System

Abstract

An object of a system according to an embodiment is to propose an optimal recipe based on a user's preference and allergy information and provide a cooking procedure even in a busy modern society.SOLUTION: A system includes an input unit, a proposal unit, a generation unit, and a provision unit. The input part inputs food material or taste and allergy information. The proposal unit proposes a recipe based on the information input by the input unit. The generation unit generates a cooking procedure based on the recipe proposed by the proposal unit. The provision unit provides the procedure generated by the generation 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, in today's busy society, a lack of cooking ideas and recipes, as well as time constraints, can limit the variety and quality of cooking.

[0005] The system according to the embodiment aims to propose optimal recipes based on the user's preferences and allergy information and provide cooking instructions, even in today's busy society. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a suggestion unit, a generation unit, and a provision unit. The input unit inputs ingredients, preferences, and allergy information. The suggestion unit proposes recipes based on the information input by the input unit. The generation unit generates cooking steps based on the recipe proposed by the suggestion unit. The provision unit provides the steps generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal recipes based on the user's preferences and allergy information and provide cooking instructions, even in today's busy society. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A cooking assistance system according to an embodiment of the present invention proposes optimal recipes and provides cooking instructions based on ingredients, preferences, and allergy information entered by a user. The cooking assistance system allows users to input information about ingredients, preferences, and allergies, and a generation AI analyzes this information to propose optimal recipes. Furthermore, the system provides cooking instructions in the form of video and text based on the proposed recipe. For example, the cooking assistance system allows users to input information about ingredients in the refrigerator, their preferences, and allergy information. For example, users input information such as "I have chicken, broccoli, and carrots," "I like spicy food," and "I have a nut allergy." This information is then input into the generation AI. The generation AI then analyzes the input information and proposes optimal recipes for the user. The generation AI generates healthy and balanced recipes based on the input ingredients, preferences, and allergy information. For example, recipes such as "stir-fried chicken and broccoli" and "carrot and chicken soup" are proposed. The cooking assistance system then generates videos and text that provide detailed instructions for the cooking steps based on the proposed recipe. For example, cooking instructions such as "cut the chicken into bite-sized pieces," "boil the broccoli," and "thinly slice the carrots" are provided via video and text. This allows the cooking assistance system to easily prepare healthy and balanced meals. This allows users to overcome problems such as a lack of cooking ideas and recipes, and time constraints, and to enjoy cooking every day. For example, in today's busy society, even people who are busy with work and housework can easily prepare healthy meals.

[0029] A cooking assistance system according to an embodiment includes an input unit, a suggestion unit, a generation unit, and a provision unit. The input unit allows a user to input ingredients, preferences, and allergy information. The information input by the user includes, but is not limited to, ingredients in the refrigerator, taste preferences, and allergy information. For example, the input unit allows a user to manually input ingredients in the refrigerator. The input unit can also input ingredient information using barcode scanning. Furthermore, the input unit can also input ingredient information using voice input. For example, the ingredient information can be automatically set by simply inputting "chicken, broccoli, carrots" by voice. The suggestion unit suggests a recipe based on the information input by the input unit. The suggestion unit, for example, uses a generation AI to analyze the input ingredients, preferences, and allergy information and suggest an optimal recipe. The suggestion unit suggests a recipe taking into consideration, for example, nutritional balance, cooking time, difficulty, and the like. The generation unit generates cooking steps based on the recipe suggested by the suggestion unit. For example, the generation unit uses a generation AI to generate a video or text that explains the cooking steps in detail based on the proposed recipe. The generation unit can also generate a new recipe based on, for example, past recipe data. The provision unit provides the steps generated by the generation unit. The provision unit provides the generated steps, for example, in the form of video or text. The provision unit provides the steps, for example, in a format that is visually easy for the user to understand. This allows the cooking assistance system according to the embodiment to enable the user to easily prepare healthy and balanced meals. For example, by suggesting an optimal recipe based on information input by the user and providing cooking steps, the user can easily prepare healthy and balanced meals.

[0030] The generation unit can generate a new recipe based on past recipe data using the generation AI. The generation unit generates a new recipe based on past recipe data using, for example, the generation AI. The generation AI analyzes past recipe data using, for example, a text generation AI (e.g., LLM) to generate a new recipe. The generation unit can also use the generation AI to generate a recipe that suits a user's preferences from past recipe data. For example, the generation AI generates a new recipe that suits a user's preferences based on past recipe data. The generation unit can also analyze past recipe data using the generation AI to generate efficient cooking procedures. For example, the generation AI generates efficient cooking procedures based on past recipe data. This improves the accuracy of generating new recipes by utilizing past recipe data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI.

[0031] The providing unit can provide the generated cooking steps as a video or text. The providing unit, for example, provides the generated cooking steps as a video. The providing unit can also provide the generated cooking steps as text. The providing unit provides the cooking steps in a format that is visually easy for the user to understand. For example, the providing unit generates a video that explains the cooking steps in detail and provides it to the user. The providing unit can also generate text that explains the cooking steps in detail and provide it to the user. For example, the providing unit generates a video including step-by-step instructions and provides it to the user. The providing unit can also generate text including an explanation of the cooking utensils to be used and provide it to the user. This makes it easier for the user to prepare food by providing the cooking steps in a format that is visually easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0032] The input unit can input ingredients in the user's refrigerator, preferences, and allergy information. For example, the user manually inputs the ingredients in the refrigerator into the input unit. The input unit can also input ingredient information using, for example, barcode scanning. The input unit can also input ingredient information using voice input. For example, the user can simply input "chicken, broccoli, carrots" by voice, and the ingredient information is automatically set. The input unit can also input the user's preferences and allergy information. For example, the user can input "I like spicy food" or "I have a nut allergy." This makes it possible to suggest recipes that take into account the ingredients the user has, their individual preferences, and allergy information. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0033] The suggestion unit can suggest recipes based on the input information. The suggestion unit, for example, uses a generation AI to analyze the input ingredients, preferences, and allergy information and suggest optimal recipes. The suggestion unit suggests recipes taking into consideration, for example, nutritional balance, cooking time, difficulty, etc. For example, the suggestion unit suggests healthy and balanced recipes based on the input information. The suggestion unit suggests recipes such as stir-fried chicken and broccoli or carrot and chicken soup. This makes it possible to suggest recipes that take the user's health into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0034] The generation unit can generate a video or text that provides detailed instructions for cooking steps based on the proposed recipe. The generation unit can use, for example, a generation AI to generate a video that provides detailed instructions for cooking steps based on the proposed recipe. The generation unit can also use, for example, a generation AI to generate text that provides detailed instructions for cooking steps based on the proposed recipe. For example, the generation unit can generate a video including step-by-step instructions and provide it to the user. The generation unit can also generate text including instructions for cooking utensils to be used and provide it to the user. This allows the user to visually confirm the detailed instructions, allowing for smooth cooking preparation. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0035] The input unit can analyze the user's past ingredient input history and select an input method. For example, the input unit automatically displays ingredients that the user has frequently input in the past as candidates. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, the input unit predicts and suggests ingredients to be used in a specific time period based on the user's past input history. In this way, by utilizing the past input history, it is possible to provide the optimal input method for the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The input unit can filter the input of ingredients, preferences, and allergy information based on the user's current diet plan or nutritional status. For example, the input unit filters to input only necessary ingredients based on the user's current diet plan. For example, the input unit excludes specific ingredients to avoid overconsumption, taking into account the user's nutritional status. For example, the input unit filters to input only recommended ingredients based on the user's health goals. This enables input that takes into account the user's diet plan and nutritional status. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The input unit can select an input means according to the user's input method when inputting information about ingredients, preferences, and allergies. For example, the input unit can automatically set ingredient information simply by the user verbally inputting "chicken, broccoli, carrots." The input unit can also automatically input ingredient information using image recognition technology by having the user take a photo of ingredients in the refrigerator with a smartphone camera. For example, when the user inputs ingredient information using text, the input unit can simplify input using a predictive conversion function. This improves input efficiency by providing the optimal means according to the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0038] When inputting information about ingredients, preferences, and allergies, the input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the input unit prioritizes inputting ingredients that are easily available in that area. For example, if the user is traveling, the input unit prioritizes inputting ingredients that are available in the area where the user is staying. For example, if the user is in a specific season, the input unit prioritizes inputting ingredients that are in season for that season. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed, for example, using AI or without using AI.

[0039] The input unit can analyze the user's social media activity and input related information when inputting information on ingredients, preferences, and allergies. The input unit, for example, inputs related ingredient information based on recipes shared by the user on social media. The input unit, for example, analyzes the content of the user's social media posts and inputs ingredient information that matches the user's preferences. The input unit, for example, inputs related ingredient information by referring to the activity of the user's friends on social media. In this way, highly relevant information can be input by utilizing the user's social media activity. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting ingredient, preference, and allergy information. The input unit improves the input method, for example, based on feedback on ingredient information previously input by the user. The input unit preferentially inputs ingredient information that matches the user's preferences, for example, based on feedback previously provided by the user. The input unit, for example, analyzes the user's past feedback and optimizes the input method for allergy information. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0041] When proposing a recipe, the suggestion unit can adjust the content of the suggestion based on the freshness and nutritional value of the ingredients. For example, the suggestion unit preferentially suggests recipes that use highly fresh ingredients. For example, the suggestion unit provides detailed explanations of recipes that use highly nutritious ingredients. For example, when using ingredients that are low in freshness or nutritional value, the suggestion unit suggests complementary ingredients. This makes it possible to suggest recipes that take into account the freshness and nutritional value of the ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0042] When proposing a recipe, the suggestion unit can apply different suggestion algorithms depending on the ingredient category. For example, when meat ingredients are used, the suggestion unit applies a suggestion algorithm specialized for meat dishes. For example, when vegetable ingredients are used, the suggestion unit applies a suggestion algorithm specialized for vegetable dishes. For example, when seafood ingredients are used, the suggestion unit applies a suggestion algorithm specialized for fish dishes. This makes it possible to suggest optimal recipes according to the ingredient category. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0043] When suggesting a recipe, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit suggests similar recipes based on recipes that the user has previously preferred. For example, the suggestion unit suggests new recipes while avoiding recipes that the user has previously rejected. For example, the suggestion unit analyzes the user's past suggestion results and suggests the optimal recipe. In this way, the accuracy of the suggestion is improved by utilizing the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0044] When suggesting recipes, the suggestion unit can determine the priority of suggestions based on when ingredients were purchased. For example, the suggestion unit suggests recipes that prioritize using recently purchased ingredients. For example, the suggestion unit suggests recipes that prioritize using ingredients that are close to their expiration date. For example, the suggestion unit postpones recipes that use ingredients that can be stored for a long time. This makes it possible to suggest optimal recipes by taking into account when ingredients were purchased. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0045] When suggesting recipes, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients. For example, the suggestion unit prioritizes suggesting recipes that use side ingredients related to a main ingredient. For example, the suggestion unit successively suggests recipes that use ingredients from the same category. For example, the suggestion unit prioritizes suggesting recipes that have a good combination of ingredients. This makes it possible to suggest optimal recipes by taking into account the relevance of ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0046] When suggesting a recipe, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit suggests recipes using simple and easy-to-understand terminology to a beginner cook. For example, the suggestion unit suggests detailed recipes using technical terminology to an experienced cook. For example, the suggestion unit suggests recipes using optimal terminology based on the user's past recipe selection history. This makes it possible to suggest optimal recipes according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0047] When generating cooking procedures, the generation unit can refer to past recipe data to generate new procedures. The generation unit, for example, uses a generation AI to generate optimal cooking procedures based on past recipe data. The generation unit, for example, uses a generation AI to generate procedures that suit the user's preferences from past recipe data. The generation unit, for example, uses a generation AI to analyze past recipe data and generate efficient cooking procedures. This makes it possible to generate optimal cooking procedures by utilizing past recipe data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0048] When generating cooking instructions, the generation unit can analyze the user's past cooking history and select the optimal steps. The generation unit, for example, uses a generation AI to generate new steps based on cooking steps that the user has previously preferred. The generation unit, for example, uses a generation AI to generate new steps that avoid cooking steps that the user has previously rejected. The generation unit, for example, uses a generation AI to analyze the user's past cooking history and select the most efficient steps. This makes it possible to generate optimal cooking instructions by utilizing the user's past cooking history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0049] When generating cooking instructions, the generation unit can customize the instructions based on the user's current cooking environment. The generation unit generates optimal instructions according to, for example, the type of cooking utensils used by the user. The generation unit customizes the instructions according to, for example, the size of the user's cooking space. The generation unit adjusts the instructions according to, for example, the user's cooking time constraints. This makes it possible to generate optimal cooking instructions according to the user's cooking environment. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0050] When generating cooking instructions, the generation unit can generate instructions taking into account the geographic distribution of ingredients. For example, the generation unit generates instructions using ingredients that are easily available locally. For example, the generation unit generates instructions that incorporate traditional cooking methods of a specific region. For example, the generation unit suggests optimal cooking instructions based on the geographic distribution of ingredients. This makes it possible to generate optimal cooking instructions by taking the geographic distribution of ingredients into consideration. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0051] When generating cooking instructions, the generation unit can improve the accuracy of the instructions by referring to related cooking videos. The generation unit, for example, generates detailed instructions based on related cooking videos. The generation unit, for example, incorporates visual information from cooking videos to make the instructions easier to understand. The generation unit, for example, generates optimal instructions by referring to the instructions in cooking videos. In this way, the accuracy of the instructions is improved by utilizing related cooking videos. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0052] When generating cooking instructions, the generation unit can generate instructions taking into account the market value of ingredients. For example, when expensive ingredients are used, the generation unit generates instructions that minimize waste. For example, when inexpensive ingredients are used, the generation unit generates instructions that are cost-effective. For example, the generation unit proposes optimal instructions based on the market value of ingredients. This makes it possible to generate optimal cooking instructions by taking the market value of ingredients into consideration. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0053] When providing instructions, the providing unit can select the optimal providing method by referring to the user's past operation history. The providing unit, for example, selects a new providing method based on the user's past preferred providing method. The providing unit, for example, selects a new providing method by avoiding a providing method that the user has previously rejected. The providing unit, for example, analyzes the user's past operation history and selects the optimal providing method. This makes it possible to provide optimal instructions by utilizing the past operation history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When providing instructions, the providing unit can customize the provided content according to the user's current task. For example, if the user is cooking, the providing unit provides content specialized for cooking instructions. For example, if the user is shopping, the providing unit provides content specialized for how to select ingredients. For example, if the user is preparing a meal, the providing unit provides content specialized for preparing ingredients. This makes it possible to provide optimal instructions according to the user's current task. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0055] The providing unit can improve the provision method by reflecting user feedback when providing a procedure. For example, if the user provides feedback on the provision method, the providing unit improves the provision method based on the feedback. For example, the providing unit analyzes the user's feedback and suggests the optimal provision method. For example, the providing unit customizes the provision method by referring to the user's past feedback. This makes it possible to improve the provision method by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0056] When providing instructions, the providing unit can select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit provides a providing method that matches the screen size. For example, if the user is using a tablet, the providing unit provides a providing method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible providing method. This makes it possible to provide optimal instructions according to the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0057] When providing the procedure, the providing unit can provide the content in multiple languages ​​according to the user's language setting. The providing unit, for example, automatically sets the language of the procedure based on the language setting of the user's device. The providing unit, for example, provides a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the procedure in that language. This makes it possible to provide procedures in multiple languages ​​according to the user's language setting. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0058] When providing instructions, the providing unit can analyze the user's social media activity and provide relevant instructions. For example, the providing unit provides instructions related to recipes shared by the user on social media. For example, the providing unit analyzes the content of the user's social media posts and provides relevant instructions. For example, the providing unit provides relevant instructions by referring to the activity of the user's friends on social media. This makes it possible to provide highly relevant instructions by utilizing the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

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

[0060] When generating cooking instructions, the generation unit can customize the instructions based on the user's current cooking environment. For example, the generation unit generates optimal instructions based on the type of cooking utensils used by the user. The generation unit customizes the instructions based on the size of the user's cooking space. The generation unit adjusts the instructions based on the user's cooking time constraints. This makes it possible to generate optimal cooking instructions based on the user's cooking environment. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0061] The input unit can analyze the user's past ingredient input history and select an input method. For example, ingredients that the user has frequently input in the past are automatically displayed as candidates. Input methods (voice, text, etc.) that the user has used in the past are given priority in suggesting them. Based on the user's past input history, ingredients to be used in a specific time period are predicted and suggested. By utilizing the user's past input history, the input unit can provide the optimal input method for the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0062] When proposing a recipe, the suggestion unit can adjust the content of the suggestion based on the freshness and nutritional value of the ingredients. For example, it may preferentially suggest recipes using highly fresh ingredients. It may provide detailed explanations of recipes using highly nutritious ingredients. It may suggest complementary ingredients when using ingredients with low freshness or nutritional value. This makes it possible to suggest recipes that take into account the freshness and nutritional value of ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0063] When generating cooking instructions, the generation unit can improve the accuracy of the instructions by referring to related cooking videos. For example, detailed instructions can be generated based on related cooking videos. Visual information from cooking videos can be incorporated to make the instructions easier to understand. Optimal instructions can be generated by referring to the instructions in cooking videos. In this way, the accuracy of the instructions can be improved by utilizing related cooking videos. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI.

[0064] When suggesting a recipe, the suggestion unit can adjust the use of technical terminology in the suggestion depending on the user's level of expertise. For example, for a beginner cook, it can suggest a recipe using simple and easy-to-understand terminology. For an experienced cook, it can suggest a detailed recipe using technical terminology. It can suggest recipes using the most appropriate terminology based on the user's past recipe selection history. This makes it possible to suggest optimal recipes depending on the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0065] When providing instructions, the providing unit can select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit provides a providing method that matches the screen size. If the user is using a tablet, the providing unit provides a providing method that is optimized for a large screen. If the user is using a smartwatch, the providing unit provides a simple and highly visible providing method. This makes it possible to provide optimal instructions according to the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The input unit allows the user to input information about ingredients, preferences, and allergies. Information input by the user includes, for example, ingredients in the refrigerator, taste preferences, and allergy information. The input unit can input ingredient information using methods such as manual input, barcode scanning, and voice input. For example, the user can simply input "chicken, broccoli, carrots" by voice, and the ingredient information will be automatically set. Step 2: The suggestion unit proposes recipes based on the information entered by the input unit. Using generative AI, the suggestion unit analyzes the input ingredients, preferences, and allergy information, and proposes optimal recipes taking into consideration nutritional balance, cooking time, difficulty, etc. Step 3: The generator generates cooking steps based on the recipe proposed by the suggester. The generator uses generative AI to generate videos and text that explain the cooking steps in detail based on the proposed recipe. The generator can also generate new recipes based on past recipe data. Step 4: The providing unit provides the procedure generated by the generating unit. The providing unit provides the generated procedure in the form of video or text, and provides the procedure in a format that is visually easy for the user to understand.

[0068] (Example 2) A cooking assistance system according to an embodiment of the present invention proposes optimal recipes and provides cooking instructions based on ingredients, preferences, and allergy information entered by a user. The cooking assistance system allows users to input information about ingredients, preferences, and allergies, and a generation AI analyzes this information to propose optimal recipes. Furthermore, the system provides cooking instructions in the form of video and text based on the proposed recipe. For example, the cooking assistance system allows users to input information about ingredients in the refrigerator, their preferences, and allergy information. For example, users input information such as "I have chicken, broccoli, and carrots," "I like spicy food," and "I have a nut allergy." This information is then input into the generation AI. The generation AI then analyzes the input information and proposes optimal recipes for the user. The generation AI generates healthy and balanced recipes based on the input ingredients, preferences, and allergy information. For example, recipes such as "stir-fried chicken and broccoli" and "carrot and chicken soup" are proposed. The cooking assistance system then generates videos and text that provide detailed instructions for the cooking steps based on the proposed recipe. For example, cooking instructions such as "cut the chicken into bite-sized pieces," "boil the broccoli," and "thinly slice the carrots" are provided via video and text. This allows the cooking assistance system to easily prepare healthy and balanced meals. This allows users to overcome problems such as a lack of cooking ideas and recipes, and time constraints, and to enjoy cooking every day. For example, in today's busy society, even people who are busy with work and housework can easily prepare healthy meals.

[0069] A cooking assistance system according to an embodiment includes an input unit, a suggestion unit, a generation unit, and a provision unit. The input unit allows a user to input ingredients, preferences, and allergy information. The information input by the user includes, but is not limited to, ingredients in the refrigerator, taste preferences, and allergy information. For example, the input unit allows a user to manually input ingredients in the refrigerator. The input unit can also input ingredient information using barcode scanning. Furthermore, the input unit can also input ingredient information using voice input. For example, the ingredient information can be automatically set by simply inputting "chicken, broccoli, carrots" by voice. The suggestion unit suggests a recipe based on the information input by the input unit. The suggestion unit, for example, uses a generation AI to analyze the input ingredients, preferences, and allergy information and suggest an optimal recipe. The suggestion unit suggests a recipe taking into consideration, for example, nutritional balance, cooking time, difficulty, and the like. The generation unit generates cooking steps based on the recipe suggested by the suggestion unit. For example, the generation unit uses a generation AI to generate a video or text that explains the cooking steps in detail based on the proposed recipe. The generation unit can also generate a new recipe based on, for example, past recipe data. The provision unit provides the steps generated by the generation unit. The provision unit provides the generated steps, for example, in the form of video or text. The provision unit provides the steps, for example, in a format that is visually easy for the user to understand. This allows the cooking assistance system according to the embodiment to enable the user to easily prepare healthy and balanced meals. For example, by suggesting an optimal recipe based on information input by the user and providing cooking steps, the user can easily prepare healthy and balanced meals.

[0070] The generation unit can generate a new recipe based on past recipe data using the generation AI. The generation unit generates a new recipe based on past recipe data using, for example, the generation AI. The generation AI analyzes past recipe data using, for example, a text generation AI (e.g., LLM) to generate a new recipe. The generation unit can also use the generation AI to generate a recipe that suits a user's preferences from past recipe data. For example, the generation AI generates a new recipe that suits a user's preferences based on past recipe data. The generation unit can also analyze past recipe data using the generation AI to generate efficient cooking procedures. For example, the generation AI generates efficient cooking procedures based on past recipe data. This improves the accuracy of generating new recipes by utilizing past recipe data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI.

[0071] The providing unit can provide the generated cooking steps as a video or text. The providing unit, for example, provides the generated cooking steps as a video. The providing unit can also provide the generated cooking steps as text. The providing unit provides the cooking steps in a format that is visually easy for the user to understand. For example, the providing unit generates a video that explains the cooking steps in detail and provides it to the user. The providing unit can also generate text that explains the cooking steps in detail and provide it to the user. For example, the providing unit generates a video including step-by-step instructions and provides it to the user. The providing unit can also generate text including an explanation of the cooking utensils to be used and provide it to the user. This makes it easier for the user to prepare food by providing the cooking steps in a format that is visually easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0072] The input unit can input ingredients in the user's refrigerator, preferences, and allergy information. For example, the user manually inputs the ingredients in the refrigerator into the input unit. The input unit can also input ingredient information using, for example, barcode scanning. The input unit can also input ingredient information using voice input. For example, the user can simply input "chicken, broccoli, carrots" by voice, and the ingredient information is automatically set. The input unit can also input the user's preferences and allergy information. For example, the user can input "I like spicy food" or "I have a nut allergy." This makes it possible to suggest recipes that take into account the ingredients the user has, their individual preferences, and allergy information. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The suggestion unit can suggest recipes based on the input information. The suggestion unit, for example, uses a generation AI to analyze the input ingredients, preferences, and allergy information and suggest optimal recipes. The suggestion unit suggests recipes taking into consideration, for example, nutritional balance, cooking time, difficulty, etc. For example, the suggestion unit suggests healthy and balanced recipes based on the input information. The suggestion unit suggests recipes such as stir-fried chicken and broccoli or carrot and chicken soup. This makes it possible to suggest recipes that take the user's health into consideration. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0074] The generation unit can generate a video or text that provides detailed instructions for cooking steps based on the proposed recipe. The generation unit can use, for example, a generation AI to generate a video that provides detailed instructions for cooking steps based on the proposed recipe. The generation unit can also use, for example, a generation AI to generate text that provides detailed instructions for cooking steps based on the proposed recipe. For example, the generation unit can generate a video including step-by-step instructions and provide it to the user. The generation unit can also generate text including instructions for cooking utensils to be used and provide it to the user. This allows the user to visually confirm the detailed instructions, allowing for smooth cooking preparation. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0075] The input unit can estimate the user's emotions and adjust the timing of input of ingredient, preference, and allergy information based on the estimated user emotions. For example, when the user is stressed, the input unit simplifies the input and requests only minimal information. For example, when the user is relaxed, the input unit provides detailed input options and suggests customizable input methods. For example, when the user is in a hurry, the input unit prioritizes voice input and enables the user to quickly input ingredient, preference, and allergy information. This reduces stress by adjusting the input timing according to the user's emotions and provides a comfortable input experience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, for example, or without AI.

[0076] The input unit can analyze the user's past ingredient input history and select an input method. For example, the input unit automatically displays ingredients that the user has frequently input in the past as candidates. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, the input unit predicts and suggests ingredients to be used in a specific time period based on the user's past input history. In this way, by utilizing the past input history, it is possible to provide the optimal input method for the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The input unit can filter the input of ingredients, preferences, and allergy information based on the user's current diet plan or nutritional status. For example, the input unit filters to input only necessary ingredients based on the user's current diet plan. For example, the input unit excludes specific ingredients to avoid overconsumption, taking into account the user's nutritional status. For example, the input unit filters to input only recommended ingredients based on the user's health goals. This enables input that takes into account the user's diet plan and nutritional status. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0078] The input unit can select an input means according to the user's input method when inputting information about ingredients, preferences, and allergies. For example, the input unit can automatically set ingredient information simply by the user verbally inputting "chicken, broccoli, carrots." The input unit can also automatically input ingredient information using image recognition technology by having the user take a photo of ingredients in the refrigerator with a smartphone camera. For example, when the user inputs ingredient information using text, the input unit can simplify input using a predictive conversion function. This improves input efficiency by providing the optimal means according to the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The input unit can estimate the user's emotions and determine the priority of ingredients, preferences, and allergy information to be input based on the estimated user's emotions. For example, when the user is stressed, the input unit prioritizes input of ingredients that are easy to cook. For example, when the user is relaxed, the input unit prioritizes input of ingredients that take a long time to cook or complicated recipes. For example, when the user is in a hurry, the input unit prioritizes input of ingredients that can be cooked in a short time. This allows more appropriate information to be input by adjusting the input priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI or without AI.

[0080] When inputting information about ingredients, preferences, and allergies, the input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the input unit prioritizes inputting ingredients that are easily available in that area. For example, if the user is traveling, the input unit prioritizes inputting ingredients that are available in the area where the user is staying. For example, if the user is in a specific season, the input unit prioritizes inputting ingredients that are in season for that season. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed, for example, using AI or without using AI.

[0081] The input unit can analyze the user's social media activity and input related information when inputting information on ingredients, preferences, and allergies. The input unit, for example, inputs related ingredient information based on recipes shared by the user on social media. The input unit, for example, analyzes the content of the user's social media posts and inputs ingredient information that matches the user's preferences. The input unit, for example, inputs related ingredient information by referring to the activity of the user's friends on social media. In this way, highly relevant information can be input by utilizing the user's social media activity. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The input unit can customize the input method by reflecting the user's past feedback when inputting ingredient, preference, and allergy information. The input unit improves the input method, for example, based on feedback on ingredient information previously input by the user. The input unit preferentially inputs ingredient information that matches the user's preferences, for example, based on feedback previously provided by the user. The input unit, for example, analyzes the user's past feedback and optimizes the input method for allergy information. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit suggests detailed recipes. For example, if the user is in a hurry, the suggestion unit suggests recipes that are easy to make and can be made quickly. For example, if the user is feeling stressed, the suggestion unit suggests recipes that use ingredients that have a stress-reducing effect. This makes it possible to suggest recipes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0084] When proposing a recipe, the suggestion unit can adjust the content of the suggestion based on the freshness and nutritional value of the ingredients. For example, the suggestion unit preferentially suggests recipes that use highly fresh ingredients. For example, the suggestion unit provides detailed explanations of recipes that use highly nutritious ingredients. For example, when using ingredients that are low in freshness or nutritional value, the suggestion unit suggests complementary ingredients. This makes it possible to suggest recipes that take into account the freshness and nutritional value of the ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0085] When proposing a recipe, the suggestion unit can apply different suggestion algorithms depending on the ingredient category. For example, when meat ingredients are used, the suggestion unit applies a suggestion algorithm specialized for meat dishes. For example, when vegetable ingredients are used, the suggestion unit applies a suggestion algorithm specialized for vegetable dishes. For example, when seafood ingredients are used, the suggestion unit applies a suggestion algorithm specialized for fish dishes. This makes it possible to suggest optimal recipes according to the ingredient category. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0086] When suggesting a recipe, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit suggests similar recipes based on recipes that the user has previously preferred. For example, the suggestion unit suggests new recipes while avoiding recipes that the user has previously rejected. For example, the suggestion unit analyzes the user's past suggestion results and suggests the optimal recipe. In this way, the accuracy of the suggestion is improved by utilizing the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the recipe suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit suggests a short, to-the-point recipe. For example, if the user is relaxed, the suggestion unit suggests a longer recipe with detailed instructions. For example, if the user is excited, the suggestion unit suggests a recipe with visually stimulating effects. This allows optimal information to be provided to the user by adjusting the length of the recipe suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0088] When suggesting recipes, the suggestion unit can determine the priority of suggestions based on when ingredients were purchased. For example, the suggestion unit suggests recipes that prioritize using recently purchased ingredients. For example, the suggestion unit suggests recipes that prioritize using ingredients that are close to their expiration date. For example, the suggestion unit postpones recipes that use ingredients that can be stored for a long time. This makes it possible to suggest optimal recipes by taking into account when ingredients were purchased. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0089] When suggesting recipes, the suggestion unit can adjust the order of suggestions based on the relevance of ingredients. For example, the suggestion unit prioritizes suggesting recipes that use side ingredients related to a main ingredient. For example, the suggestion unit successively suggests recipes that use ingredients from the same category. For example, the suggestion unit prioritizes suggesting recipes that have a good combination of ingredients. This makes it possible to suggest optimal recipes by taking into account the relevance of ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0090] When suggesting a recipe, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit suggests recipes using simple and easy-to-understand terminology to a beginner cook. For example, the suggestion unit suggests detailed recipes using technical terminology to an experienced cook. For example, the suggestion unit suggests recipes using optimal terminology based on the user's past recipe selection history. This makes it possible to suggest optimal recipes according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI.

[0091] The generation unit can estimate the user's emotions and adjust the cooking procedure generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates cooking procedures that proceed at a leisurely pace. For example, if the user is in a hurry, the generation unit generates procedures that allow cooking to be done in the shortest time. For example, if the user is excited, the generation unit generates cooking procedures that add visually stimulating effects. This makes it possible to generate optimal cooking procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0092] When generating cooking procedures, the generation unit can refer to past recipe data to generate new procedures. The generation unit, for example, uses a generation AI to generate optimal cooking procedures based on past recipe data. The generation unit, for example, uses a generation AI to generate procedures that suit the user's preferences from past recipe data. The generation unit, for example, uses a generation AI to analyze past recipe data and generate efficient cooking procedures. This makes it possible to generate optimal cooking procedures by utilizing past recipe data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0093] When generating cooking instructions, the generation unit can analyze the user's past cooking history and select the optimal steps. The generation unit, for example, uses a generation AI to generate new steps based on cooking steps that the user has previously preferred. The generation unit, for example, uses a generation AI to generate new steps that avoid cooking steps that the user has previously rejected. The generation unit, for example, uses a generation AI to analyze the user's past cooking history and select the most efficient steps. This makes it possible to generate optimal cooking instructions by utilizing the user's past cooking history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0094] When generating cooking instructions, the generation unit can customize the instructions based on the user's current cooking environment. The generation unit generates optimal instructions according to, for example, the type of cooking utensils used by the user. The generation unit customizes the instructions according to, for example, the size of the user's cooking space. The generation unit adjusts the instructions according to, for example, the user's cooking time constraints. This makes it possible to generate optimal cooking instructions according to the user's cooking environment. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI.

[0095] The generation unit can estimate the user's emotions and adjust the display method of the cooking instructions based on the estimated user emotions. For example, if the user is nervous, the generation unit provides a simple, highly visible display method. For example, if the user is relaxed, the generation unit provides a display method including detailed information. For example, if the user is in a hurry, the generation unit provides a display method that focuses on the main points. This makes it possible to provide cooking instructions in an optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0096] When generating cooking instructions, the generation unit can generate instructions taking into account the geographic distribution of ingredients. For example, the generation unit generates instructions using ingredients that are easily available locally. For example, the generation unit generates instructions that incorporate traditional cooking methods of a specific region. For example, the generation unit suggests optimal cooking instructions based on the geographic distribution of ingredients. This makes it possible to generate optimal cooking instructions by taking the geographic distribution of ingredients into consideration. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0097] When generating cooking instructions, the generation unit can improve the accuracy of the instructions by referring to related cooking videos. The generation unit, for example, generates detailed instructions based on related cooking videos. The generation unit, for example, incorporates visual information from cooking videos to make the instructions easier to understand. The generation unit, for example, generates optimal instructions by referring to the instructions in cooking videos. In this way, the accuracy of the instructions is improved by utilizing related cooking videos. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0098] When generating cooking instructions, the generation unit can generate instructions taking into account the market value of ingredients. For example, when expensive ingredients are used, the generation unit generates instructions that minimize waste. For example, when inexpensive ingredients are used, the generation unit generates instructions that are cost-effective. For example, the generation unit proposes optimal instructions based on the market value of ingredients. This makes it possible to generate optimal cooking instructions by taking the market value of ingredients into consideration. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0099] The providing unit can estimate the user's emotions and adjust the method of providing instructions based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible method of providing instructions. For example, if the user is relaxed, the providing unit provides a method of providing instructions that includes detailed information. For example, if the user is in a hurry, the providing unit provides a method of providing instructions that focuses on the main points. This makes it possible to provide optimal instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0100] When providing instructions, the providing unit can select the optimal providing method by referring to the user's past operation history. The providing unit, for example, selects a new providing method based on the user's past preferred providing method. The providing unit, for example, selects a new providing method by avoiding a providing method that the user has previously rejected. The providing unit, for example, analyzes the user's past operation history and selects the optimal providing method. This makes it possible to provide optimal instructions by utilizing the past operation history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0101] When providing instructions, the providing unit can customize the provided content according to the user's current task. For example, if the user is cooking, the providing unit provides content specialized for cooking instructions. For example, if the user is shopping, the providing unit provides content specialized for how to select ingredients. For example, if the user is preparing a meal, the providing unit provides content specialized for preparing ingredients. This makes it possible to provide optimal instructions according to the user's current task. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0102] The providing unit can improve the provision method by reflecting user feedback when providing a procedure. For example, if the user provides feedback on the provision method, the providing unit improves the provision method based on the feedback. For example, the providing unit analyzes the user's feedback and suggests the optimal provision method. For example, the providing unit customizes the provision method by referring to the user's past feedback. This makes it possible to improve the provision method by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0103] The providing unit can estimate the user's emotions and determine the priority of providing procedures based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit prioritizes providing simple procedures. For example, if the user is relaxed, the providing unit prioritizes providing detailed procedures. For example, if the user is in a hurry, the providing unit prioritizes providing procedures that can be completed in a short time. This makes it possible to determine the optimal priority of providing procedures according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0104] When providing instructions, the providing unit can select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit provides a providing method that matches the screen size. For example, if the user is using a tablet, the providing unit provides a providing method that is optimized for a large screen. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible providing method. This makes it possible to provide optimal instructions according to the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0105] When providing the procedure, the providing unit can provide the content in multiple languages ​​according to the user's language setting. The providing unit, for example, automatically sets the language of the procedure based on the language setting of the user's device. The providing unit, for example, provides a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the providing unit provides the procedure in that language. This makes it possible to provide procedures in multiple languages ​​according to the user's language setting. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0106] When providing instructions, the providing unit can analyze the user's social media activity and provide relevant instructions. For example, the providing unit provides instructions related to recipes shared by the user on social media. For example, the providing unit analyzes the content of the user's social media posts and provides relevant instructions. For example, the providing unit provides relevant instructions by referring to the activity of the user's friends on social media. This makes it possible to provide highly relevant instructions by utilizing the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, suggestion unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit allows a user to input information about ingredients, preferences, and allergies using the reception device 38 or microphone 38B of the smart device 14. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the input information using a generation AI and proposes an optimal recipe. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a video or text that explains cooking steps in detail based on the proposed recipe. The provision unit, for example, uses the output device 40 of the smart device 14 to provide the generated steps as a video or text. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, suggestion unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit allows a user to input ingredients, preferences, and allergy information using the microphone 238 of the smart glasses 214. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the input information using a generation AI and proposes an optimal recipe. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a video or text that explains cooking steps in detail based on the proposed recipe. The provision unit, for example, provides the generated steps by voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, suggestion unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit allows the user to input information about ingredients, preferences, and allergies using the microphone 238 of the headset-type terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI to propose an optimal recipe. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates video and text that explain cooking steps in detail based on the proposed recipe. The provision unit visually presents the generated steps using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, suggestion unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit allows the user to input information about ingredients, preferences, and allergies using the microphone 238 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information using a generation AI to propose an optimal recipe. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates video or text that explains cooking steps in detail based on the proposed recipe. The provision unit provides the generated steps by voice, for example, using the speaker 240 of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated user's emotions. For example, if the user is relaxed, a detailed recipe is suggested. If the user is in a hurry, a simple recipe that can be made quickly is suggested. If the user is stressed, a recipe using ingredients that have a stress-reducing effect is suggested. This makes it possible to suggest recipes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0109] When generating cooking instructions, the generation unit can customize the instructions based on the user's current cooking environment. For example, the generation unit generates optimal instructions based on the type of cooking utensils used by the user. The generation unit customizes the instructions based on the size of the user's cooking space. The generation unit adjusts the instructions based on the user's cooking time constraints. This makes it possible to generate optimal cooking instructions based on the user's cooking environment. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0110] The providing unit can estimate the user's emotions and adjust the method of providing instructions based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible method of providing instructions is provided. If the user is relaxed, a method including detailed information is provided. If the user is in a hurry, a method that focuses on the main points is provided. This makes it possible to provide optimal instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0111] The input unit can analyze the user's past ingredient input history and select an input method. For example, ingredients that the user has frequently input in the past are automatically displayed as candidates. Input methods (voice, text, etc.) that the user has used in the past are given priority in suggesting them. Based on the user's past input history, ingredients to be used in a specific time period are predicted and suggested. By utilizing the user's past input history, the input unit can provide the optimal input method for the user. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0112] When proposing a recipe, the suggestion unit can adjust the content of the suggestion based on the freshness and nutritional value of the ingredients. For example, it may preferentially suggest recipes using highly fresh ingredients. It may provide detailed explanations of recipes using highly nutritious ingredients. It may suggest complementary ingredients when using ingredients with low freshness or nutritional value. This makes it possible to suggest recipes that take into account the freshness and nutritional value of ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0113] When generating cooking instructions, the generation unit can improve the accuracy of the instructions by referring to related cooking videos. For example, detailed instructions can be generated based on related cooking videos. Visual information from cooking videos can be incorporated to make the instructions easier to understand. Optimal instructions can be generated by referring to the instructions in cooking videos. In this way, the accuracy of the instructions can be improved by utilizing related cooking videos. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI.

[0114] The input unit can estimate the user's emotions and determine the priority of ingredients, preferences, and allergy information to be input based on the estimated user emotions. For example, if the user is stressed, it prioritizes the input of ingredients that are easy to prepare. If the user is relaxed, it prioritizes the input of ingredients that take a long time to cook or complicated recipes. If the user is in a hurry, it prioritizes the input of ingredients that can be cooked in a short time. This allows more appropriate information to be input by adjusting the input priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes text generation AI (e.g., LLM) and multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI.

[0115] When suggesting a recipe, the suggestion unit can adjust the use of technical terminology in the suggestion depending on the user's level of expertise. For example, for a beginner cook, it can suggest a recipe using simple and easy-to-understand terminology. For an experienced cook, it can suggest a detailed recipe using technical terminology. It can suggest recipes using the most appropriate terminology based on the user's past recipe selection history. This makes it possible to suggest optimal recipes depending on the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0116] When providing instructions, the providing unit can select the optimal providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit provides a providing method that matches the screen size. If the user is using a tablet, the providing unit provides a providing method that is optimized for a large screen. If the user is using a smartwatch, the providing unit provides a simple and highly visible providing method. This makes it possible to provide optimal instructions according to the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0117] The providing unit can estimate the user's emotions and determine the priority of providing procedures based on the estimated user emotions. For example, if the user is feeling stressed, simple procedures are provided preferentially. If the user is relaxed, detailed procedures are provided preferentially. If the user is in a hurry, procedures that can be completed in a short time are provided preferentially. This allows the optimal priority of providing procedures to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The input unit allows the user to input information about ingredients, preferences, and allergies. Information input by the user includes, for example, ingredients in the refrigerator, taste preferences, and allergy information. The input unit can input ingredient information using methods such as manual input, barcode scanning, and voice input. For example, the user can simply input "chicken, broccoli, carrots" by voice, and the ingredient information will be automatically set. Step 2: The suggestion unit proposes recipes based on the information entered by the input unit. Using generative AI, the suggestion unit analyzes the input ingredients, preferences, and allergy information, and proposes optimal recipes taking into consideration nutritional balance, cooking time, difficulty, etc. Step 3: The generator generates cooking steps based on the recipe proposed by the suggester. The generator uses generative AI to generate videos and text that explain the cooking steps in detail based on the proposed recipe. The generator can also generate new recipes based on past recipe data. Step 4: The providing unit provides the procedure generated by the generating unit. The providing unit provides the generated procedure in the form of video or text, and provides the procedure in a format that is visually easy for the user to understand.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting ingredients, preferences, and allergy information; a suggestion unit that suggests a recipe based on the information input by the input unit; a generation unit that generates cooking procedures based on the recipes suggested by the suggestion unit; a providing unit that provides the procedure generated by the generating unit. A system characterized by:

2. The generation unit Generative AI generates new recipes based on past recipe data 2. The system of claim 1.

3. The providing unit Provide generated instructions in video or text format 2. The system of claim 1.

4. The input unit Enter the ingredients in the user's refrigerator, preferences, and allergy information 2. The system of claim 1.

5. The proposal unit Recipe suggestions based on input information 2. The system of claim 1.

6. The generation unit Generate detailed video and text instructions for cooking based on a suggested recipe 2. The system of claim 1.

7. The input unit Estimates the user's emotions and adjusts the timing of inputting information about ingredients, preferences, and allergies based on the estimated emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past ingredient input history and select the input method 2. The system of claim 1.

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