes allergy risk.

[0074] When analyzing the nutritional information of ingredients, the analysis unit can perform a detailed analysis based on the storage method and cooking method of the ingredients. The analysis unit, for example, analyzes fluctuations in nutritional value taking into account the storage method of the ingredients (refrigerated, frozen, etc.). For example, the analysis unit analyzes fluctuations in nutritional value of ingredients stored in the refrigerator. The analysis unit can also analyze fluctuations in nutritional value taking into account the cooking method of the ingredients (baking, boiling, etc.). Furthermore, the analysis unit can analyze fluctuations in nutritional value taking into account the storage period of the ingredients. In this way, more accurate nutritional information can be provided by taking into account the storage method and cooking method of the ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes nutritional information using the storage method and cooking method of the ingredients as input.

[0075] The generation unit can estimate the user's emotions and adjust the menu variations based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the menu variations based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can suggest a simple and easy-to-prepare menu. Also, if the user is relaxed, the generation unit can suggest a menu with a wide variety of dishes. Furthermore, if the user is in a hurry, the generation unit can suggest a menu that can be prepared in a short amount of time. This improves user satisfaction by adjusting the menu variations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a menu using an AI model that inputs user emotion data and adjusts the menu variations.

[0076] The generation unit can take into account the user's past meal history when automatically generating a nutritionally balanced menu. The generation unit, for example, proposes a nutritionally balanced menu based on the user's past meal history. For example, the generation unit proposes a menu that includes the user's favorite ingredients based on the user's past meal history. The generation unit can also analyze the user's past meal history and propose a menu that corrects an imbalance in nutritional balance. This makes it possible to propose a more appropriate menu by taking the user's past meal history into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate a menu using an AI model that generates a menu using the user's past meal history as input.

[0077] When generating a menu, the generation unit can take into account the availability and price of ingredients. For example, the generation unit can consider the availability of ingredients and propose a menu using seasonal ingredients. For example, the generation unit generates a menu based on the supply situation for each season. The generation unit can also propose a cost-effective menu based on ingredient price information. Furthermore, the generation unit can consider the inventory status of ingredients and propose a waste-free menu. In this way, a waste-free menu can be proposed by considering the availability and price of ingredients. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a menu using an AI model that generates a menu using ingredient availability and price information as input.

[0078] The generation unit can estimate the user's emotions and adjust the menu display method based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the menu display method based on the estimated user emotions. For example, if the user is stressed, a simple display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This reduces the burden on the user by adjusting the menu display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can generate a menu using an AI model that inputs user emotion data and adjusts the menu display method.

[0079] The generation unit can take into account the user's dietary preferences and allergy information when automatically generating a nutritionally balanced menu. The generation unit, for example, proposes a menu including the user's favorite ingredients based on the user's dietary preferences. For example, the generation unit generates a nutritionally balanced menu based on the user's favorite ingredients. The generation unit can also propose a safe menu by taking into account the user's allergy information. Furthermore, the generation unit can propose a balanced menu based on the user's dietary preferences and allergy information. This makes it possible to propose a more appropriate menu by taking the user's dietary preferences and allergy information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can automatically generate a menu using an AI model that generates a menu using the user's dietary preferences and allergy information as input.

[0080] When generating a menu, the generation unit can generate the menu taking into consideration the user's meal times and meal frequency. The generation unit, for example, considers the user's meal times and proposes a menu that can be consumed at an appropriate time. For example, the generation unit generates a menu based on the user's breakfast, lunch, and dinner times. The generation unit can also consider the user's meal frequency and propose a balanced menu. Furthermore, the generation unit can also propose an optimal menu based on the user's meal times and frequency. In this way, a more appropriate menu can be proposed by considering the user's meal times and frequency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a menu using an AI model that generates a menu using the user's meal times and frequency as input.

[0081] The suggestion unit can estimate the user's emotions and adjust the content of the suggestion based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the content of the suggestion based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple and easy-to-prepare meal plan. Also, if the user is relaxed, the suggestion unit can suggest a meal plan with a wide variety of options. Furthermore, if the user is in a hurry, the suggestion unit can suggest a meal plan that can be prepared in a short amount of time. This improves user satisfaction by adjusting the content of the suggestion based on the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can suggest a meal plan using an AI model that inputs the user's emotion data and adjusts the content of the suggestion.

[0082] The suggestion unit can take into consideration the user's health condition and doctor's instructions when proposing a meal plan tailored to the user's dietary restrictions. The suggestion unit, for example, proposes an appropriate meal plan based on the user's health condition. For example, the suggestion unit proposes a nutritionally balanced meal plan based on the user's health checkup results. The suggestion unit can also propose a plan tailored to the user's dietary restrictions by taking into consideration the doctor's instructions. Furthermore, the suggestion unit can propose an optimal meal plan based on the user's health condition and doctor's instructions. This makes it possible to propose a more appropriate meal plan by taking the user's health condition and doctor's instructions into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose a meal plan using an AI model that inputs the user's health condition and doctor's instructions and proposes a meal plan.

[0083] The suggestion unit can include cooking methods and recipes for ingredients in the suggested meal plan. For example, the suggestion unit can include detailed cooking methods in the suggested meal plan. For example, the suggestion unit can provide recipes that describe in detail the cooking steps for ingredients. The suggestion unit can also include recipes that are easy to make. Furthermore, the suggestion unit can also include recipes that take nutritional balance into consideration. In this way, by including cooking methods and recipes for ingredients, the user can easily prepare meals. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can suggest a meal plan using an AI model that inputs cooking methods and recipes for ingredients and suggests a meal plan.

[0084] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, meal plans that are easy to prepare can be prioritized. Furthermore, if the user is relaxed, meal plans with a wide variety of options can be prioritized. Furthermore, if the user is in a hurry, meal plans that can be prepared quickly can be prioritized. This improves user satisfaction by prioritizing suggestions based on the user's emotions. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Examples of generative AI include text generation AI (e.g., LLM) and multimodal generation AI, but are not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can suggest meal plans using an AI model that receives user emotion data and prioritizes suggestions.

[0085] The suggestion unit can take into account the user's lifestyle habits and amount of exercise when proposing a meal plan tailored to the user's dietary restrictions. The suggestion unit, for example, proposes an appropriate meal plan based on the user's lifestyle habits. For example, the suggestion unit proposes a nutritionally balanced meal plan based on the user's lifestyle pattern. The suggestion unit can also propose a nutritionally balanced meal plan by taking into account the user's amount of exercise. Furthermore, the suggestion unit can also propose an optimal meal plan based on the user's lifestyle habits and amount of exercise. This makes it possible to propose a more appropriate meal plan by taking the user's lifestyle habits and amount of exercise into consideration. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose a meal plan using an AI model that inputs the user's lifestyle habits and amount of exercise and proposes a meal plan.

[0086] The suggestion unit can include information on food storage methods and purchasing locations in the proposed meal plan. For example, the suggestion unit can include food storage methods in the proposed meal plan. For example, the suggestion unit can provide detailed information on refrigerating and freezing food storage methods. The suggestion unit can also include purchasing location information in the proposed meal plan. For example, the suggestion unit can provide a list of food purchasing locations to enable the user to purchase food efficiently. Furthermore, the suggestion unit can provide information on storage methods and purchasing locations in an integrated manner. In this way, by including information on food storage methods and purchasing locations, the user can efficiently manage food ingredients. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input information on food storage methods and purchasing locations and use an AI model to suggest a meal plan.

[0087] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition frequency of location information can be reduced to reduce the burden. Furthermore, if the user is relaxed, the acquisition frequency of location information can be increased to provide more detailed information. Furthermore, if the user is in a hurry, the acquisition frequency of location information can be increased to provide quick suggestions. This reduces the burden on the user by adjusting the timing of acquiring location information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI. For example, the acquisition unit can acquire location information using an AI model that inputs user emotion data and adjusts the timing of acquiring location information.

[0088] The acquisition unit can take the user's past movement history into consideration when acquiring the user's current location. The acquisition unit, for example, predicts and acquires the current location based on the user's past movement history. For example, the acquisition unit analyzes the user's past movement patterns and acquires optimal location information. The acquisition unit can also acquire the current location by referring to the user's past movement history. In this way, more accurate location information can be acquired by considering the user's past movement history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs the user's past movement history and predicts the current location.

[0089] When acquiring location information, the acquisition unit can select an acquisition method taking into account the remaining battery level of the user's device. For example, when the device's battery level is low, the acquisition unit selects a low-power location information acquisition method. For example, when the battery level is low, the acquisition unit refrains from using GPS and selects a location information acquisition method using Wi-Fi or Bluetooth. Furthermore, when the device's battery level is sufficient, the acquisition unit can select a high-precision location information acquisition method. For example, when the battery level is sufficient, the acquisition unit selects a high-precision location information acquisition method using GPS. Furthermore, the frequency of location information acquisition can be adjusted according to the device's remaining battery level. This allows for efficient acquisition of location information by taking into account the remaining battery level of the user's device. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can acquire location information using an AI model that inputs the device's remaining battery level and selects the optimal location information acquisition method.

[0090] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of location information to be acquired based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can prioritize acquisition of main location information. Also, if the user is relaxed, the acquisition unit can prioritize acquisition of detailed location information. Furthermore, if the user is in a hurry, the acquisition unit can prioritize location information that can be acquired quickly. This reduces the burden on the user by determining the priority of location information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that receives user emotion data as input and determines the priority of location information.

[0091] When acquiring location information, the acquisition unit can select the optimal acquisition method taking into consideration the type and settings of the user's device. For example, when using a smartphone, the acquisition unit selects a location information acquisition method using GPS. For example, the acquisition unit acquires accurate location information using the smartphone's GPS function. Furthermore, when using a tablet, the acquisition unit can select a location information acquisition method using Wi-Fi. For example, the acquisition unit acquires location information using the tablet's Wi-Fi function. Furthermore, when using a smartwatch, the acquisition unit can select a location information acquisition method using Bluetooth. This allows for efficient acquisition of location information by taking into consideration the type and settings of the user's device. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs the type and settings of the device and selects the optimal location information acquisition method.

[0092] When acquiring location information, the acquisition unit can select an acquisition method taking into consideration the user's current activity status and schedule. For example, when the user is moving, the acquisition unit acquires location information in real time. For example, the acquisition unit detects that the user is moving and acquires location information in real time. Furthermore, when the user is taking a break, the acquisition unit can reduce the frequency of acquiring location information. For example, the acquisition unit detects that the user is taking a break and reduces the frequency of acquiring location information. Furthermore, the acquisition unit can acquire location information at the optimal timing taking into consideration the user's schedule. This allows for efficient acquisition of location information by taking into consideration the user's activity status and schedule. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can acquire location information using an AI model that inputs the user's activity status and schedule and selects the optimal location information acquisition method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, proposal unit, and acquisition unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The acquisition unit is realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, suggestion unit, and acquisition unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The acquisition unit is realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, proposal unit, and acquisition unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The acquisition unit is realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, proposal unit, and acquisition unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The acquisition unit is realized, for example, by the control unit 46A of the robot 414.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can also analyze the user's dietary history and evaluate nutritional balance based on past eating patterns. For example, the analysis unit retrieves nutritional information on ingredients the user has previously consumed from a database and analyzes it. The analysis unit can also identify imbalances in nutritional balance based on the user's dietary history and suggest areas for improvement. Furthermore, the analysis unit can take the user's dietary history into consideration and suggest ingredients to supplement nutrients that were previously lacking. This allows for more personalized nutritional management by taking the user's past dietary history into consideration.

[0095] The generation unit can also generate menus taking into account the user's exercise amount and activity level in addition to dietary restrictions. For example, the generation unit receives as input the type and frequency of the user's daily exercise and calculates the appropriate calorie intake based on that. The generation unit can also suggest menus that take into account the user's energy expenditure according to the user's activity level. Furthermore, the generation unit can generate menus that include nutrients needed after a specific exercise. This allows for more appropriate nutritional management by taking into account the user's exercise amount and activity level.

[0096] The suggestion unit can estimate the user's emotions and adjust the difficulty of the meal plan based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a meal plan that is simple and easy to prepare. If the user is relaxed, the suggestion unit can suggest a delicious meal plan that takes time to prepare. Furthermore, if the user is in a hurry, the suggestion unit can suggest a meal plan that can be prepared in a short amount of time. In this way, by adjusting the difficulty of the meal plan according to the user's emotions, the user's satisfaction can be improved.

[0097] The acquisition unit can acquire location information by taking into account the user's past movement patterns in addition to the user's current location. For example, the acquisition unit records places frequently visited by the user in a database and analyzes the user's past movement patterns. The acquisition unit can also suggest the closest store to the user's current location based on the location information of stores the user has previously visited. Furthermore, the acquisition unit can also suggest an efficient route by taking into account the user's past movement patterns. This makes it possible to acquire more accurate location information by taking into account the user's past movement patterns.

[0098] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-understand display method can be provided. If the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed. In this way, the burden on the user can be reduced by adjusting the display method of the analysis results according to the user's emotions.

[0099] The generation unit can also generate a menu taking into account the allergen information of ingredients in addition to the user's dietary restrictions. For example, the generation unit can exclude ingredients to which the user is allergic from the database to generate a safe menu. The generation unit can also suggest alternative ingredients based on the allergen information. Furthermore, the generation unit can also generate a nutritionally balanced menu taking into account the allergen information. This makes it possible to suggest a safe and appropriate menu by taking into account the user's allergy information.

[0100] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple and easy-to-make meal plan. If the user is relaxed, the suggestion unit can suggest a meal plan with a wide variety of options. Furthermore, if the user is in a hurry, the suggestion unit can suggest a meal plan that can be made in a short amount of time. In this way, by adjusting the content of the suggestions according to the user's emotions, it is possible to improve user satisfaction.

[0101] The acquisition unit can also select a location information acquisition method taking into account the remaining battery power of the user's device. For example, if the device's battery power is low, the acquisition unit can select a low-power location information acquisition method. Alternatively, if the device's battery power is sufficient, the acquisition unit can select a high-precision location information acquisition method. Furthermore, the acquisition unit can adjust the frequency of location information acquisition according to the device's remaining battery power. This allows for efficient location information acquisition by taking into account the device's remaining battery power.

[0102] The generation unit can estimate the user's emotions and adjust the menu variations based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-prepare menu can be suggested. If the user is relaxed, a menu with a wide variety can be suggested. Furthermore, if the user is in a hurry, a menu that can be prepared in a short time can be suggested. In this way, by adjusting the menu variations according to the user's emotions, user satisfaction can be improved.

[0103] The suggestion unit can also suggest a meal plan taking into consideration the user's dietary restrictions, doctor's instructions, and health checkup results. For example, the suggestion unit can suggest a nutritionally balanced meal plan based on the user's health checkup results. The suggestion unit can also suggest a meal plan that restricts specific nutrients, taking into consideration the doctor's instructions. Furthermore, the suggestion unit can suggest an optimal meal plan based on the user's health condition and doctor's instructions. This makes it possible to suggest a more appropriate meal plan by taking into consideration the user's health condition and doctor's instructions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The analysis unit analyzes the nutritional information. Nutritional information includes calories, vitamins, minerals, etc. The analysis unit retrieves and analyzes the nutritional information for each ingredient from the database. It can also analyze nutritional information based on ingredient information entered by the user. For example, it analyzes information such as calories, protein, lipids, and vitamins of ingredients to evaluate their nutritional balance. Step 2: The generation unit automatically generates a menu based on the information analyzed by the analysis unit. The generation unit uses an algorithm to automatically generate a nutritionally balanced menu. The generation unit can also adjust the menu according to the user's dietary restrictions and preferences. For example, it can generate a low-carbohydrate menu for diabetics or a low-calorie menu for users on a diet. Step 3: The suggestion unit proposes a meal plan based on the menu generated by the generation unit. The suggestion unit proposes a meal plan that takes into account the user's dietary restrictions and health condition. The suggestion unit can also adjust the meal plan taking into account the user's preferences and allergy information. For example, the suggestion unit proposes a meal plan that excludes ingredients to which the user is allergic. Step 4: The acquisition unit acquires location information. The acquisition unit acquires the user's current location using GPS data or Wi-Fi location information. It can also acquire location information manually entered by the user. Step 5: The analysis unit analyzes the price information based on the location information acquired by the acquisition unit. The analysis unit acquires and analyzes price information of nearby stores from a database. It can also acquire and analyze online price information. Step 6: The suggestion unit suggests the cheapest store based on the price information analyzed by the analysis unit. The suggestion unit suggests the store that offers the lowest price for the ingredients the user wants to purchase. It can also suggest the store that is closest to the user's current location.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, in order to avoid confusion and to 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes nutritional information; A generation unit that automatically generates a menu based on the information analyzed by the analysis unit; a suggestion unit that proposes a meal plan based on the menu generated by the generation unit; an acquisition unit that acquires location information; an analysis unit that analyzes price information based on the location information acquired by the acquisition unit; a suggestion unit that suggests the cheapest store based on the price information analyzed by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Analyzing the nutritional information of each ingredient 2. The system of claim 1.

3. The generation unit Automatically generate nutritious meals 2. The system of claim 1.

4. The proposal unit Proposes meal plans tailored to the user's dietary restrictions 2. The system of claim 1.

5. The acquisition unit Get the user's current location 2. The system of claim 1.

6. The analysis unit Detailed analysis of price information of nearby stores 2. The system of claim 1.

7. The proposal unit Suggest the lowest priced store 2. The system of claim 1.

8. The analysis unit Estimate user emotions and adjust analysis priorities based on the estimated user emotions 2. The system of claim 1.

Images