System

Abstract

To provide a system according to an embodiment that proposes an efficient recipe considering a change in price of foodstuffs and food materials in season.SOLUTION: A system comprises a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects time-series data on the prices of foodstuffs and recipe data. The analysis unit analyzes the data collected by the collection unit and specifies food materials in season. The proposal unit proposes a recipe making the most of the food materials in season specified by the analysis unit. The optimization unit optimizes the distribution of the foodstuffs on the basis of the recipe proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not provide sufficient efficient recipe suggestions that take into account food price fluctuations and seasonal ingredients, and there is room for improvement.

[0005] The system according to the embodiment aims to propose efficient recipes that take into account fluctuations in food prices and seasonal ingredients. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects food price time series data and recipe data. The analysis unit analyzes the data collected by the collection unit and identifies seasonal ingredients. The proposal unit proposes recipes that utilize the seasonal ingredients identified by the analysis unit. The optimization unit optimizes food allocation based on the recipes proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can suggest efficient recipes that take into account fluctuations in food prices and seasonal ingredients. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The recipe suggestion system according to the embodiment of the present invention is a system that analyzes food price time series data and recipe data, and proposes economically efficient recipes that make use of seasonal ingredients. In this recipe suggestion system, food price time series data and recipe data from Kurashiru are input into a generation AI, which analyzes these data to identify seasonal ingredients. Furthermore, the generation AI proposes economically efficient recipes that make use of the identified seasonal ingredients. This proposal targets ordinary households, restaurants, and retail stores, and contributes to improving product quality and planning sales strategies. In addition, by promoting the optimization of food allocation, food waste can be reduced and contribute to the SDGs. For example, food price time series data and recipe data from Kurashiru are input into the generation AI. At this time, the price data is detailed data including past price trends, and the recipe data includes detailed information such as the type of ingredients and cooking methods. For example, price trend data for tomatoes over the past year and recipe data using tomatoes are input. Next, the generation AI analyzes the input data and identifies seasonal ingredients. The generation AI analyzes the fluctuation pattern of the price data and identifies ingredients whose prices fall at a specific time as seasonal ingredients. For example, if the price of tomatoes falls in summer, it is determined that summer is the season for tomatoes. Furthermore, the generation AI proposes economically efficient recipes that make use of the identified seasonal ingredients. The generation AI analyzes recipe data and selects recipes that use seasonal ingredients. For example, it proposes recipes for salads and pasta that use tomatoes in summer. This proposal targets general households, restaurants, and retail stores, and contributes to improving product quality and planning sales strategies. For example, restaurants can provide high-quality dishes while reducing costs by offering menus that use seasonal ingredients. In addition, retail stores can increase sales by actively selling seasonal ingredients. Furthermore, by optimizing the allocation of food, food waste can be reduced and contribute to the SDGs. The generation AI considers the balance between supply and demand of ingredients and proposes the appropriate amount of ingredients. This prevents excessive purchasing and waste, and reduces food waste. As a result, the recipe proposal system can analyze food price time series data and recipe data, and propose economically efficient recipes that make use of seasonal ingredients.

[0029] The recipe suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an optimization unit. The collection unit collects food price time series data and recipe data. The collection unit collects, for example, past price transition data and recipe data. For example, the collection unit can collect tomato price transition data for the past year and recipe data using tomatoes. The analysis unit analyzes the data collected by the collection unit to identify seasonal ingredients. The analysis unit analyzes, for example, a fluctuation pattern of price data and identifies ingredients whose prices drop at a specific time as seasonal ingredients. For example, if the price of tomatoes drops in summer, the analysis unit can identify that summer is the season for tomatoes. The suggestion unit proposes recipes that make use of the seasonal ingredients identified by the analysis unit. The suggestion unit analyzes, for example, recipe data and selects recipes that use seasonal ingredients. For example, the suggestion unit can propose recipes for salads and pasta that use tomatoes in summer. The optimization unit optimizes the allocation of food ingredients based on the recipes proposed by the suggestion unit. For example, the optimization unit considers the balance between demand and supply of ingredients and proposes appropriate amounts of ingredients. For example, the optimization unit can prevent excessive purchases and waste, and reduce food loss. As a result, the recipe proposal system according to the embodiment can analyze food price time series data and recipe data, and propose economically efficient recipes that make use of seasonal ingredients. Some or all of the above-mentioned processes in the collection unit, analysis unit, proposal unit, and optimization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit inputs food price time series data and recipe data to the generation AI, and the generation AI can analyze these data to identify seasonal ingredients. The proposal unit can propose recipes that make use of seasonal ingredients identified by the generation AI. The optimization unit can optimize the allocation of food ingredients based on the recipes proposed by the generation AI.

[0030] The collection unit collects food price time series data and recipe data. Specifically, the collection unit collects past price transition data and recipe data from various sources. For example, the collection unit can collect price databases of supermarkets and online grocery stores, and recipe data from cooking sites and recipe apps. This allows the collection unit to collect tomato price transition data for the past year and recipe data using tomatoes. Furthermore, the collection unit can collect data according to a specific region or season by adjusting the frequency and range of data collection. For example, by focusing on collecting price data from supermarkets in a specific region, it is possible to grasp the price fluctuation pattern specific to that region. In addition, the collection unit can check the consistency and consistency of the data to ensure the quality of the data, and perform processing to exclude inaccurate data and missing data. This allows the collection unit to collect reliable data and provide a foundation for the analysis unit and proposal unit to perform accurate analysis and proposals.

[0031] The analysis unit analyzes the data collected by the collection unit and identifies seasonal ingredients. Specifically, the analysis unit analyzes the fluctuation pattern of the price data and identifies ingredients whose prices fall at a specific time as seasonal ingredients. For example, if the price of tomatoes falls in summer, it can be identified that summer is the season for tomatoes. The analysis unit uses AI to analyze the data and extract trends and patterns of price fluctuations. The AI ​​can learn from past price data and predict seasonal price fluctuations. Furthermore, the analysis unit can use not only price data but also other related data such as weather data and harvest data for analysis. This allows the analysis unit to more accurately identify seasonal ingredients. For example, by analyzing weather data, it is possible to evaluate the impact of specific weather conditions on the price of ingredients and use it as a reference when identifying seasonal ingredients. Furthermore, the analysis unit can analyze not only past data but also real-time data. This allows the analysis unit to identify seasonal ingredients that reflect the current market situation.

[0032] The suggestion unit suggests recipes that make use of the seasonal ingredients identified by the analysis unit. Specifically, the suggestion unit analyzes the recipe data and selects recipes that use seasonal ingredients. For example, the suggestion unit can suggest salad and pasta recipes that use tomatoes in the summer. The suggestion unit can analyze the recipe data using AI and automatically select recipes that use seasonal ingredients. The AI ​​can learn from the recipe database and suggest optimal recipes that take into account the user's preferences and nutritional balance from among recipes that use seasonal ingredients. Furthermore, the suggestion unit can make personalized recipe suggestions based on the user's past selections and ratings. For example, the suggestion unit can analyze recipes that the user has given high ratings in the past and the user's tendency to prefer specific ingredients, and suggest recipes based on that. In addition to suggesting recipes, the suggestion unit can also provide related information such as cooking methods and methods for preserving ingredients. This allows the suggestion unit to support the user in making the most of seasonal ingredients.

[0033] The optimization unit optimizes the allocation of food ingredients based on the recipes proposed by the proposal unit. Specifically, the optimization unit considers the balance between the demand and supply of ingredients and proposes an appropriate amount of ingredients. For example, the optimization unit can prevent excessive purchase and waste and reduce food loss. The optimization unit can use AI to predict demand for ingredients and calculate an appropriate purchase amount. The AI ​​can learn past demand data and current market conditions and predict future demand. Furthermore, the optimization unit can consider the entire supply chain and optimize the supplier and delivery schedule of ingredients. This allows the optimization unit to efficiently manage the supply of ingredients and reduce costs. In addition, the optimization unit can make individualized proposals by considering the user's purchase history and preferences. For example, the optimization unit can propose an appropriate amount of ingredients based on ingredients and recipes purchased by the user in the past. This allows the optimization unit to support the user in using ingredients without waste. Furthermore, the optimization unit can update data in real time and quickly respond to fluctuations in demand and supply. This allows the optimization unit to always make optimal proposals based on the latest information, thereby reducing food loss and improving economic efficiency.

[0034] The collection unit can collect past price transition data or recipe data. The collection unit, for example, collects past price transition data. For example, the collection unit can collect price transition data for the past year. The collection unit can also collect recipe data. For example, the collection unit can collect recipe data from Classy. In this way, the collection unit can perform more accurate analysis by collecting past price transition data and recipe data.

[0035] The analysis unit analyzes the fluctuation pattern of the price data and can identify ingredients whose prices fall at specific times as seasonal ingredients. The analysis unit, for example, analyzes the fluctuation pattern of the price data. For example, the analysis unit can analyze the fluctuation pattern of the price data and identify ingredients whose prices fall at specific times as seasonal ingredients. For example, if the price of tomatoes falls in summer, the analysis unit can identify that summer is the season for tomatoes. In this way, the analysis unit can identify seasonal ingredients by analyzing the fluctuation pattern of the price data.

[0036] The suggestion unit can analyze the recipe data and select recipes that use seasonal ingredients. The suggestion unit, for example, analyzes the recipe data. For example, the suggestion unit can analyze the recipe data and select recipes that use seasonal ingredients. For example, the suggestion unit can suggest salad and pasta recipes that use tomatoes in the summer. In this way, the suggestion unit can select recipes that use seasonal ingredients by analyzing the recipe data.

[0037] The optimization unit is capable of proposing the necessary amount of ingredients by taking into account the balance between demand and supply of ingredients. The optimization unit is capable of proposing the necessary amount of ingredients by taking into account the balance between demand and supply of ingredients, for example. The optimization unit is capable of proposing the necessary amount of ingredients by taking into account the balance between demand and supply of ingredients. In this way, the optimization unit is capable of proposing an appropriate amount of ingredients by taking into account the balance between demand and supply of ingredients.

[0038] The proposal department targets general households or restaurants and retail stores, and can contribute to improving product quality and formulating sales strategies. The proposal department targets general households, for example. For example, the proposal department targets general households, and can contribute to improving product quality and formulating sales strategies. The proposal department can also target restaurants and retail stores. For example, the proposal department can target restaurants and retail stores, and contribute to improving product quality and formulating sales strategies. In this way, the proposal department can contribute to improving product quality and formulating sales strategies by targeting general households, restaurants and retail stores.

[0039] The collection unit can collect food price data in real time, enabling instantaneous reflection of price fluctuations. The collection unit, for example, collects food price data in real time. For example, the collection unit can collect price data in real time through the API of each retail store. The collection unit can also periodically obtain price data from online marketplaces and instantaneously reflect the data. Furthermore, the collection unit can collect market prices in each region in real time and reflect the data in the database. This allows the collection unit to instantly reflect price fluctuations by collecting price data in real time.

[0040] When collecting recipe data, the collection unit can preferentially collect highly relevant recipes based on the user's past cooking history. The collection unit, for example, preferentially collects highly relevant recipes taking into account the user's past cooking history. For example, the collection unit can preferentially collect highly relevant recipes based on recipes created by the user in the past. The collection unit can also preferentially collect recipes that use specific ingredients from the user's past cooking history. Furthermore, the collection unit can analyze the user's past cooking history and preferentially collect recipes that include a preferred cooking method. In this way, the collection unit can preferentially collect highly relevant recipes by taking into account the user's past cooking history.

[0041] The collection unit can collect price data of food products while taking into account price differences between regions. For example, the collection unit can collect market prices in each region and reflect the price differences between regions in the database. The collection unit can also collect price data for each region through the API of each retail store. Furthermore, the collection unit can also classify and collect price data from online marketplaces by region. This enables the collection unit to collect more accurate price data by taking into account price differences between regions.

[0042] When collecting recipe data, the collection unit can preferentially collect safe recipes taking into consideration the user's ingredient allergy information. The collection unit, for example, preferentially collects safe recipes taking into consideration the user's ingredient allergy information. For example, the collection unit can preferentially collect safe recipes based on the user's allergy information. The collection unit can also preferentially collect recipes that do not contain specific ingredients taking into consideration the user's allergy information. Furthermore, the collection unit can analyze the user's allergy information and preferentially collect recipes that include safe cooking methods. In this way, the collection unit can preferentially collect safe recipes by taking into consideration the user's ingredient allergy information.

[0043] The analysis unit can improve the analysis accuracy based on past weather data when analyzing the fluctuation pattern of the price data. For example, the analysis unit takes into account past weather data when analyzing the fluctuation pattern of the price data. For example, the analysis unit can identify the factors of price fluctuations based on past weather data and improve the analysis accuracy. The analysis unit can also analyze the price fluctuation pattern by combining weather data and price data. Furthermore, the analysis unit can also analyze price fluctuations under specific weather conditions by taking into account the weather data. In this way, the analysis unit can improve the analysis accuracy by taking into account past weather data.

[0044] During the analysis, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients. The analysis unit, for example, can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients. For example, the analysis unit can identify healthy, seasonal ingredients based on the nutritional value data of the ingredients. The analysis unit can also identify healthy, seasonal ingredients by combining nutritional value data and price data. Furthermore, the analysis unit can also identify seasonal ingredients that contain a lot of specific nutrients by taking into account the nutritional value data. In this way, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients.

[0045] The analysis unit can improve the analysis accuracy based on the transportation cost data of ingredients when analyzing the fluctuation pattern of the price data. For example, the analysis unit takes into account the transportation cost data of ingredients when analyzing the fluctuation pattern of the price data. For example, the analysis unit can identify the cause of the price fluctuation based on the transportation cost data of ingredients and improve the analysis accuracy. The analysis unit can also analyze the price fluctuation pattern by combining the transportation cost data and the price data. Furthermore, the analysis unit can also analyze the price fluctuation under specific transportation conditions by taking into account the transportation cost data. In this way, the analysis unit can improve the analysis accuracy by taking into account the transportation cost data of ingredients.

[0046] During the analysis, the analysis unit can identify seasonal ingredients that can be stored for a long time by taking into account the storage period data of the ingredients. The analysis unit, for example, identifies seasonal ingredients that can be stored for a long time by taking into account the storage period data of the ingredients. For example, the analysis unit can identify seasonal ingredients that can be stored for a long time based on the storage period data of the ingredients. The analysis unit can also identify seasonal ingredients that can be stored for a long time by combining the storage period data and price data. Furthermore, the analysis unit can also identify seasonal ingredients under specific storage conditions by taking into account the storage period data. In this way, the analysis unit can identify seasonal ingredients that can be stored for a long time by taking into account the storage period data of the ingredients.

[0047] When suggesting a recipe, the suggestion unit can suggest an optimal recipe by taking into consideration the user's past cooking history. The suggestion unit, for example, suggests an optimal recipe by taking into consideration the user's past cooking history. For example, the suggestion unit can suggest highly relevant recipes based on the user's past cooking history. The suggestion unit can also suggest recipes that use specific ingredients from the user's past cooking history. Furthermore, the suggestion unit can analyze the user's past cooking history and suggest recipes that include a preferred cooking method. In this way, the suggestion unit can suggest an optimal recipe by taking into consideration the user's past cooking history.

[0048] When proposing a recipe, the suggestion unit can suggest a health-conscious recipe by taking into consideration the nutritional value data of ingredients. The suggestion unit, for example, proposes a health-conscious recipe by taking into consideration the nutritional value data of ingredients. For example, the suggestion unit can suggest a health-conscious recipe based on the nutritional value data of ingredients. The suggestion unit can also combine nutritional value data and price data to suggest a healthy recipe. Furthermore, the suggestion unit can also suggest a recipe that is rich in a specific nutrient by taking into consideration the nutritional value data. In this way, the suggestion unit can suggest a health-conscious recipe by taking into consideration the nutritional value data of ingredients.

[0049] When proposing a recipe, the suggestion unit can suggest a safe recipe by taking into consideration the user's ingredient allergy information. The suggestion unit, for example, proposes a safe recipe by taking into consideration the user's ingredient allergy information. For example, the suggestion unit can suggest a safe recipe based on the user's allergy information. The suggestion unit can also suggest a recipe that does not contain a specific ingredient by taking into consideration the user's allergy information. Furthermore, the suggestion unit can analyze the user's allergy information and suggest a recipe that includes a safe cooking method. In this way, the suggestion unit can suggest a safe recipe by taking into consideration the user's ingredient allergy information.

[0050] When proposing a recipe, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user. For example, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user. For example, the suggestion unit can suggest a recipe with an appropriate difficulty level based on the cooking skill level of the user. In addition, the suggestion unit can also suggest a recipe for beginners by taking into account the cooking skill level of the user. Furthermore, the suggestion unit can analyze the cooking skill level of the user and suggest a recipe for advanced users. In this way, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user.

[0051] During optimization, the optimization unit can perform optimization to minimize waste by taking into account the storage period data of ingredients. The optimization unit performs optimization to minimize waste by taking into account, for example, the storage period data of ingredients. For example, the optimization unit can propose an optimal allocation that minimizes waste based on the storage period data of ingredients. The optimization unit can also combine the storage period data and demand data to perform optimal allocation that minimizes waste. Furthermore, the optimization unit can also propose an optimal allocation under specific storage conditions by taking into account the storage period data. This enables the optimization unit to perform optimization to minimize waste by taking into account the storage period data of ingredients.

[0052] During optimization, the optimization unit can perform health-conscious optimization by taking into account the nutritional value data of ingredients. The optimization unit performs health-conscious optimization by taking into account, for example, the nutritional value data of ingredients. For example, the optimization unit can propose an optimal health-conscious allocation based on the nutritional value data of ingredients. The optimization unit can also combine nutritional value data and demand data to perform an optimal health-friendly allocation. Furthermore, the optimization unit can also propose an optimal allocation that contains a large amount of a specific nutrient by taking into account the nutritional value data. This enables the optimization unit to perform health-conscious optimization by taking into account the nutritional value data of ingredients.

[0053] During optimization, the optimization unit can perform cost-efficient optimization by taking into account the transportation cost data of ingredients. The optimization unit performs cost-efficient optimization by taking into account, for example, the transportation cost data of ingredients. For example, the optimization unit can propose a cost-efficient optimal allocation based on the transportation cost data of ingredients. The optimization unit can also combine the transportation cost data and demand data to perform cost-efficient optimal allocation. Furthermore, the optimization unit can also propose an optimal allocation under specific transportation conditions by taking into account the transportation cost data. This enables the optimization unit to perform cost-efficient optimization by taking into account the transportation cost data of ingredients.

[0054] During optimization, the optimization unit can perform optimal allocation for each region by taking into account the geographical distribution data of ingredients. The optimization unit performs optimal allocation for each region by taking into account, for example, the geographical distribution data of ingredients. For example, the optimization unit can propose optimal allocation for each region based on the geographical distribution data of ingredients. The optimization unit can also combine the geographical distribution data and demand data to perform optimal allocation for each region. Furthermore, the optimization unit can also propose optimal allocation under specific regional conditions by taking into account the geographical distribution data. This enables the optimization unit to perform optimal allocation for each region by taking into account the geographical distribution data of ingredients.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The recipe suggestion system may further include a learning unit that learns the user's ingredient preferences. The learning unit collects data on recipes and ingredients previously selected by the user, and learns the user's preferences. For example, it can analyze ingredients and cooking methods frequently selected by the user, and suggest recipes that suit the user's preferences. The learning unit can also suggest appropriate recipes taking into consideration the user's ingredient allergy information and health condition. Furthermore, the learning unit can also manage inventory based on the user's ingredient purchase history, and promote the use of ingredients without waste. In this way, the recipe suggestion system can provide a more personalized service by suggesting recipes that match the user's preferences and health condition.

[0057] The collection unit can preferentially collect highly relevant recipes based on the user's ingredient purchase history. For example, the collection unit analyzes data on ingredients previously purchased by the user and collects highly relevant recipes. The collection unit can also preferentially collect recipes that use specific ingredients based on the user's purchase history. Furthermore, the collection unit can also preferentially collect recipes that include a preferred cooking method based on the user's purchase history. In this way, the collection unit can preferentially collect highly relevant recipes by taking into account the user's purchase history.

[0058] When analyzing the fluctuation pattern of the price data, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients. For example, the analysis unit can identify healthy, seasonal ingredients based on the nutritional value data of the ingredients. The analysis unit can also identify healthy, seasonal ingredients by combining the nutritional value data and the price data. Furthermore, the analysis unit can also identify seasonal ingredients that contain a lot of a specific nutrient by taking into account the nutritional value data. In this way, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients.

[0059] When analyzing the fluctuation pattern of the price data, the analysis unit can improve the analysis accuracy based on past weather data. For example, the analysis unit identifies the factors of the price fluctuation based on the past weather data, and improves the analysis accuracy. The analysis unit can also analyze the price fluctuation pattern by combining the weather data and the price data. Furthermore, the analysis unit can also analyze the price fluctuation under specific weather conditions by taking the weather data into account. In this way, the analysis unit can improve the analysis accuracy by taking the past weather data into account.

[0060] When proposing a recipe, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user. For example, the suggestion unit can suggest a recipe with an appropriate difficulty level based on the cooking skill level of the user. The suggestion unit can also suggest a recipe for beginners by taking into account the cooking skill level of the user. Furthermore, the suggestion unit can analyze the cooking skill level of the user and suggest a recipe for advanced users. In this way, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user.

[0061] During optimization, the optimization unit can perform optimization to minimize waste by taking into account the storage period data of ingredients. For example, the optimization unit can propose an optimal allocation that minimizes waste based on the storage period data of ingredients. The optimization unit can also perform optimal allocation to minimize waste by combining the storage period data and demand data. Furthermore, the optimization unit can also propose an optimal allocation under specific storage conditions by taking into account the storage period data. This enables the optimization unit to perform optimization to minimize waste by taking into account the storage period data of ingredients.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects food price time series data and recipe data. For example, it can collect price trend data for tomatoes over the past year and recipe data using tomatoes. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies seasonal ingredients. For example, the analysis unit analyzes the fluctuation pattern of price data and identifies ingredients whose prices fall at a specific time as seasonal ingredients. For example, if the price of tomatoes falls in summer, it can be determined that summer is the season for tomatoes. Step 3: The suggestion unit proposes recipes that utilize the seasonal ingredients identified by the analysis unit. For example, the suggestion unit analyzes the recipe data and selects recipes that use seasonal ingredients. For example, it can propose recipes for salads and pasta that use tomatoes in the summer. Step 4: The optimization unit optimizes the allocation of food ingredients based on the recipe proposed by the suggestion unit. For example, it considers the balance between supply and demand for ingredients and suggests the appropriate amount of ingredients. For example, it can prevent excessive purchasing and waste and reduce food loss.

[0064] (Example 2) The recipe suggestion system according to the embodiment of the present invention is a system that analyzes food price time series data and recipe data, and proposes economically efficient recipes that make use of seasonal ingredients. In this recipe suggestion system, food price time series data and recipe data from Kurashiru are input into a generation AI, which analyzes these data to identify seasonal ingredients. Furthermore, the generation AI proposes economically efficient recipes that make use of the identified seasonal ingredients. This proposal targets ordinary households, restaurants, and retail stores, and contributes to improving product quality and planning sales strategies. In addition, by promoting the optimization of food allocation, food waste can be reduced and contribute to the SDGs. For example, food price time series data and recipe data from Kurashiru are input into the generation AI. At this time, the price data is detailed data including past price trends, and the recipe data includes detailed information such as the type of ingredients and cooking methods. For example, price trend data for tomatoes over the past year and recipe data using tomatoes are input. Next, the generation AI analyzes the input data and identifies seasonal ingredients. The generation AI analyzes the fluctuation pattern of the price data and identifies ingredients whose prices fall at a specific time as seasonal ingredients. For example, if the price of tomatoes falls in summer, it is determined that summer is the season for tomatoes. Furthermore, the generation AI proposes economically efficient recipes that make use of the identified seasonal ingredients. The generation AI analyzes recipe data and selects recipes that use seasonal ingredients. For example, it proposes recipes for salads and pasta that use tomatoes in summer. This proposal targets general households, restaurants, and retail stores, and contributes to improving product quality and planning sales strategies. For example, restaurants can provide high-quality dishes while reducing costs by offering menus that use seasonal ingredients. In addition, retail stores can increase sales by actively selling seasonal ingredients. Furthermore, by optimizing the allocation of food, food waste can be reduced and contribute to the SDGs. The generation AI considers the balance between supply and demand of ingredients and proposes the appropriate amount of ingredients. This prevents excessive purchasing and waste, and reduces food waste. As a result, the recipe proposal system can analyze food price time series data and recipe data, and propose economically efficient recipes that make use of seasonal ingredients.

[0065] The recipe suggestion system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and an optimization unit. The collection unit collects food price time series data and recipe data. The collection unit collects, for example, past price transition data and recipe data. For example, the collection unit can collect tomato price transition data for the past year and recipe data using tomatoes. The analysis unit analyzes the data collected by the collection unit to identify seasonal ingredients. The analysis unit analyzes, for example, a fluctuation pattern of price data and identifies ingredients whose prices drop at a specific time as seasonal ingredients. For example, if the price of tomatoes drops in summer, the analysis unit can identify that summer is the season for tomatoes. The suggestion unit proposes recipes that make use of the seasonal ingredients identified by the analysis unit. The suggestion unit analyzes, for example, recipe data and selects recipes that use seasonal ingredients. For example, the suggestion unit can propose recipes for salads and pasta that use tomatoes in summer. The optimization unit optimizes the allocation of food ingredients based on the recipes proposed by the suggestion unit. For example, the optimization unit considers the balance between demand and supply of ingredients and proposes appropriate amounts of ingredients. For example, the optimization unit can prevent excessive purchases and waste, and reduce food loss. As a result, the recipe proposal system according to the embodiment can analyze food price time series data and recipe data, and propose economically efficient recipes that make use of seasonal ingredients. Some or all of the above-mentioned processes in the collection unit, analysis unit, proposal unit, and optimization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit inputs food price time series data and recipe data to the generation AI, and the generation AI can analyze these data to identify seasonal ingredients. The proposal unit can propose recipes that make use of seasonal ingredients identified by the generation AI. The optimization unit can optimize the allocation of food ingredients based on the recipes proposed by the generation AI.

[0066] The collection unit collects food price time series data and recipe data. Specifically, the collection unit collects past price transition data and recipe data from various sources. For example, the collection unit can collect price databases of supermarkets and online grocery stores, and recipe data from cooking sites and recipe apps. This allows the collection unit to collect tomato price transition data for the past year and recipe data using tomatoes. Furthermore, the collection unit can collect data according to a specific region or season by adjusting the frequency and range of data collection. For example, by focusing on collecting price data from supermarkets in a specific region, it is possible to grasp the price fluctuation pattern specific to that region. In addition, the collection unit can check the consistency and consistency of the data to ensure the quality of the data, and perform processing to exclude inaccurate data and missing data. This allows the collection unit to collect reliable data and provide a foundation for the analysis unit and proposal unit to perform accurate analysis and proposals.

[0067] The analysis unit analyzes the data collected by the collection unit and identifies seasonal ingredients. Specifically, the analysis unit analyzes the fluctuation pattern of the price data and identifies ingredients whose prices fall at a specific time as seasonal ingredients. For example, if the price of tomatoes falls in summer, it can be identified that summer is the season for tomatoes. The analysis unit uses AI to analyze the data and extract trends and patterns of price fluctuations. The AI ​​can learn from past price data and predict seasonal price fluctuations. Furthermore, the analysis unit can use not only price data but also other related data such as weather data and harvest data for analysis. This allows the analysis unit to more accurately identify seasonal ingredients. For example, by analyzing weather data, it is possible to evaluate the impact of specific weather conditions on the price of ingredients and use it as a reference when identifying seasonal ingredients. Furthermore, the analysis unit can analyze not only past data but also real-time data. This allows the analysis unit to identify seasonal ingredients that reflect the current market situation.

[0068] The suggestion unit suggests recipes that make use of the seasonal ingredients identified by the analysis unit. Specifically, the suggestion unit analyzes the recipe data and selects recipes that use seasonal ingredients. For example, the suggestion unit can suggest salad and pasta recipes that use tomatoes in the summer. The suggestion unit can analyze the recipe data using AI and automatically select recipes that use seasonal ingredients. The AI ​​can learn from the recipe database and suggest optimal recipes that take into account the user's preferences and nutritional balance from among recipes that use seasonal ingredients. Furthermore, the suggestion unit can make personalized recipe suggestions based on the user's past selections and ratings. For example, the suggestion unit can analyze recipes that the user has given high ratings in the past and the user's tendency to prefer specific ingredients, and suggest recipes based on that. In addition to suggesting recipes, the suggestion unit can also provide related information such as cooking methods and methods for preserving ingredients. This allows the suggestion unit to support the user in making the most of seasonal ingredients.

[0069] The optimization unit optimizes the allocation of food ingredients based on the recipes proposed by the proposal unit. Specifically, the optimization unit considers the balance between the demand and supply of ingredients and proposes an appropriate amount of ingredients. For example, the optimization unit can prevent excessive purchase and waste and reduce food loss. The optimization unit can use AI to predict demand for ingredients and calculate an appropriate purchase amount. The AI ​​can learn past demand data and current market conditions and predict future demand. Furthermore, the optimization unit can consider the entire supply chain and optimize the supplier and delivery schedule of ingredients. This allows the optimization unit to efficiently manage the supply of ingredients and reduce costs. In addition, the optimization unit can make individualized proposals by considering the user's purchase history and preferences. For example, the optimization unit can propose an appropriate amount of ingredients based on ingredients and recipes purchased by the user in the past. This allows the optimization unit to support the user in using ingredients without waste. Furthermore, the optimization unit can update data in real time and quickly respond to fluctuations in demand and supply. This allows the optimization unit to always make optimal proposals based on the latest information, thereby reducing food loss and improving economic efficiency.

[0070] The collection unit can collect past price transition data or recipe data. The collection unit, for example, collects past price transition data. For example, the collection unit can collect price transition data for the past year. The collection unit can also collect recipe data. For example, the collection unit can collect recipe data from Classy. In this way, the collection unit can perform more accurate analysis by collecting past price transition data and recipe data.

[0071] The analysis unit analyzes the fluctuation pattern of the price data and can identify ingredients whose prices fall at specific times as seasonal ingredients. The analysis unit, for example, analyzes the fluctuation pattern of the price data. For example, the analysis unit can analyze the fluctuation pattern of the price data and identify ingredients whose prices fall at specific times as seasonal ingredients. For example, if the price of tomatoes falls in summer, the analysis unit can identify that summer is the season for tomatoes. In this way, the analysis unit can identify seasonal ingredients by analyzing the fluctuation pattern of the price data.

[0072] The suggestion unit can analyze the recipe data and select recipes that use seasonal ingredients. The suggestion unit, for example, analyzes the recipe data. For example, the suggestion unit can analyze the recipe data and select recipes that use seasonal ingredients. For example, the suggestion unit can suggest salad and pasta recipes that use tomatoes in the summer. In this way, the suggestion unit can select recipes that use seasonal ingredients by analyzing the recipe data.

[0073] The optimization unit is capable of proposing the necessary amount of ingredients by taking into account the balance between demand and supply of ingredients. The optimization unit is capable of proposing the necessary amount of ingredients by taking into account the balance between demand and supply of ingredients, for example. The optimization unit is capable of proposing the necessary amount of ingredients by taking into account the balance between demand and supply of ingredients. In this way, the optimization unit is capable of proposing an appropriate amount of ingredients by taking into account the balance between demand and supply of ingredients.

[0074] The proposal department targets general households or restaurants and retail stores, and can contribute to improving product quality and formulating sales strategies. The proposal department targets general households, for example. For example, the proposal department targets general households, and can contribute to improving product quality and formulating sales strategies. The proposal department can also target restaurants and retail stores. For example, the proposal department can target restaurants and retail stores, and contribute to improving product quality and formulating sales strategies. In this way, the proposal department can contribute to improving product quality and formulating sales strategies by targeting general households, restaurants and retail stores.

[0075] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, the collection unit provides a simple and intuitive data collection interface and prioritizes important data collection when the user is stressed. In addition, the collection unit can provide detailed data collection options and collect a wide range of data when the user is relaxed. Furthermore, the collection unit can provide a shortcut to quickly collect the most important data when the user is in a hurry. This allows the collection unit to determine the priority of data based on the user's emotion, thereby enabling more appropriate data collection. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] The collection unit can collect food price data in real time, enabling instantaneous reflection of price fluctuations. The collection unit, for example, collects food price data in real time. For example, the collection unit can collect price data in real time through the API of each retail store. The collection unit can also periodically obtain price data from online marketplaces and instantaneously reflect the data. Furthermore, the collection unit can collect market prices in each region in real time and reflect the data in the database. This allows the collection unit to instantly reflect price fluctuations by collecting price data in real time.

[0077] When collecting recipe data, the collection unit can preferentially collect highly relevant recipes based on the user's past cooking history. The collection unit, for example, preferentially collects highly relevant recipes taking into account the user's past cooking history. For example, the collection unit can preferentially collect highly relevant recipes based on recipes created by the user in the past. The collection unit can also preferentially collect recipes that use specific ingredients from the user's past cooking history. Furthermore, the collection unit can analyze the user's past cooking history and preferentially collect recipes that include a preferred cooking method. In this way, the collection unit can preferentially collect highly relevant recipes by taking into account the user's past cooking history.

[0078] The collection unit can estimate the user's emotion and filter the data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit can estimate the user's emotion and filter the data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit filters and collects only important data. In addition, when the user is relaxed, the collection unit can also collect detailed data without filtering. Furthermore, when the user is in a hurry, the collection unit can quickly filter and collect the most important data. This allows the collection unit to filter data based on the user's emotion, thereby enabling more appropriate data collection. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The collection unit can collect price data of food products while taking into account price differences between regions. For example, the collection unit can collect market prices in each region and reflect the price differences between regions in the database. The collection unit can also collect price data for each region through the API of each retail store. Furthermore, the collection unit can also classify and collect price data from online marketplaces by region. This enables the collection unit to collect more accurate price data by taking into account price differences between regions.

[0080] When collecting recipe data, the collection unit can preferentially collect safe recipes taking into consideration the user's ingredient allergy information. The collection unit, for example, preferentially collects safe recipes taking into consideration the user's ingredient allergy information. For example, the collection unit can preferentially collect safe recipes based on the user's allergy information. The collection unit can also preferentially collect recipes that do not contain specific ingredients taking into consideration the user's allergy information. Furthermore, the collection unit can analyze the user's allergy information and preferentially collect recipes that include safe cooking methods. In this way, the collection unit can preferentially collect safe recipes by taking into consideration the user's ingredient allergy information.

[0081] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. In addition, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to provide more appropriate information by adjusting the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The analysis unit can improve the analysis accuracy based on past weather data when analyzing the fluctuation pattern of the price data. For example, the analysis unit takes into account past weather data when analyzing the fluctuation pattern of the price data. For example, the analysis unit can identify the factors of price fluctuations based on past weather data and improve the analysis accuracy. The analysis unit can also analyze the price fluctuation pattern by combining weather data and price data. Furthermore, the analysis unit can also analyze price fluctuations under specific weather conditions by taking into account the weather data. In this way, the analysis unit can improve the analysis accuracy by taking into account past weather data.

[0083] During the analysis, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients. The analysis unit, for example, can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients. For example, the analysis unit can identify healthy, seasonal ingredients based on the nutritional value data of the ingredients. The analysis unit can also identify healthy, seasonal ingredients by combining nutritional value data and price data. Furthermore, the analysis unit can also identify seasonal ingredients that contain a lot of specific nutrients by taking into account the nutritional value data. In this way, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients.

[0084] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, the analysis unit preferentially displays important analysis results when the user is stressed. In addition, the analysis unit can also display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can quickly display the most important analysis results when the user is in a hurry. This allows the analysis unit to determine the priority of the analysis results based on the user's emotion, thereby enabling more appropriate information provision. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The analysis unit can improve the analysis accuracy based on the transportation cost data of ingredients when analyzing the fluctuation pattern of the price data. For example, the analysis unit takes into account the transportation cost data of ingredients when analyzing the fluctuation pattern of the price data. For example, the analysis unit can identify the cause of the price fluctuation based on the transportation cost data of ingredients and improve the analysis accuracy. The analysis unit can also analyze the price fluctuation pattern by combining the transportation cost data and the price data. Furthermore, the analysis unit can also analyze the price fluctuation under specific transportation conditions by taking into account the transportation cost data. In this way, the analysis unit can improve the analysis accuracy by taking into account the transportation cost data of ingredients.

[0086] During the analysis, the analysis unit can identify seasonal ingredients that can be stored for a long time by taking into account the storage period data of the ingredients. The analysis unit, for example, identifies seasonal ingredients that can be stored for a long time by taking into account the storage period data of the ingredients. For example, the analysis unit can identify seasonal ingredients that can be stored for a long time based on the storage period data of the ingredients. The analysis unit can also identify seasonal ingredients that can be stored for a long time by combining the storage period data and price data. Furthermore, the analysis unit can also identify seasonal ingredients under specific storage conditions by taking into account the storage period data. In this way, the analysis unit can identify seasonal ingredients that can be stored for a long time by taking into account the storage period data of the ingredients.

[0087] The suggestion unit can estimate the user's emotion and adjust the expression method of the recipe to be proposed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the expression method of the recipe to be proposed based on the estimated user's emotion. For example, the suggestion unit proposes a simple and highly visible recipe when the user is nervous. In addition, the suggestion unit can also propose a recipe including detailed information when the user is relaxed. Furthermore, the suggestion unit can also propose a recipe with the main points when the user is in a hurry. This allows the suggestion unit to adjust the expression method of the recipe based on the user's emotion, thereby making it possible to propose a more appropriate recipe. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] When suggesting a recipe, the suggestion unit can suggest an optimal recipe by taking into consideration the user's past cooking history. The suggestion unit, for example, suggests an optimal recipe by taking into consideration the user's past cooking history. For example, the suggestion unit can suggest highly relevant recipes based on the user's past cooking history. The suggestion unit can also suggest recipes that use specific ingredients from the user's past cooking history. Furthermore, the suggestion unit can analyze the user's past cooking history and suggest recipes that include a preferred cooking method. In this way, the suggestion unit can suggest an optimal recipe by taking into consideration the user's past cooking history.

[0089] When proposing a recipe, the suggestion unit can suggest a health-conscious recipe by taking into consideration the nutritional value data of ingredients. The suggestion unit, for example, proposes a health-conscious recipe by taking into consideration the nutritional value data of ingredients. For example, the suggestion unit can suggest a health-conscious recipe based on the nutritional value data of ingredients. The suggestion unit can also combine nutritional value data and price data to suggest a healthy recipe. Furthermore, the suggestion unit can also suggest a recipe that is rich in a specific nutrient by taking into consideration the nutritional value data. In this way, the suggestion unit can suggest a health-conscious recipe by taking into consideration the nutritional value data of ingredients.

[0090] The suggestion unit can estimate the user's emotion and determine the priority order of the recipes to be proposed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion and determine the priority order of the recipes to be proposed based on the estimated user's emotion. For example, the suggestion unit preferentially suggests recipes that are easy and can be made easily when the user is stressed. In addition, the suggestion unit can also suggest recipes that can be made in a long time when the user is relaxed. Furthermore, the suggestion unit can also preferentially suggest recipes that can be made in a short time when the user is in a hurry. This allows the suggestion unit to determine the priority order of recipes based on the user's emotion, thereby enabling more appropriate recipe proposal. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] When proposing a recipe, the suggestion unit can suggest a safe recipe by taking into consideration the user's ingredient allergy information. The suggestion unit, for example, proposes a safe recipe by taking into consideration the user's ingredient allergy information. For example, the suggestion unit can suggest a safe recipe based on the user's allergy information. The suggestion unit can also suggest a recipe that does not contain a specific ingredient by taking into consideration the user's allergy information. Furthermore, the suggestion unit can analyze the user's allergy information and suggest a recipe that includes a safe cooking method. In this way, the suggestion unit can suggest a safe recipe by taking into consideration the user's ingredient allergy information.

[0092] When proposing a recipe, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user. For example, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user. For example, the suggestion unit can suggest a recipe with an appropriate difficulty level based on the cooking skill level of the user. In addition, the suggestion unit can also suggest a recipe for beginners by taking into account the cooking skill level of the user. Furthermore, the suggestion unit can analyze the cooking skill level of the user and suggest a recipe for advanced users. In this way, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user.

[0093] The optimization unit can estimate the user's emotion and adjust the optimization method based on the estimated user's emotion. The optimization unit, for example, estimates the user's emotion. For example, the optimization unit can estimate the user's emotion and adjust the optimization method based on the estimated user's emotion. For example, the optimization unit provides a simple and intuitive optimization method when the user is nervous. The optimization unit can also provide detailed optimization options when the user is relaxed. Furthermore, the optimization unit can provide a shortcut for quickly performing optimization when the user is in a hurry. This allows the optimization unit to adjust the optimization method based on the user's emotion, thereby enabling more appropriate optimization. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] During optimization, the optimization unit can perform optimization to minimize waste by taking into account the storage period data of ingredients. The optimization unit performs optimization to minimize waste by taking into account, for example, the storage period data of ingredients. For example, the optimization unit can propose an optimal allocation that minimizes waste based on the storage period data of ingredients. The optimization unit can also combine the storage period data and demand data to perform optimal allocation that minimizes waste. Furthermore, the optimization unit can also propose an optimal allocation under specific storage conditions by taking into account the storage period data. This enables the optimization unit to perform optimization to minimize waste by taking into account the storage period data of ingredients.

[0095] During optimization, the optimization unit can perform health-conscious optimization by taking into account the nutritional value data of ingredients. The optimization unit performs health-conscious optimization by taking into account, for example, the nutritional value data of ingredients. For example, the optimization unit can propose an optimal health-conscious allocation based on the nutritional value data of ingredients. The optimization unit can also combine nutritional value data and demand data to perform an optimal health-friendly allocation. Furthermore, the optimization unit can also propose an optimal allocation that contains a large amount of a specific nutrient by taking into account the nutritional value data. This enables the optimization unit to perform health-conscious optimization by taking into account the nutritional value data of ingredients.

[0096] The optimization unit can estimate the user's emotion and determine the priority of optimization based on the estimated user's emotion. The optimization unit, for example, estimates the user's emotion. For example, the optimization unit can estimate the user's emotion and determine the priority of optimization based on the estimated user's emotion. For example, the optimization unit prioritizes important optimization items when the user is stressed. In addition, the optimization unit can also process detailed optimization items when the user is relaxed. Furthermore, the optimization unit can quickly process the most important optimization items when the user is in a hurry. This enables the optimization unit to determine the priority of optimization based on the user's emotion, thereby enabling more appropriate optimization. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] During optimization, the optimization unit can perform cost-efficient optimization by taking into account the transportation cost data of ingredients. The optimization unit performs cost-efficient optimization by taking into account, for example, the transportation cost data of ingredients. For example, the optimization unit can propose a cost-efficient optimal allocation based on the transportation cost data of ingredients. The optimization unit can also combine the transportation cost data and demand data to perform cost-efficient optimal allocation. Furthermore, the optimization unit can also propose an optimal allocation under specific transportation conditions by taking into account the transportation cost data. This enables the optimization unit to perform cost-efficient optimization by taking into account the transportation cost data of ingredients.

[0098] During optimization, the optimization unit can perform optimal allocation for each region by taking into account the geographical distribution data of ingredients. The optimization unit performs optimal allocation for each region by taking into account, for example, the geographical distribution data of ingredients. For example, the optimization unit can propose optimal allocation for each region based on the geographical distribution data of ingredients. The optimization unit can also combine the geographical distribution data and demand data to perform optimal allocation for each region. Furthermore, the optimization unit can also propose optimal allocation under specific regional conditions by taking into account the geographical distribution data. This enables the optimization unit to perform optimal allocation for each region by taking into account the geographical distribution data of ingredients.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The recipe suggestion system may further include a learning unit that learns the user's ingredient preferences. The learning unit collects data on recipes and ingredients previously selected by the user, and learns the user's preferences. For example, it can analyze ingredients and cooking methods frequently selected by the user, and suggest recipes that suit the user's preferences. The learning unit can also suggest appropriate recipes taking into consideration the user's ingredient allergy information and health condition. Furthermore, the learning unit can also manage inventory based on the user's ingredient purchase history, and promote the use of ingredients without waste. In this way, the recipe suggestion system can provide a more personalized service by suggesting recipes that match the user's preferences and health condition.

[0101] The collection unit can preferentially collect highly relevant recipes based on the user's ingredient purchase history. For example, the collection unit analyzes data on ingredients previously purchased by the user and collects highly relevant recipes. The collection unit can also preferentially collect recipes that use specific ingredients based on the user's purchase history. Furthermore, the collection unit can also preferentially collect recipes that include a preferred cooking method based on the user's purchase history. In this way, the collection unit can preferentially collect highly relevant recipes by taking into account the user's purchase history.

[0102] When analyzing the fluctuation pattern of the price data, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients. For example, the analysis unit can identify healthy, seasonal ingredients based on the nutritional value data of the ingredients. The analysis unit can also identify healthy, seasonal ingredients by combining the nutritional value data and the price data. Furthermore, the analysis unit can also identify seasonal ingredients that contain a lot of a specific nutrient by taking into account the nutritional value data. In this way, the analysis unit can identify healthy, seasonal ingredients by taking into account the nutritional value data of the ingredients.

[0103] The suggestion unit can estimate the user's emotions and adjust the way in which a recipe is presented based on the estimated user's emotions. For example, when the user is nervous, the suggestion unit can suggest a simple recipe with high visibility. When the user is relaxed, the suggestion unit can also suggest a recipe that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also suggest a recipe that focuses on the main points. This allows the suggestion unit to suggest more appropriate recipes by adjusting the way in which a recipe is presented based on the user's emotions.

[0104] The optimization unit can estimate the user's emotion and adjust the optimization method based on the estimated user's emotion. For example, the optimization unit can provide a simple and intuitive optimization method when the user is nervous. The optimization unit can also provide detailed optimization options when the user is relaxed. Furthermore, the optimization unit can provide a shortcut for quick optimization when the user is in a hurry. This allows the optimization unit to adjust the optimization method based on the user's emotion, thereby enabling more appropriate optimization.

[0105] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit provides a simple and intuitive data collection interface and prioritizes the collection of important data. When the user is relaxed, the collection unit can also provide detailed data collection options and collect a wide range of data. Furthermore, when the user is in a hurry, the collection unit can provide a shortcut to quickly collect the most important data. This allows the collection unit to determine the priority of data based on the user's emotion, thereby enabling more appropriate data collection.

[0106] When analyzing the fluctuation pattern of the price data, the analysis unit can improve the analysis accuracy based on past weather data. For example, the analysis unit identifies the factors of the price fluctuation based on the past weather data, and improves the analysis accuracy. The analysis unit can also analyze the price fluctuation pattern by combining the weather data and the price data. Furthermore, the analysis unit can also analyze the price fluctuation under specific weather conditions by taking the weather data into account. In this way, the analysis unit can improve the analysis accuracy by taking the past weather data into account.

[0107] When proposing a recipe, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user. For example, the suggestion unit can suggest a recipe with an appropriate difficulty level based on the cooking skill level of the user. The suggestion unit can also suggest a recipe for beginners by taking into account the cooking skill level of the user. Furthermore, the suggestion unit can analyze the cooking skill level of the user and suggest a recipe for advanced users. In this way, the suggestion unit can suggest a recipe according to the difficulty level by taking into account the cooking skill level of the user.

[0108] During optimization, the optimization unit can perform optimization to minimize waste by taking into account the storage period data of ingredients. For example, the optimization unit can propose an optimal allocation that minimizes waste based on the storage period data of ingredients. The optimization unit can also perform optimal allocation to minimize waste by combining the storage period data and demand data. Furthermore, the optimization unit can also propose an optimal allocation under specific storage conditions by taking into account the storage period data. This enables the optimization unit to perform optimization to minimize waste by taking into account the storage period data of ingredients.

[0109] The suggestion unit can estimate the user's emotions and determine the priority order of recipes to be suggested based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit can preferentially suggest recipes that are simple and easy to make. When the user is relaxed, the suggestion unit can also suggest recipes that can be made over time. Furthermore, when the user is in a hurry, the suggestion unit can also preferentially suggest recipes that can be made in a short time. This allows the suggestion unit to suggest more appropriate recipes by determining the priority order of recipes based on the user's emotions.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The collection unit collects food price time series data and recipe data. For example, it can collect price trend data for tomatoes over the past year and recipe data using tomatoes. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies seasonal ingredients. For example, the analysis unit analyzes the fluctuation pattern of price data and identifies ingredients whose prices fall at a specific time as seasonal ingredients. For example, if the price of tomatoes falls in summer, it can be determined that summer is the season for tomatoes. Step 3: The suggestion unit proposes recipes that utilize the seasonal ingredients identified by the analysis unit. For example, the suggestion unit analyzes the recipe data and selects recipes that use seasonal ingredients. For example, it can propose recipes for salads and pasta that use tomatoes in the summer. Step 4: The optimization unit optimizes the allocation of food ingredients based on the recipe proposed by the suggestion unit. For example, it considers the balance between supply and demand for ingredients and suggests the appropriate amount of ingredients. For example, it can prevent excessive purchasing and waste and reduce food loss.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and optimization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14, and collects food price time series data and recipe data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify seasonal ingredients. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes recipes that make use of seasonal ingredients. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the allocation of food items based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and optimization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214, and collects food price time series data and recipe data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify seasonal ingredients. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes recipes that make use of seasonal ingredients. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the allocation of food items based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and optimization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and collects food price time series data and recipe data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify seasonal ingredients. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes recipes that make use of seasonal ingredients. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the allocation of food items based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and optimization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects food price time series data and recipe data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify seasonal ingredients. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes recipes that make use of seasonal ingredients. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the allocation of food items based on the proposed recipe. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) A collection unit that collects food price time series data and recipe data; an analysis unit that analyzes the data collected by the collection unit and identifies seasonal ingredients; A suggestion unit that proposes recipes that utilize seasonal ingredients identified by the analysis unit; and an optimization unit that optimizes the allocation of food items based on the recipe suggested by the suggestion unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect historical price trends or recipe data 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing patterns of price fluctuations in price data and identifying ingredients whose prices fall at certain times as in-season ingredients 2. The system of claim 1, (Appendix 4) The suggestion unit, Analyze recipe data and select recipes that use seasonal ingredients 2. The system of claim 1, (Appendix 5) The optimization unit is Considering the balance between supply and demand for ingredients, we propose the necessary amount of ingredients 2. The system of claim 1, (Appendix 6) The suggestion unit, Contribute to improving product quality and formulating sales strategies targeting general households, restaurants, and retail stores. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 8) The collecting unit includes: Collecting food price data in real time, enabling price fluctuations to be reflected immediately 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting recipe data, prioritize collection of highly relevant recipes based on the user's past cooking history. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user emotions and filter data to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting food price data, take into account price differences by region. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting recipe data, prioritize collecting safe recipes by considering the user's food allergy information. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing fluctuation patterns in price data, improve the accuracy of the analysis based on past weather data 2. The system of claim 1, (Appendix 15) The analysis unit is During the analysis, the nutritional value data of ingredients is taken into account to identify healthy, seasonal ingredients. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing fluctuation patterns in price data, improve the accuracy of analysis based on food transportation cost data. 2. The system of claim 1, (Appendix 18) The analysis unit is During the analysis, food storage period data is taken into account to identify seasonal foods that can be stored for a long time. 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggested recipes based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, When suggesting recipes, the system considers the user's past cooking history to suggest the most suitable recipes. 2. The system of claim 1, (Appendix 21) The suggestion unit, When suggesting recipes, the system takes into account the nutritional value data of ingredients to suggest healthier recipes. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and prioritize recipe suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, When suggesting recipes, the system takes into account the user's food allergy information to suggest safe recipes. 2. The system of claim 1, (Appendix 24) The suggestion unit, When suggesting recipes, the system takes into account the user's cooking skill level and suggests recipes according to their difficulty level. 2. The system of claim 1, (Appendix 25) The optimization unit is Infer user sentiment and adjust optimization methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The optimization unit is During optimization, food storage life data is taken into account to optimize for minimal waste. 2. The system of claim 1, (Appendix 27) The optimization unit is During optimization, the nutritional value data of ingredients is taken into account to optimize for health. 2. The system of claim 1, (Appendix 28) The optimization unit is Infer user sentiment and prioritize optimizations based on the inferred user sentiment 2. The system of claim 1, (Appendix 29) The optimization unit is During optimization, food transportation cost data is taken into account for cost-effective optimization. 2. The system of claim 1, (Appendix 30) The optimization unit is During optimization, the geographic distribution data of ingredients is taken into account to optimize allocation by region. 2. The system of claim 1, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects food price time series data and recipe data; an analysis unit that analyzes the data collected by the collection unit and identifies seasonal ingredients; A suggestion unit that proposes recipes that utilize seasonal ingredients identified by the analysis unit; and an optimization unit that optimizes the allocation of food items based on the recipe suggested by the suggestion unit. A system characterized by:

2. The collecting unit includes: Collect historical price trends or recipe data 2. The system of claim 1 .

3. The analysis unit is Analyzing patterns of price fluctuations in price data and identifying ingredients whose prices fall at certain times as in-season ingredients 2. The system of claim 1 .

4. The suggestion unit, Analyze recipe data and select recipes that use seasonal ingredients 2. The system of claim 1 .

5. The optimization unit is Considering the balance between supply and demand for ingredients, we propose the necessary amount of ingredients 2. The system of claim 1 .

6. The suggestion unit, Contribute to improving product quality and formulating sales strategies targeting general households, restaurants, and retail stores.

2. The system of claim 1 .

7. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

8. The collecting unit includes: Collecting food price data in real time, enabling price fluctuations to be reflected immediately 2. The system of claim 1 .

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