system

Abstract

The system according to this embodiment aims to analyze the quality and taste of fresh food and support appropriate selection. [Solution] The system according to the embodiment comprises an evaluation unit, an analysis unit, and a recommendation unit. When a user holds fresh food up to the camera, the evaluation unit uses AI to analyze and evaluate the food. The analysis unit displays a breakdown of the results evaluated by the evaluation unit. The recommendation unit recommends dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to determine the quality and taste of fresh food and an appropriate selection cannot be made.

[0005] The system according to the embodiment aims to analyze the quality and taste of fresh food and assist in making an appropriate selection.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an evaluation unit, an analysis unit, and a recommendation unit. When a user holds fresh food up to the camera, the evaluation unit uses AI to analyze and evaluate the food. The analysis unit displays a breakdown of the evaluation results from the evaluation unit. The recommendation unit recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained from the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the quality and taste of fresh food and support appropriate selection. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fresh food evaluation system according to an embodiment of the present invention is an application for people shopping for fresh food, and is a system that visualizes the deliciousness of fresh food using AI. In this system, when a user points the application's camera at fresh food, the AI ​​analyzes the fresh food and assigns a score to each individual item to evaluate its deliciousness. The breakdown of the evaluation is also displayed, and includes, for example, the characteristics of deliciousness for each type (smaller stems mean tastier, yellow mouths on mackerel mean tastier) and color (color of fillets or meat, etc.). Furthermore, based on the fresh food the user is about to buy, the AI ​​recommends suitable dishes, necessary ingredients, and recipes. This function makes it easy for users to find out how to cook the fresh food they have purchased. The application also aims to monetize by displaying display advertisements. Revenue is generated from advertisements displayed when users use the application. In this way, the fresh food evaluation system can improve the quality of shopping by visualizing the deliciousness of fresh food and providing convenient information to users. For example, when a user points the application's camera at fresh produce, the AI ​​analyzes the produce, assigns a score to each individual item, and evaluates its taste. The breakdown of the evaluation is also displayed, including characteristics of taste specific to each type of produce (e.g., smaller stems indicate better taste, yellow mouths on mackerel indicate better taste) and color (e.g., color of fillets or meat). Furthermore, based on the fresh produce the user is considering buying, the AI ​​recommends suitable dishes, necessary ingredients, and recipes. This feature makes it easy for users to learn how to cook the fresh produce they have purchased. The application also monetizes by displaying advertisements. Revenue is generated from ads displayed when users use the application. In this way, the fresh produce evaluation system can improve the quality of shopping by visualizing the taste of fresh produce and providing users with useful information. As a result, the fresh produce evaluation system allows users to easily evaluate the taste of fresh produce and choose appropriate dishes and ingredients.

[0029] The fresh food evaluation system according to this embodiment comprises an evaluation unit, an analysis unit, and a recommendation unit. When a user points a camera at fresh food, the evaluation unit uses AI to analyze and evaluate the food. For example, when a user points a camera at fresh food, the evaluation unit uses image analysis technology to analyze the color and shape of the food and evaluate its freshness and quality. The evaluation unit can, for example, analyze changes in the color and shape characteristics of the food to evaluate its freshness and quality. The evaluation unit can, for example, analyze the color and shape of the food's surface to evaluate its freshness and quality. The analysis unit displays a breakdown of the results evaluated by the evaluation unit. For example, the analysis unit displays the evaluation results as a score so that the user can intuitively understand the evaluation results. For example, the analysis unit displays the evaluation results as a graph or chart so that the user can visually confirm the evaluation results. For example, the analysis unit displays the evaluation results in detail so that the user can understand the breakdown of the evaluation results. The recommendation unit recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit. The recommendation unit recommends appropriate dishes and ingredients by considering, for example, the user's preferences and past purchase history. The recommendation unit can recommend appropriate dishes and ingredients by analyzing, for example, the user's preferences and past purchase history. The recommendation unit can recommend appropriate recipes by considering, for example, the user's preferences and past purchase history. As a result, the fresh food evaluation system according to this embodiment improves the quality of shopping by allowing users to check the evaluation results of fresh food and be recommended appropriate dishes and ingredients.

[0030] The evaluation unit uses AI to analyze and evaluate fresh food items when a user points their camera at them. Specifically, when a user takes a picture of fresh food using their smartphone or a dedicated device's camera, the AI ​​uses image analysis technology to analyze the food's color and shape in detail. The AI ​​detects changes in the food's surface color and shape characteristics to evaluate its freshness and quality. For example, it analyzes changes in the color of fruit, the degree of wilting of vegetable leaves, and the color of fat in meat, quantifying freshness and quality. Based on a large amount of pre-trained food image data, the AI ​​can evaluate the condition of food with high accuracy. The evaluation unit provides these analysis results to the user in real time, allowing the user to check the freshness and quality of the food on the spot. Furthermore, the evaluation unit applies different evaluation criteria depending on the type and characteristics of the food to perform more accurate evaluations. For example, by using the optimal evaluation algorithm for each different food category, such as fruits, vegetables, and meats, it achieves evaluations tailored to the characteristics of each food. This allows users to check the freshness and quality of food before purchasing, enabling them to shop with confidence.

[0031] The analysis unit displays a breakdown of the results evaluated by the evaluation unit. Specifically, it displays the freshness and quality evaluation results provided by the evaluation unit as scores, graphs, and charts, allowing users to intuitively understand the evaluation results. For example, the evaluation results can be displayed as a score out of 100 points, allowing users to grasp the freshness and quality of the food at a glance. In addition, the evaluation results can be displayed as pie charts or bar graphs, allowing users to visually check the detailed breakdown of each evaluation item. For example, scores can be displayed for each evaluation item such as color changes, shape characteristics, and surface condition, allowing users to understand which aspects influenced the evaluation. Furthermore, the analysis unit displays the evaluation results in detail, allowing users to understand the breakdown of the evaluation results. For example, it displays specific comments and advice based on the evaluation results, allowing users to learn more about the condition of the food. As a result, users can accurately grasp the freshness and quality of the food based on the evaluation results and make appropriate decisions.

[0032] The recommendation department recommends dishes, necessary ingredients, and recipes based on evaluation results obtained by the analysis department. Specifically, it suggests appropriate dishes and ingredients considering the user's preferences and past purchase history. For example, for fresh vegetables with high evaluation results, it will recommend salad and stir-fry recipes using those vegetables. Even if the evaluation results are low, it will suggest dishes and storage methods that make effective use of food that has lost its freshness. The recommendation department can analyze the user's past purchase history and preferences to suggest the most suitable recipes and ingredients for each individual user. For example, it can suggest recipes that match the user's taste based on data of food that the user has purchased and dishes that the user has made in the past. The recommendation department can also suggest recipes that use seasonal ingredients and local specialties, taking into account seasonal and regional characteristics. This allows users to choose appropriate dishes and ingredients based on evaluation results, improving the quality of their shopping. Furthermore, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of recommendations. For example, it can collect evaluations of dishes that users have actually made and ingredients they have used, and reflect this in future recommendations. This allows the recommendation department to provide optimal suggestions tailored to user needs, thereby improving user satisfaction.

[0033] The evaluation unit includes a feature analysis unit that analyzes features such as color and shape. The evaluation unit can, for example, analyze the color and shape of food to evaluate its freshness and quality. The evaluation unit can, for example, analyze changes in the color and shape features of food to evaluate its freshness and quality. The evaluation unit can, for example, analyze the color and shape of the surface of food to evaluate its freshness and quality. By analyzing the features of color and shape, the accuracy of evaluating fresh food is improved. Some or all of the above-described processing in the feature analysis unit may be performed using AI, for example, or without using AI. For example, the feature analysis unit can input data on the color and shape of food into a generating AI and have the generating AI perform the analysis of the features of color and shape.

[0034] The evaluation unit includes a score display unit that displays the evaluation results as scores. The evaluation unit, for example, displays the evaluation results as scores so that the user can intuitively understand the evaluation results. The evaluation unit, for example, displays the evaluation results as graphs or charts so that the user can visually confirm the evaluation results. The evaluation unit, for example, displays the evaluation results in detail so that the user can understand the breakdown of the evaluation results. In this way, by displaying the evaluation results as scores, the user can intuitively understand the evaluation results. Some or all of the above processing in the score display unit may be performed using AI, for example, or without using AI. For example, the score display unit can input the evaluation result data into a generating AI and cause the generating AI to perform the display as scores.

[0035] The analysis unit includes a feature display unit that displays the characteristics of deliciousness for each type. The analysis unit, for example, displays the characteristics of deliciousness for each type to make it easier for users to understand how to choose fresh food. The analysis unit, for example, displays the characteristics of deliciousness for each type as graphs or charts so that users can understand them visually. The analysis unit, for example, displays the characteristics of deliciousness for each type in detail to make it easier for users to understand. In this way, by displaying the characteristics of deliciousness for each type, it becomes easier for users to understand how to choose fresh food. Some or all of the above processing in the feature display unit may be performed using AI, for example, or without using AI. For example, the feature display unit can input data on the characteristics of deliciousness for each type into a generating AI and have the generating AI perform the display of the characteristics.

[0036] The analysis unit includes an information display unit that displays information on color and shape. The analysis unit, for example, displays information on color and shape so that users can visually confirm the quality of fresh food. The analysis unit, for example, displays information on color and shape as graphs or charts so that users can visually understand it. The analysis unit, for example, displays information on color and shape in detail to make it easy for users to understand. As a result, by displaying information on color and shape, users can visually confirm the quality of fresh food. Some or all of the above processing in the information display unit may be performed using AI, for example, or without AI. For example, the information display unit can input data on color and shape information into a generating AI and have the generating AI perform the display of the information.

[0037] The recommendation unit includes a history analysis unit that considers the user's preferences and past purchase history. The recommendation unit, for example, analyzes the user's preferences and past purchase history and recommends appropriate dishes and ingredients. The recommendation unit can recommend appropriate dishes and ingredients by considering the user's preferences and past purchase history. The recommendation unit can recommend appropriate recipes by considering the user's preferences and past purchase history. This makes it possible to make more appropriate recommendations by considering the user's preferences and past purchase history. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input data of the user's past purchase history into a generating AI and have the generating AI perform the history analysis.

[0038] The recommendation unit includes a recipe display unit that displays recipe details. The recommendation unit, for example, displays recipe details so that users can easily understand the cooking method. The recommendation unit, for example, displays recipe details as graphs or charts so that users can understand them visually. The recommendation unit, for example, displays recipe details in detail to make them easy for users to understand. As a result, by displaying recipe details, users can easily understand the cooking method. Some or all of the above processing in the recipe display unit may be performed using AI, for example, or without AI. For example, the recipe display unit can input recipe detail data into a generating AI and have the generating AI perform the display of the details.

[0039] The evaluation unit analyzes the freshness of fresh food in real time and makes an evaluation based on its freshness. For example, the evaluation unit can analyze the surface temperature of fresh food and evaluate its freshness. For example, the evaluation unit can analyze changes in the color of fresh food and evaluate its freshness. For example, the evaluation unit can detect the smell of fresh food with a sensor and evaluate its freshness. This makes it possible to make an evaluation based on freshness by analyzing the freshness of fresh food in real time. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input freshness data of fresh food into a generating AI and have the generating AI perform the freshness analysis.

[0040] The evaluation unit analyzes the origin information of fresh produce and performs evaluations considering the characteristics of each origin. For example, the evaluation unit can analyze the origin information of fresh produce and evaluate it as a specialty product of that region. For example, the evaluation unit can analyze the climate conditions of the origin of fresh produce and evaluate factors that affect quality. For example, the evaluation unit can analyze the cultivation methods of the origin of fresh produce and evaluate factors that affect quality. In this way, by analyzing the origin information of fresh produce, it becomes possible to perform evaluations that take into account the characteristics of each origin. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the origin information of fresh produce into a generating AI and have the generating AI perform an analysis of the characteristics of each origin.

[0041] The evaluation unit analyzes the nutritional value of fresh food and performs an evaluation based on that nutritional value. For example, the evaluation unit can analyze the vitamin content of fresh food and evaluate its nutritional value. For example, the evaluation unit can analyze the mineral content of fresh food and evaluate its nutritional value. For example, the evaluation unit can analyze the calories of fresh food and evaluate its nutritional value. This makes it possible to perform an evaluation based on nutritional value by analyzing the nutritional value of fresh food. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input nutritional value data of fresh food into a generating AI and have the generating AI perform the nutritional value analysis.

[0042] The evaluation unit analyzes the storage methods of fresh food and performs an evaluation based on those methods. For example, the evaluation unit can analyze the refrigerated storage methods of fresh food and evaluate the storage condition. For example, the evaluation unit can analyze the frozen storage methods of fresh food and evaluate the storage condition. For example, the evaluation unit can analyze the room-temperature storage methods of fresh food and evaluate the storage condition. This makes it possible to perform an evaluation based on the storage method by analyzing the storage methods of fresh food. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input fresh food storage method data into a generating AI and have the generating AI perform the analysis of the storage methods.

[0043] The analysis unit analyzes the component information of fresh food and displays the analysis results based on that information. For example, the analysis unit can analyze the vitamin content of fresh food and display the component information. For example, the analysis unit can analyze the mineral content of fresh food and display the component information. For example, the analysis unit can analyze the calories of fresh food and display the component information. In this way, by analyzing the component information of fresh food, analysis results based on that component information are displayed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the component information data of fresh food into a generating AI and have the generating AI perform the analysis of the component information.

[0044] The analysis unit analyzes the cooking method of fresh food and displays the analysis results based on the cooking method. For example, the analysis unit can analyze how to grill fresh food and display the cooking method. For example, the analysis unit can analyze how to boil fresh food and display the cooking method. For example, the analysis unit can analyze how to deep-fry fresh food and display the cooking method. In this way, by analyzing the cooking method of fresh food, analysis results based on the cooking method are displayed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input fresh food cooking method data into a generating AI and have the generating AI perform the analysis of the cooking method.

[0045] The analysis unit analyzes the shelf life of fresh food and displays the analysis results based on the shelf life. For example, the analysis unit can analyze the refrigerated shelf life of fresh food and display the shelf life. For example, the analysis unit can analyze the frozen shelf life of fresh food and display the shelf life. For example, the analysis unit can analyze the room-temperature shelf life of fresh food and display the shelf life. In this way, by analyzing the shelf life of fresh food, analysis results based on the shelf life are displayed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fresh food shelf life data into a generating AI and have the generating AI perform the shelf life analysis.

[0046] The analysis unit analyzes allergen information of fresh food and displays the analysis results based on the allergen information. For example, the analysis unit can analyze the allergen components of fresh food and display the allergen information. For example, the analysis unit can analyze the allergen risk of fresh food and display the allergen information. For example, the analysis unit can analyze allergen countermeasures for fresh food and display the allergen information. In this way, by analyzing the allergen information of fresh food, analysis results based on the allergen information are displayed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input allergen information data of fresh food into a generating AI and have the generating AI perform the analysis of the allergen information.

[0047] The recommendation unit analyzes the user's family structure and makes recommendations based on that structure. For example, the recommendation unit can analyze the user's family structure and recommend recipes that the whole family can enjoy. For example, the recommendation unit can recommend recipes for children, taking into account the user's family structure. For example, the recommendation unit can recommend recipes for the elderly, taking into account the user's family structure. In this way, by making recommendations based on the user's family structure, it is possible to provide recipes that the whole family can enjoy. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation unit can input the user's family structure data into a generating AI and have the generating AI perform the family structure analysis.

[0048] The recommendation unit analyzes the user's food inventory information and makes recommendations based on that information. For example, the recommendation unit can analyze the user's refrigerator inventory information and recommend recipes that utilize that inventory. For example, the recommendation unit can analyze the user's pantry inventory information and recommend recipes that utilize that inventory. For example, the recommendation unit can analyze the user's freezer inventory information and recommend recipes that utilize that inventory. This allows users to make efficient use of their ingredients by making recommendations based on their food inventory information. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's inventory data into a generating AI and have the generating AI perform the analysis of the inventory information.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The evaluation unit can analyze the freshness of fresh food in real time and perform evaluations based on that freshness. For example, it can analyze the surface temperature of fresh food and evaluate its freshness. It can also analyze changes in the color of fresh food and evaluate its freshness. Furthermore, it can detect the odor of fresh food using sensors and evaluate its freshness. As a result, by analyzing the freshness of fresh food in real time, evaluations based on freshness become possible.

[0051] The evaluation unit can analyze the origin information of fresh produce and conduct evaluations that take into account the characteristics of each production area. For example, it can analyze the origin information of fresh produce and evaluate it as a specialty product of that region. It can also analyze the climatic conditions of the production area of ​​fresh produce and evaluate factors that affect quality. Furthermore, it can analyze the cultivation methods of the production area of ​​fresh produce and evaluate factors that affect quality. In this way, by analyzing the origin information of fresh produce, it becomes possible to conduct evaluations that take into account the characteristics of each production area.

[0052] The evaluation unit can analyze the nutritional value of fresh foods and perform evaluations based on that value. For example, it can analyze the vitamin content of fresh foods and evaluate their nutritional value. It can also analyze the mineral content of fresh foods and evaluate their nutritional value. Furthermore, it can analyze the calories of fresh foods and evaluate their nutritional value. As a result, by analyzing the nutritional value of fresh foods, evaluations based on nutritional value become possible.

[0053] The evaluation unit can analyze the preservation methods of fresh foods and perform evaluations based on those methods. For example, it can analyze the refrigerated preservation methods of fresh foods and evaluate their preservation status. It can also analyze the frozen preservation methods of fresh foods and evaluate their preservation status. Furthermore, it can analyze the room-temperature preservation methods of fresh foods and evaluate their preservation status. This makes it possible to perform evaluations based on preservation methods by analyzing the preservation methods of fresh foods.

[0054] The analysis unit can analyze the component information of fresh food and display the analysis results based on that information. For example, it can analyze the vitamin content of fresh food and display the component information. It can also analyze the mineral content of fresh food and display the component information. Furthermore, it can analyze the calories of fresh food and display the component information. In this way, by analyzing the component information of fresh food, analysis results based on that component information are displayed.

[0055] The analysis unit can analyze the cooking method of fresh food and display the analysis results based on the cooking method. For example, it can analyze how to grill fresh food and display the cooking method. It can also analyze how to boil fresh food and display the cooking method. Furthermore, it can analyze how to deep-fry fresh food and display the cooking method. In this way, by analyzing the cooking method of fresh food, analysis results based on the cooking method are displayed.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: When a user points the camera at fresh food, the AI ​​analyzes and evaluates the food. For example, when a user points the camera at fresh food, the AI ​​uses image analysis technology to analyze the color and shape of the food and evaluate its freshness and quality. The evaluation unit can analyze changes in the color and characteristics of the shape of the food to evaluate its freshness and quality. Step 2: The analysis unit displays a breakdown of the results evaluated by the evaluation unit. For example, it may display the evaluation results as a score so that the user can intuitively understand the results. The analysis unit may display the evaluation results as a graph or chart so that the user can visually confirm the results. The analysis unit may display the evaluation results in detail so that the user can understand the breakdown of the evaluation results. Step 3: The recommendation unit recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit. For example, it recommends appropriate dishes and ingredients by considering the user's preferences and past purchase history. The recommendation unit can analyze the user's preferences and past purchase history and recommend appropriate dishes and ingredients. The recommendation unit can recommend appropriate recipes by considering the user's preferences and past purchase history.

[0058] (Example of form 2) The fresh food evaluation system according to an embodiment of the present invention is an application for people shopping for fresh food, and is a system that visualizes the deliciousness of fresh food using AI. In this system, when a user points the application's camera at fresh food, the AI ​​analyzes the fresh food and assigns a score to each individual item to evaluate its deliciousness. The breakdown of the evaluation is also displayed, and includes, for example, the characteristics of deliciousness for each type (smaller stems mean tastier, yellow mouths on mackerel mean tastier) and color (color of fillets or meat, etc.). Furthermore, based on the fresh food the user is about to buy, the AI ​​recommends suitable dishes, necessary ingredients, and recipes. This function makes it easy for users to find out how to cook the fresh food they have purchased. The application also aims to monetize by displaying display advertisements. Revenue is generated from advertisements displayed when users use the application. In this way, the fresh food evaluation system can improve the quality of shopping by visualizing the deliciousness of fresh food and providing convenient information to users. For example, when a user points the application's camera at fresh produce, the AI ​​analyzes the produce, assigns a score to each individual item, and evaluates its taste. The breakdown of the evaluation is also displayed, including characteristics of taste specific to each type of produce (e.g., smaller stems indicate better taste, yellow mouths on mackerel indicate better taste) and color (e.g., color of fillets or meat). Furthermore, based on the fresh produce the user is considering buying, the AI ​​recommends suitable dishes, necessary ingredients, and recipes. This feature makes it easy for users to learn how to cook the fresh produce they have purchased. The application also monetizes by displaying advertisements. Revenue is generated from ads displayed when users use the application. In this way, the fresh produce evaluation system can improve the quality of shopping by visualizing the taste of fresh produce and providing users with useful information. As a result, the fresh produce evaluation system allows users to easily evaluate the taste of fresh produce and choose appropriate dishes and ingredients.

[0059] The fresh food evaluation system according to this embodiment comprises an evaluation unit, an analysis unit, and a recommendation unit. When a user points a camera at fresh food, the evaluation unit uses AI to analyze and evaluate the food. For example, when a user points a camera at fresh food, the evaluation unit uses image analysis technology to analyze the color and shape of the food and evaluate its freshness and quality. The evaluation unit can, for example, analyze changes in the color and shape characteristics of the food to evaluate its freshness and quality. The evaluation unit can, for example, analyze the color and shape of the food's surface to evaluate its freshness and quality. The analysis unit displays a breakdown of the results evaluated by the evaluation unit. For example, the analysis unit displays the evaluation results as a score so that the user can intuitively understand the evaluation results. For example, the analysis unit displays the evaluation results as a graph or chart so that the user can visually confirm the evaluation results. For example, the analysis unit displays the evaluation results in detail so that the user can understand the breakdown of the evaluation results. The recommendation unit recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit. The recommendation unit recommends appropriate dishes and ingredients by considering, for example, the user's preferences and past purchase history. The recommendation unit can recommend appropriate dishes and ingredients by analyzing, for example, the user's preferences and past purchase history. The recommendation unit can recommend appropriate recipes by considering, for example, the user's preferences and past purchase history. As a result, the fresh food evaluation system according to this embodiment improves the quality of shopping by allowing users to check the evaluation results of fresh food and be recommended appropriate dishes and ingredients.

[0060] The evaluation unit uses AI to analyze and evaluate fresh food items when a user points their camera at them. Specifically, when a user takes a picture of fresh food using their smartphone or a dedicated device's camera, the AI ​​uses image analysis technology to analyze the food's color and shape in detail. The AI ​​detects changes in the food's surface color and shape characteristics to evaluate its freshness and quality. For example, it analyzes changes in the color of fruit, the degree of wilting of vegetable leaves, and the color of fat in meat, quantifying freshness and quality. Based on a large amount of pre-trained food image data, the AI ​​can evaluate the condition of food with high accuracy. The evaluation unit provides these analysis results to the user in real time, allowing the user to check the freshness and quality of the food on the spot. Furthermore, the evaluation unit applies different evaluation criteria depending on the type and characteristics of the food to perform more accurate evaluations. For example, by using the optimal evaluation algorithm for each different food category, such as fruits, vegetables, and meats, it achieves evaluations tailored to the characteristics of each food. This allows users to check the freshness and quality of food before purchasing, enabling them to shop with confidence.

[0061] The analysis unit displays a breakdown of the results evaluated by the evaluation unit. Specifically, it displays the freshness and quality evaluation results provided by the evaluation unit as scores, graphs, and charts, allowing users to intuitively understand the evaluation results. For example, the evaluation results can be displayed as a score out of 100 points, allowing users to grasp the freshness and quality of the food at a glance. In addition, the evaluation results can be displayed as pie charts or bar graphs, allowing users to visually check the detailed breakdown of each evaluation item. For example, scores can be displayed for each evaluation item such as color changes, shape characteristics, and surface condition, allowing users to understand which aspects influenced the evaluation. Furthermore, the analysis unit displays the evaluation results in detail, allowing users to understand the breakdown of the evaluation results. For example, it displays specific comments and advice based on the evaluation results, allowing users to learn more about the condition of the food. As a result, users can accurately grasp the freshness and quality of the food based on the evaluation results and make appropriate decisions.

[0062] The recommendation department recommends dishes, necessary ingredients, and recipes based on evaluation results obtained by the analysis department. Specifically, it suggests appropriate dishes and ingredients considering the user's preferences and past purchase history. For example, for fresh vegetables with high evaluation results, it will recommend salad and stir-fry recipes using those vegetables. Even if the evaluation results are low, it will suggest dishes and storage methods that make effective use of food that has lost its freshness. The recommendation department can analyze the user's past purchase history and preferences to suggest the most suitable recipes and ingredients for each individual user. For example, it can suggest recipes that match the user's taste based on data of food that the user has purchased and dishes that the user has made in the past. The recommendation department can also suggest recipes that use seasonal ingredients and local specialties, taking into account seasonal and regional characteristics. This allows users to choose appropriate dishes and ingredients based on evaluation results, improving the quality of their shopping. Furthermore, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of recommendations. For example, it can collect evaluations of dishes that users have actually made and ingredients they have used, and reflect this in future recommendations. This allows the recommendation department to provide optimal suggestions tailored to user needs, thereby improving user satisfaction.

[0063] The evaluation unit includes a feature analysis unit that analyzes features such as color and shape. The evaluation unit can, for example, analyze the color and shape of food to evaluate its freshness and quality. The evaluation unit can, for example, analyze changes in the color and shape features of food to evaluate its freshness and quality. The evaluation unit can, for example, analyze the color and shape of the surface of food to evaluate its freshness and quality. By analyzing the features of color and shape, the accuracy of evaluating fresh food is improved. Some or all of the above-described processing in the feature analysis unit may be performed using AI, for example, or without using AI. For example, the feature analysis unit can input data on the color and shape of food into a generating AI and have the generating AI perform the analysis of the features of color and shape.

[0064] The evaluation unit includes a score display unit that displays the evaluation results as scores. The evaluation unit, for example, displays the evaluation results as scores so that the user can intuitively understand the evaluation results. The evaluation unit, for example, displays the evaluation results as graphs or charts so that the user can visually confirm the evaluation results. The evaluation unit, for example, displays the evaluation results in detail so that the user can understand the breakdown of the evaluation results. In this way, by displaying the evaluation results as scores, the user can intuitively understand the evaluation results. Some or all of the above processing in the score display unit may be performed using AI, for example, or without using AI. For example, the score display unit can input the evaluation result data into a generating AI and cause the generating AI to perform the display as scores.

[0065] The analysis unit includes a feature display unit that displays the characteristics of deliciousness for each type. The analysis unit, for example, displays the characteristics of deliciousness for each type to make it easier for users to understand how to choose fresh food. The analysis unit, for example, displays the characteristics of deliciousness for each type as graphs or charts so that users can understand them visually. The analysis unit, for example, displays the characteristics of deliciousness for each type in detail to make it easier for users to understand. In this way, by displaying the characteristics of deliciousness for each type, it becomes easier for users to understand how to choose fresh food. Some or all of the above processing in the feature display unit may be performed using AI, for example, or without using AI. For example, the feature display unit can input data on the characteristics of deliciousness for each type into a generating AI and have the generating AI perform the display of the characteristics.

[0066] The analysis unit includes an information display unit that displays information on color and shape. The analysis unit, for example, displays information on color and shape so that users can visually confirm the quality of fresh food. The analysis unit, for example, displays information on color and shape as graphs or charts so that users can visually understand it. The analysis unit, for example, displays information on color and shape in detail to make it easy for users to understand. As a result, by displaying information on color and shape, users can visually confirm the quality of fresh food. Some or all of the above processing in the information display unit may be performed using AI, for example, or without AI. For example, the information display unit can input data on color and shape information into a generating AI and have the generating AI perform the display of the information.

[0067] The recommendation unit includes a history analysis unit that considers the user's preferences and past purchase history. The recommendation unit, for example, analyzes the user's preferences and past purchase history and recommends appropriate dishes and ingredients. The recommendation unit can recommend appropriate dishes and ingredients by considering the user's preferences and past purchase history. The recommendation unit can recommend appropriate recipes by considering the user's preferences and past purchase history. This makes it possible to make more appropriate recommendations by considering the user's preferences and past purchase history. Some or all of the above processing in the history analysis unit may be performed using AI, for example, or without AI. For example, the history analysis unit can input data of the user's past purchase history into a generating AI and have the generating AI perform the history analysis.

[0068] The recommendation unit includes a recipe display unit that displays recipe details. The recommendation unit, for example, displays recipe details so that users can easily understand the cooking method. The recommendation unit, for example, displays recipe details as graphs or charts so that users can understand them visually. The recommendation unit, for example, displays recipe details in detail to make them easy for users to understand. As a result, by displaying recipe details, users can easily understand the cooking method. Some or all of the above processing in the recipe display unit may be performed using AI, for example, or without AI. For example, the recipe display unit can input recipe detail data into a generating AI and have the generating AI perform the display of the details.

[0069] The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit may tighten the evaluation criteria and recommend higher-quality fresh food. If the user is relaxed, the evaluation unit may loosen the evaluation criteria and recommend fresh food that is easily purchased. If the user is in a hurry, the evaluation unit may simplify the evaluation criteria and provide evaluation results quickly. By adjusting the evaluation criteria according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The evaluation unit analyzes the freshness of fresh food in real time and makes an evaluation based on its freshness. For example, the evaluation unit can analyze the surface temperature of fresh food and evaluate its freshness. For example, the evaluation unit can analyze changes in the color of fresh food and evaluate its freshness. For example, the evaluation unit can detect the smell of fresh food with a sensor and evaluate its freshness. This makes it possible to make an evaluation based on freshness by analyzing the freshness of fresh food in real time. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input freshness data of fresh food into a generating AI and have the generating AI perform the freshness analysis.

[0071] The evaluation unit analyzes the origin information of fresh produce and performs evaluations considering the characteristics of each origin. For example, the evaluation unit can analyze the origin information of fresh produce and evaluate it as a specialty product of that region. For example, the evaluation unit can analyze the climate conditions of the origin of fresh produce and evaluate factors that affect quality. For example, the evaluation unit can analyze the cultivation methods of the origin of fresh produce and evaluate factors that affect quality. In this way, by analyzing the origin information of fresh produce, it becomes possible to perform evaluations that take into account the characteristics of each origin. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the origin information of fresh produce into a generating AI and have the generating AI perform an analysis of the characteristics of each origin.

[0072] The evaluation unit estimates the user's emotions and adjusts the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the evaluation unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the evaluation unit can provide a display method that gets straight to the point. By adjusting the display method of the evaluation results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The evaluation unit analyzes the nutritional value of fresh food and performs an evaluation based on that nutritional value. For example, the evaluation unit can analyze the vitamin content of fresh food and evaluate its nutritional value. For example, the evaluation unit can analyze the mineral content of fresh food and evaluate its nutritional value. For example, the evaluation unit can analyze the calories of fresh food and evaluate its nutritional value. This makes it possible to perform an evaluation based on nutritional value by analyzing the nutritional value of fresh food. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input nutritional value data of fresh food into a generating AI and have the generating AI perform the nutritional value analysis.

[0074] The evaluation unit analyzes the storage methods of fresh food and performs an evaluation based on those methods. For example, the evaluation unit can analyze the refrigerated storage methods of fresh food and evaluate the storage condition. For example, the evaluation unit can analyze the frozen storage methods of fresh food and evaluate the storage condition. For example, the evaluation unit can analyze the room-temperature storage methods of fresh food and evaluate the storage condition. This makes it possible to perform an evaluation based on the storage method by analyzing the storage methods of fresh food. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input fresh food storage method data into a generating AI and have the generating AI perform the analysis of the storage methods.

[0075] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit analyzes the component information of fresh food and displays the analysis results based on that information. For example, the analysis unit can analyze the vitamin content of fresh food and display the component information. For example, the analysis unit can analyze the mineral content of fresh food and display the component information. For example, the analysis unit can analyze the calories of fresh food and display the component information. In this way, by analyzing the component information of fresh food, analysis results based on that component information are displayed. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the component information data of fresh food into a generating AI and have the generating AI perform the analysis of the component information.

[0077] The analysis unit analyzes the cooking method of fresh food and displays the analysis results based on the cooking method. For example, the analysis unit can analyze how to grill fresh food and display the cooking method. For example, the analysis unit can analyze how to boil fresh food and display the cooking method. For example, the analysis unit can analyze how to deep-fry fresh food and display the cooking method. In this way, by analyzing the cooking method of fresh food, analysis results based on the cooking method are displayed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input fresh food cooking method data into a generating AI and have the generating AI perform the analysis of the cooking method.

[0078] The analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit may prioritize displaying important information. For example, if the user is relaxed, the analysis unit may prioritize displaying detailed information. For example, if the user is in a hurry, the analysis unit may prioritize displaying concise information. In this way, by prioritizing the analysis results according to the user's emotions, more appropriate information is displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit analyzes the shelf life of fresh food and displays the analysis results based on the shelf life. For example, the analysis unit can analyze the refrigerated shelf life of fresh food and display the shelf life. For example, the analysis unit can analyze the frozen shelf life of fresh food and display the shelf life. For example, the analysis unit can analyze the room-temperature shelf life of fresh food and display the shelf life. In this way, by analyzing the shelf life of fresh food, analysis results based on the shelf life are displayed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fresh food shelf life data into a generating AI and have the generating AI perform the shelf life analysis.

[0080] The analysis unit analyzes allergen information of fresh food and displays the analysis results based on the allergen information. For example, the analysis unit can analyze the allergen components of fresh food and display the allergen information. For example, the analysis unit can analyze the allergen risk of fresh food and display the allergen information. For example, the analysis unit can analyze allergen countermeasures for fresh food and display the allergen information. In this way, by analyzing the allergen information of fresh food, analysis results based on the allergen information are displayed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input allergen information data of fresh food into a generating AI and have the generating AI perform the analysis of the allergen information.

[0081] The recommendation unit estimates the user's emotions and adjusts the recommendations based on the estimated emotions. For example, if the user is relaxed, the recommendation unit may recommend recipes that proceed at a leisurely pace. If the user is in a hurry, the recommendation unit may recommend recipes that can be cooked in a short time. If the user is excited, the recommendation unit may recommend visually stimulating recipes. By adjusting the recommendations according to the user's emotions, more appropriate recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The recommendation unit analyzes the user's family structure and makes recommendations based on that structure. For example, the recommendation unit can analyze the user's family structure and recommend recipes that the whole family can enjoy. For example, the recommendation unit can recommend recipes for children, taking into account the user's family structure. For example, the recommendation unit can recommend recipes for the elderly, taking into account the user's family structure. In this way, by making recommendations based on the user's family structure, it is possible to provide recipes that the whole family can enjoy. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation unit can input the user's family structure data into a generating AI and have the generating AI perform the family structure analysis.

[0083] The recommendation unit analyzes the user's food inventory information and makes recommendations based on that information. For example, the recommendation unit can analyze the user's refrigerator inventory information and recommend recipes that utilize that inventory. For example, the recommendation unit can analyze the user's pantry inventory information and recommend recipes that utilize that inventory. For example, the recommendation unit can analyze the user's freezer inventory information and recommend recipes that utilize that inventory. This allows users to make efficient use of their ingredients by making recommendations based on their food inventory information. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's inventory data into a generating AI and have the generating AI perform the analysis of the inventory information.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is stressed, the evaluation criteria can be made stricter to recommend higher-quality fresh food. Conversely, if the user is relaxed, the evaluation criteria can be relaxed to recommend fresh food that is easily purchased. Furthermore, if the user is in a hurry, the evaluation criteria can be simplified to provide evaluation results quickly. In this way, adjusting the evaluation criteria according to the user's emotions enables more appropriate evaluations.

[0086] The evaluation unit can analyze the freshness of fresh food in real time and perform evaluations based on that freshness. For example, it can analyze the surface temperature of fresh food and evaluate its freshness. It can also analyze changes in the color of fresh food and evaluate its freshness. Furthermore, it can detect the odor of fresh food using sensors and evaluate its freshness. As a result, by analyzing the freshness of fresh food in real time, evaluations based on freshness become possible.

[0087] The evaluation unit can analyze the origin information of fresh produce and conduct evaluations that take into account the characteristics of each production area. For example, it can analyze the origin information of fresh produce and evaluate it as a specialty product of that region. It can also analyze the climatic conditions of the production area of ​​fresh produce and evaluate factors that affect quality. Furthermore, it can analyze the cultivation methods of the production area of ​​fresh produce and evaluate factors that affect quality. In this way, by analyzing the origin information of fresh produce, it becomes possible to conduct evaluations that take into account the characteristics of each production area.

[0088] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible 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 gets straight to the point can be provided. In this way, by adjusting the display method of the evaluation results according to the user's emotions, a more appropriate display becomes possible.

[0089] The evaluation unit can analyze the nutritional value of fresh foods and perform evaluations based on that value. For example, it can analyze the vitamin content of fresh foods and evaluate their nutritional value. It can also analyze the mineral content of fresh foods and evaluate their nutritional value. Furthermore, it can analyze the calories of fresh foods and evaluate their nutritional value. As a result, by analyzing the nutritional value of fresh foods, evaluations based on nutritional value become possible.

[0090] The evaluation unit can analyze the preservation methods of fresh foods and perform evaluations based on those methods. For example, it can analyze the refrigerated preservation methods of fresh foods and evaluate their preservation status. It can also analyze the frozen preservation methods of fresh foods and evaluate their preservation status. Furthermore, it can analyze the room-temperature preservation methods of fresh foods and evaluate their preservation status. This makes it possible to perform evaluations based on preservation methods by analyzing the preservation methods of fresh foods.

[0091] 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 nervous, a simple and highly visible 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 essentials can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible.

[0092] The analysis unit can analyze the component information of fresh food and display the analysis results based on that information. For example, it can analyze the vitamin content of fresh food and display the component information. It can also analyze the mineral content of fresh food and display the component information. Furthermore, it can analyze the calories of fresh food and display the component information. In this way, by analyzing the component information of fresh food, analysis results based on that component information are displayed.

[0093] The analysis unit can analyze the cooking method of fresh food and display the analysis results based on the cooking method. For example, it can analyze how to grill fresh food and display the cooking method. It can also analyze how to boil fresh food and display the cooking method. Furthermore, it can analyze how to deep-fry fresh food and display the cooking method. In this way, by analyzing the cooking method of fresh food, analysis results based on the cooking method are displayed.

[0094] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on those emotions. For example, if the user is stressed, important information can be displayed preferentially. If the user is relaxed, detailed information can be displayed preferentially. Furthermore, if the user is in a hurry, concise information can be displayed preferentially. In this way, by determining the priority of analysis results according to the user's emotions, more appropriate information is displayed preferentially.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: When a user points the camera at fresh food, the AI ​​analyzes and evaluates the food. For example, when a user points the camera at fresh food, the AI ​​uses image analysis technology to analyze the color and shape of the food and evaluate its freshness and quality. The evaluation unit can analyze changes in the color and characteristics of the shape of the food to evaluate its freshness and quality. Step 2: The analysis unit displays a breakdown of the results evaluated by the evaluation unit. For example, it may display the evaluation results as a score so that the user can intuitively understand the results. The analysis unit may display the evaluation results as a graph or chart so that the user can visually confirm the results. The analysis unit may display the evaluation results in detail so that the user can understand the breakdown of the evaluation results. Step 3: The recommendation unit recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit. For example, it recommends appropriate dishes and ingredients by considering the user's preferences and past purchase history. The recommendation unit can analyze the user's preferences and past purchase history and recommend appropriate dishes and ingredients. The recommendation unit can recommend appropriate recipes by considering the user's preferences and past purchase history.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the evaluation unit, analysis unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit acquires images of fresh food using the camera 42 of the smart device 14 and performs analysis using the processor 46. The analysis unit displays the evaluation results using, for example, the specific processing unit 290 of the data processing unit 12. The recommendation unit suggests appropriate dishes and ingredients to the user using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the evaluation unit, analysis unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit acquires images of fresh food using the camera 42 of the smart glasses 214 and performs analysis using the processor 46. The analysis unit displays the evaluation results, for example, using the specific processing unit 290 of the data processing unit 12. The recommendation unit suggests appropriate dishes and ingredients to the user, for example, using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the evaluation unit, analysis unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit acquires images of fresh food using the camera 42 of the headset terminal 314 and performs analysis using the processor 46. The analysis unit displays the evaluation results using the specific processing unit 290 of the data processing unit 12. The recommendation unit suggests appropriate dishes and ingredients to the user using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the evaluation unit, analysis unit, and recommendation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit acquires images of fresh food using the camera 42 of the robot 414 and performs analysis using the processor 46. The analysis unit displays the evaluation results, for example, using the specific processing unit 290 of the data processing unit 12. The recommendation unit suggests appropriate dishes and ingredients to the user, for example, using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) When a user points a camera at fresh food, an evaluation unit uses AI to analyze and evaluate the food, An analysis unit that displays a breakdown of the results evaluated by the evaluation unit, The system includes a recommendation unit that recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit. A system characterized by the following features. (Note 2) The evaluation unit, It includes a feature analysis unit that analyzes features such as color and shape. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, It includes a score display unit that displays the evaluation results as a score. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It is equipped with a feature display section that shows the characteristics of the deliciousness of each type. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It is equipped with an information display unit that displays information about color and shape. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recommendation unit is, It includes a history analysis unit that takes into account user preferences and past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recommendation unit is, It features a recipe display section that shows the details of the recipe. The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit, The freshness of perishable foods is analyzed in real time, and evaluations are made based on that freshness. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit, We analyze the origin information of fresh produce and evaluate it while considering the characteristics of each production area. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, Analyze the nutritional value of fresh foods and evaluate them based on their nutritional value. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, We will analyze methods for preserving fresh food and evaluate them based on those methods. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, This software analyzes the ingredient information of fresh foods and displays the analysis results based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, This software analyzes the cooking methods of fresh foods and displays the analysis results based on those methods. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, This tool analyzes the shelf life of fresh food products and displays the analysis results based on the shelf life. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, This system analyzes allergen information in fresh foods and displays the analysis results based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, It estimates the user's emotions and adjusts recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, The system analyzes the user's family structure and provides recommendations based on that structure. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, The system analyzes the user's food inventory information and provides recommendations based on that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. When a user points a camera at fresh food, an evaluation unit analyzes and evaluates the food using AI. An analysis unit that displays a breakdown of the results evaluated by the evaluation unit, The system includes a recommendation unit that recommends suitable dishes, necessary ingredients, and recipes based on the evaluation results obtained by the analysis unit. A system characterized by the following features.

2. The evaluation unit, It includes a feature analysis unit that analyzes features such as color and shape. The system according to feature 1.

3. The evaluation unit, It includes a score display unit that displays the evaluation results as a score. The system according to feature 1.

4. The aforementioned analysis unit, It is equipped with a feature display section that shows the characteristics of the deliciousness of each type. The system according to feature 1.

5. The aforementioned analysis unit, It is equipped with an information display unit that displays information about color and shape. The system according to feature 1.

6. The recommendation unit is, It includes a history analysis unit that takes into account user preferences and past purchase history. The system according to feature 1.

7. The recommendation unit is, It features a recipe display section that shows the details of the recipe. The system according to feature 1.

8. The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system according to feature 1.

9. The evaluation unit, The freshness of perishable foods is analyzed in real time, and evaluations are made based on that freshness. The system according to feature 1.

10. The evaluation unit, We analyze the origin information of fresh produce and evaluate it while considering the characteristics of each production area. The system according to feature 1.

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