System

Abstract

To provide a system that automatically generates optimal menus appropriate to a user's lifestyle and preferences.SOLUTION: A system according to the embodiment comprises a reception unit, an acquisition unit, a learning unit, and a generation unit. The reception unit allows a user to receive information about purchase food ingredients via voice. The acquisition unit acquires information about the user from an application the user is using. The learning unit learns the user's lifestyle or preferences based on the information acquired by the acquisition unit. The generation unit generates a menu based on the information about purchase food ingredients received by the reception unit and the information about the user's lifestyle or preferences learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, it was difficult to automatically generate optimal menus that matched a user's lifestyle and preferences.

[0005] The system according to the embodiment aims to automatically generate an optimal menu that matches the user's lifestyle and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a learning unit, and a generation unit. The reception unit receives information about ingredients purchased by a user through voice. The acquisition unit acquires information about the user from an app the user is using. The learning unit learns the user's lifestyle or preferences based on the information acquired by the acquisition unit. The generation unit generates a menu based on the information about purchased ingredients received by the reception unit and the information about the user's lifestyle or preferences learned by the learning unit. Effect of the Invention

[0007] The system according to the embodiment can automatically generate an optimal menu that matches a user's lifestyle and preferences. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The menu generation system according to the embodiment of the present invention is a system that generates an optimal menu based on ingredients purchased by a user. In this system, the user provides information on purchased ingredients through voice input, and acquires information on the user's lifestyle, preferences, and health condition from other apps (such as a calendar or health care). In this way, a menu is generated that matches the user's lifestyle and preferences. For example, the user inputs information on the ingredients purchased by voice. For example, information such as "I bought tomatoes, chicken, and pasta" is input by voice. This information is converted into text data using voice recognition technology. Next, the system acquires information from other apps used by the user. For example, the user's schedule is acquired from a calendar app, and the user's amount of exercise and sleep state are acquired from a health care app. In this way, information on the user's lifestyle and health condition is collected. The system learns the user's lifestyle and preferences based on the collected information. For example, the system learns that the user prefers dishes with shorter cooking times on busy days, and prefers dishes with fewer calories on days when the user exercises less. Finally, the system generates an optimal menu based on the purchased ingredients and the learned information. For example, if the user purchases tomatoes, chicken, and pasta, and it is a busy day, the system suggests a simple pasta dish using tomatoes and chicken. It is also possible to suggest nutritionally balanced menus based on the user's health condition. As a result, the menu generation system automatically generates menus that match the user's lifestyle and preferences, allowing the user to enjoy healthy and delicious meals without any hassle. Furthermore, by using voice input, the user can easily provide ingredient information, improving convenience. As a result, the menu generation system can generate optimal menus based on the user's purchased ingredients and lifestyle.

[0029] The menu creation system according to the embodiment includes a reception unit, an acquisition unit, a learning unit, and a generation unit. The reception unit receives information about ingredients purchased by the user through voice. The ingredients purchased by the user include, but are not limited to, tomatoes, chicken, and pasta. For example, the reception unit inputs by voice, "I bought tomatoes, chicken, and pasta." This information is converted into text data using voice recognition technology. The acquisition unit acquires information about the user from an app used by the user. For example, the acquisition unit acquires the user's schedule from a calendar app, and the user's amount of exercise and sleep state from a health care app. This allows information about the user's lifestyle and health condition to be collected. The learning unit learns the user's lifestyle or preferences based on the information acquired by the acquisition unit. For example, the learning unit learns that the user prefers dishes with shorter cooking times on busy days and prefers dishes with fewer calories on days when the user exercises less. The generation unit generates an appropriate menu based on the information about purchased ingredients received by the reception unit and the information about the user's lifestyle or preferences learned by the learning unit. For example, if a user purchases tomatoes, chicken, and pasta and it is a busy day, the generation unit suggests a simple pasta dish using tomatoes and chicken. The generation unit can also suggest a nutritionally balanced menu based on the user's health condition. As a result, the menu generation system according to the embodiment can generate an optimal menu based on the user's purchased ingredients and lifestyle. Some or all of the above-mentioned processes in the reception unit, acquisition unit, learning unit, and generation unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can use an AI model that converts the user's voice input into text data using voice recognition technology. The acquisition unit can use an AI model that acquires information from a calendar app or a health care app. The learning unit can use an AI model that learns the user's lifestyle and preferences. The generation unit can use an AI model that generates a menu based on purchased ingredients and learned information.As a result, the menu creation system according to the embodiment can create an optimal menu based on the user's purchased ingredients and lifestyle.

[0030] The reception unit receives information about ingredients purchased by the user through voice. The ingredients purchased by the user include, but are not limited to, tomatoes, chicken, and pasta. For example, the user inputs "I bought tomatoes, chicken, and pasta" through voice into the reception unit. This information is converted into text data using voice recognition technology. Specifically, a voice recognition model using deep learning is used for the voice recognition technology. This model has learned a large amount of voice data and can convert voice into text with high accuracy. When the user inputs voice, the reception unit transmits the voice data to a server, where the voice recognition model processes the voice data. The voice data is first subjected to noise removal and volume normalization as preprocessing, and then input into the voice recognition model. The model analyzes the voice waveform, identifies phonemes and words, and converts them into text data. This text data is saved as a list of ingredients purchased by the user and is used for subsequent processing. Furthermore, the reception unit saves the user's voice input history and can improve the accuracy of voice recognition based on past input data. For example, by learning the ingredients that a particular user frequently purchases and their particular pronunciation habits, the accuracy of the voice recognition can be individually optimized. This allows the reception unit to convert the user's voice input into text data with high accuracy, improving the performance of the entire system.

[0031] The acquisition unit acquires information about the user from the application that the user is using. For example, the acquisition unit acquires the user's schedule from a calendar application, and acquires the user's amount of exercise and sleep state from a health application. This allows information about the user's lifestyle and health condition to be collected. Specifically, the acquisition unit acquires data using the API of each application. In the case of a calendar application, the acquisition unit acquires the user's schedule and event information, and identifies busy days and days when the user has free time. In the case of a health application, the acquisition unit acquires data such as the user's number of steps, calories burned, and sleep time, and grasps the user's health condition. These data are centrally managed by the acquisition unit and used to comprehensively evaluate the user's lifestyle and health condition. Furthermore, the acquisition unit can acquire data from other related applications and devices with the user's consent. For example, data from a smart watch or fitness tracker can be acquired to collect more detailed health information. This allows the acquisition unit to collect a variety of data about the user's lifestyle and health condition, and improve the accuracy and reliability of the entire system.

[0032] The learning unit learns the lifestyle or preferences of the user based on the information acquired by the acquisition unit. For example, the learning unit learns that the user prefers dishes with a short cooking time on busy days and prefers dishes with low calories on days when the user exercises less. Specifically, the learning unit uses a machine learning algorithm to analyze the user's behavioral patterns and preferences. For example, based on past data, a model is constructed that predicts what dishes the user will select under certain conditions. This model uses the user's schedule, health condition, past selection history, etc. as input data and can predict the user's preferences with high accuracy. Furthermore, the learning unit can continuously update the model based on the user's feedback to improve accuracy. For example, the user evaluates the proposed menu, and the evaluation data is reflected in the learning to improve the accuracy of the next proposal. In addition, the learning unit can also identify common patterns and trends by comparing and analyzing data from different users and build a more general preference model. This allows the learning unit to learn the user's lifestyle and preferences with high accuracy and improve the degree of personalization of the entire system.

[0033] The generation unit generates an appropriate menu based on the information on the purchased ingredients received by the reception unit and the information on the user's lifestyle or preferences learned by the learning unit. For example, if the user purchases tomatoes, chicken, and pasta and it is a busy day, the generation unit suggests a simple pasta dish using tomatoes and chicken. The generation unit can also suggest a nutritionally balanced menu based on the user's health condition. Specifically, the generation unit refers to a recipe database and searches for recipes that match the purchased ingredients. Furthermore, the generation unit filters and customizes recipes based on the user's lifestyle and preferences. For example, recipes with short cooking times and recipes with low calories are preferentially suggested. The generation unit can also generate and customize recipes using AI. For example, the generation unit can automatically generate recipe steps and ingredients using natural language processing technology and provide them to the user. Furthermore, the generation unit can continuously improve the proposed content based on user feedback and provide more appropriate menus. As a result, the generation unit can generate optimal menus based on the user's purchased ingredients and lifestyle, and support the user's eating habits.

[0034] The acquisition unit acquires the user's schedule from a calendar app, and the generation unit can generate a menu of dishes with short cooking times on busy days. The acquisition unit acquires the user's schedule from, for example, a calendar app. The user's schedule includes, for example, work plans and private plans, but is not limited to such examples. The generation unit generates, for example, a menu of dishes with short cooking times on busy days. Dishes with short cooking times include, for example, within 30 minutes, within 15 minutes, etc., but are not limited to such examples. For example, the generation unit can suggest a simple pasta dish using tomatoes and chicken on busy days. This makes it possible to reduce cooking time according to the user's schedule. A part or all of the above-mentioned processes in the acquisition unit and the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that acquires the user's schedule from a calendar app. The generation unit can use an AI model that generates a menu of dishes with short cooking times based on the user's schedule.

[0035] The acquisition unit acquires the user's amount of exercise and sleep state from a health care application, and the generation unit can propose a menu taking into account the health condition. The acquisition unit acquires, for example, the user's amount of exercise and sleep state from a health care application. The amount of exercise and sleep state include, for example, the number of steps, sleep time, and sleep quality, but are not limited to such examples. The generation unit proposes a menu taking into account, for example, the health condition of the user. Consideration of the health condition includes, for example, weight management, blood pressure management, and the like, but are not limited to such examples. For example, the generation unit can propose low-calorie dishes on days when the user does not exercise much. This makes it possible to propose a menu according to the user's health condition. A part or all of the above-mentioned processes in the acquisition unit and the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that acquires the user's amount of exercise and sleep state from a health care application. The generation unit can use an AI model that proposes a menu based on the user's health condition.

[0036] The reception unit can improve the accuracy of the input by referring to the user's past input history during voice input. The reception unit can improve the accuracy of the input by referring to the user's past input history during voice input, for example. The past input history includes, for example, past voice commands, input contents, etc., but is not limited to such examples. For example, the reception unit automatically displays ingredients names that the user has frequently used in the past as candidates. The reception unit can also predict a specific combination of ingredients from the user's past input history and complement the input. The reception unit can also correct an ingredient name that the user has input incorrectly in the past and prompt the user to input it correctly. As a result, the accuracy of the voice input is improved by referring to the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that improves the accuracy of the input by referring to the user's past input history.

[0037] The reception unit can adjust the accuracy of the input based on the user's speech speed and volume when inputting voice. The reception unit adjusts the accuracy of the input based on the user's speech speed and volume when inputting voice, for example. The speech speed and volume include, but are not limited to, a range of speech speeds and a range of volume. For example, when the user speaks quickly, the reception unit increases the sensitivity of the voice recognition to accurately recognize the input. Furthermore, when the user speaks slowly, the reception unit can adjust the sensitivity of the voice recognition to improve the accuracy of the input. Furthermore, when the user speaks softly, the reception unit can automatically amplify the volume to accurately recognize the input. This makes it possible to adjust the accuracy of the voice input according to the speech speed and volume. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that adjusts the accuracy of the input based on the user's speech speed and volume.

[0038] The reception unit can preferentially accept highly relevant ingredient information in consideration of the geographical location information of the user when the voice is input. For example, the reception unit preferentially accepts highly relevant ingredient information in consideration of the geographical location information of the user when the voice is input. The geographical location information includes, for example, the current location and local specialty product information, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially accepts ingredient information that is often used in that area. Furthermore, when the user is traveling, the reception unit can preferentially accept ingredient information related to specialty products of the travel destination. Furthermore, when the user is at home, the reception unit can preferentially accept ingredient information that can be purchased at a nearby supermarket. This makes it possible to preferentially accept highly relevant ingredient information based on geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that preferentially accepts highly relevant ingredient information in consideration of the geographical location information of the user.

[0039] The reception unit can analyze the social media activity of the user at the time of voice input and accept related ingredient information. The reception unit can analyze the social media activity of the user at the time of voice input, for example, and accept related ingredient information. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. For example, the reception unit accepts related ingredient information based on recipes shared by the user on social media. In addition, the reception unit can analyze the content of posts of cooking accounts that the user follows on social media and accept related ingredient information. In addition, the reception unit can accept related ingredient information based on dishes that the user has "liked" on social media. This makes it possible to accept related ingredient information based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that analyzes the social media activity of the user and accepts related ingredient information.

[0040] The acquisition unit can analyze the user's past ingredient purchase history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past ingredient purchase history and selects an appropriate acquisition method. The past ingredient purchase history includes, for example, purchase date, purchase amount, purchase frequency, etc., but is not limited to such examples. For example, the acquisition unit preferentially acquires ingredients that the user has frequently purchased in the past. In addition, the acquisition unit can predict and acquire ingredients to be purchased in a specific season from the user's past purchase history. In addition, the acquisition unit can analyze the user's past purchase history and preferentially acquire ingredients that are purchased frequently. This makes it possible to select an optimal acquisition method based on the past purchase history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that analyzes the user's past ingredient purchase history and selects an appropriate acquisition method.

[0041] The acquisition unit can prioritize acquisition of highly relevant information in consideration of the geographical location information of the user at the time of acquisition. For example, the acquisition unit prioritizes acquisition of highly relevant information in consideration of the geographical location information of the user at the time of acquisition. Highly relevant information includes, for example, geographically close information, local specialty product information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the acquisition unit prioritizes acquisition of food ingredient information that is often used in that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquisition of information on local specialty products of the travel destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquisition of food ingredient information that can be purchased at a nearby supermarket. This makes it possible to prioritize acquisition of highly relevant information based on geographical location information. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that prioritizes acquisition of highly relevant information in consideration of the geographical location information of the user.

[0042] The acquisition unit can analyze the user's social media activity at the time of acquisition and collect related information. The acquisition unit, for example, analyzes the user's social media activity at the time of acquisition and collects related information. The related information includes, for example, the content of social media posts and the user's interests, but is not limited to such examples. For example, the acquisition unit acquires related ingredient information based on a recipe shared by the user on social media. In addition, the acquisition unit can analyze the content of posts of cooking accounts that the user follows on social media and acquire related ingredient information. In addition, the acquisition unit can acquire related ingredient information based on dishes that the user has "liked" on social media. This makes it possible to acquire related information based on social media activities. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that analyzes the user's social media activity and collects related information.

[0043] The learning unit can improve the learning algorithm by referring to past learning data during learning. The learning unit, for example, improves the learning algorithm by referring to past learning data during learning. The learning algorithm includes, for example, a neural network, a decision tree, and the like, but is not limited to such examples. For example, the learning unit preferentially learns recipes that match the user's preferences from the past learning data. In addition, the learning unit can analyze the past learning data and improve the accuracy of the learning algorithm. In addition, the learning unit can learn recipes that match the user's lifestyle based on the past learning data. This makes it possible to optimize the learning algorithm based on the past learning data. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that improves the learning algorithm by referring to past learning data.

[0044] The learning unit can revise the learning content in consideration of the user's ingredient purchase history and changes in lifestyle during learning. For example, the learning unit revise the learning content in consideration of the user's ingredient purchase history and changes in lifestyle during learning. Changes in lifestyle include, for example, changes in eating habits and changes in exercise habits, but are not limited to such examples. For example, the learning unit preferentially learns ingredients that are frequently purchased based on the user's ingredient purchase history. In addition, the learning unit can update the learning content in consideration of changes in the user's lifestyle (for example, moving or changing jobs). In addition, the learning unit can learn optimal recipes by combining the user's ingredient purchase history and changes in lifestyle. This makes it possible to update the learning content based on the ingredient purchase history and changes in lifestyle. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that revise the learning content in consideration of the user's ingredient purchase history and changes in lifestyle.

[0045] The learning unit can weight the learning data in consideration of the geographical location information of the user during learning. For example, the learning unit weights the learning data in consideration of the geographical location information of the user during learning. Weighting of the learning data includes, for example, geographical proximity and regional characteristics, but is not limited to such examples. For example, when the user is in a specific region, the learning unit weights and learns ingredients that are frequently used in that region. In addition, when the user is traveling, the learning unit can weight and learn data related to local products of the travel destination. In addition, when the user is at home, the learning unit can weight and learn ingredients that can be purchased at a nearby supermarket. This makes it possible to weight the learning data based on the geographical location information. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that weights the learning data in consideration of the geographical location information of the user.

[0046] The learning unit can analyze the user's social media activities during learning and incorporate related data into the learning. For example, the learning unit can analyze the user's social media activities during learning and incorporate related data into the learning. The related data includes, for example, social media posting content, the user's interests, and the like, but is not limited to such examples. For example, the learning unit incorporates recipes shared by the user on social media as learning data. In addition, the learning unit can incorporate posting content of cooking accounts that the user follows on social media as learning data. In addition, the learning unit can incorporate dishes that the user "liked" on social media as learning data. This makes it possible to learn related data based on social media activities. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that analyzes the user's social media activities and incorporates related data into the learning.

[0047] The generating unit can change the level of detail of the menu based on the importance of the ingredients when generating the menu. For example, the generating unit changes the level of detail of the menu based on the importance of the ingredients when generating the menu. The importance of ingredients includes, for example, nutritional value, frequency of use, etc., but is not limited to such examples. For example, the generating unit generates a detailed menu that focuses on main ingredients. In addition, the generating unit can generate a menu by simplifying information about supplementary ingredients. In addition, the generating unit can generate a menu that includes detailed explanations according to the importance of the ingredients. This makes it possible to adjust the level of detail of the menu according to the importance of the ingredients. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can use an AI model that changes the level of detail of the menu based on the importance of the ingredients.

[0048] The generating unit can use different generation algorithms depending on the category of ingredients when generating a menu. For example, the generating unit uses different generation algorithms depending on the category of ingredients when generating a menu. The categories of ingredients include, for example, vegetables, meat, fish, etc., but are not limited to such examples. For example, the generating unit applies a generation algorithm that emphasizes nutritional balance to a menu that is mainly made of vegetables. In addition, the generating unit can apply a generation algorithm that emphasizes cooking time to a menu that is mainly made of meat. In addition, the generating unit can apply a generation algorithm that emphasizes visual beauty to a menu that is mainly made of dessert. This makes it possible to apply an optimal generation algorithm depending on the category of ingredients. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can use an AI model that uses different generation algorithms depending on the category of ingredients.

[0049] The generating unit can set the priority order of the menu based on the purchase time of the ingredients when generating the menu. For example, the generating unit sets the priority order of the menu based on the purchase time of the ingredients when generating the menu. The purchase time of the ingredients includes, for example, the purchase date, the expiration date, and the like, but is not limited to such examples. For example, the generating unit generates a menu that uses ingredients that have just been purchased preferentially. In addition, the generating unit can generate a menu that uses ingredients that have been purchased a long time ago as soon as possible. In addition, the generating unit can generate a menu that takes into account the freshness of the ingredients based on the purchase time. This makes it possible to determine the priority order of the menu based on the purchase time of the ingredients. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can use an AI model that sets the priority order of the menu based on the purchase time of the ingredients.

[0050] The generating unit can change the order of the menu based on the relevance of ingredients when generating the menu. The generating unit, for example, changes the order of the menu based on the relevance of ingredients when generating the menu. The relevance of ingredients includes, for example, food compatibility and nutritional balance, but is not limited to such examples. For example, the generating unit generates a menu that combines related ingredients with a main ingredient as the center. The generating unit can also generate a menu with optimized cooking procedures based on the relevance of ingredients. The generating unit can also generate a balanced menu taking into account the relevance of ingredients. This makes it possible to adjust the order of the menu based on the relevance of ingredients. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can use an AI model that changes the order of the menu based on the relevance of ingredients.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The acquisition unit can analyze the user's past food ingredient purchase history and grasp the seasonal trends in food ingredient usage. For example, the acquisition unit can identify ingredients that the user frequently purchased in the summer in the past and generate a menu suitable for the summer. The acquisition unit can also analyze ingredients that the user prefers in the winter and propose a menu suitable for the winter. Furthermore, the acquisition unit can identify ingredients used for a specific event (e.g., a birthday or a holiday) from the user's past purchase history and generate a menu suitable for the event. This makes it possible to propose an optimal menu according to the season or event.

[0053] The reception unit can analyze background sounds and remove noise when the user inputs voice. For example, when the user inputs voice in a noisy environment, the reception unit filters out background sounds to obtain accurate voice data. When the user inputs voice in a quiet environment, the reception unit can adjust the sensitivity of voice recognition to improve input accuracy. Furthermore, when the user inputs voice while listening to music, the reception unit can minimize the influence of music to obtain accurate voice data. This improves the accuracy of voice input in various environments.

[0054] When generating a menu, the generation unit can generate the menu taking into consideration the user's ingredient allergy information. For example, if the user is allergic to a specific ingredient, the generation unit can propose a menu that does not include that ingredient. Also, if the user has multiple allergies, the generation unit can generate a menu that avoids those ingredients. Furthermore, the generation unit can propose a menu that uses alternative ingredients based on the user's allergy information. This makes it possible to propose a safe menu that takes the user's health into consideration.

[0055] The acquisition unit can analyze the user's past ingredient purchase history and preferentially acquire ingredients that are purchased frequently. For example, the acquisition unit can identify ingredients that the user frequently purchases and generate a menu using those ingredients. The acquisition unit can also analyze ingredients that the user purchases in a particular season and suggest a menu according to the season. Furthermore, the acquisition unit can identify ingredients used for a particular event (e.g., a birthday or a holiday) from the user's past purchase history and generate a menu suitable for the event. This makes it possible to suggest an optimal menu based on the user's purchase history.

[0056] The reception unit can analyze the tone and pitch of the user's speech during voice input to improve the accuracy of the input. For example, when the user speaks in a high tone, the reception unit adjusts the sensitivity of the voice recognition to accurately recognize the input. Also, when the user speaks in a low tone, the reception unit can adjust the sensitivity of the voice recognition to improve the accuracy of the input. Furthermore, the reception unit can analyze the pitch of the user's speech and optimize the voice recognition algorithm to improve the accuracy of the input. This makes it possible to improve the accuracy of the voice input according to the tone and pitch of the speech.

[0057] When generating a menu, the generation unit can generate the menu taking into consideration the storage conditions of ingredients of the user. For example, the generation unit can consider the freshness of ingredients stored by the user in the refrigerator and propose a menu that will be used soon. The generation unit can also propose a menu that uses ingredients stored by the user in the freezer. Furthermore, the generation unit can suggest an optimal cooking method according to the storage method used by the user. This makes it possible to propose an optimal menu based on the storage conditions of ingredients.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The reception unit receives information about purchased ingredients by voice from the user. For example, when the user inputs by voice, "I bought tomatoes, chicken, and pasta," this information is converted into text data using voice recognition technology. Step 2: The acquisition unit acquires information about the user from an application that the user is using. For example, the acquisition unit acquires the user's schedule from a calendar application, and the user's amount of exercise and sleep state from a health care application. Step 3: The learning unit learns the lifestyle or preferences of the user based on the information acquired by the acquisition unit. For example, the learning unit learns that the user prefers food with a short cooking time on busy days, or prefers food with fewer calories on days when the user exercises less. Step 4: The generation unit generates an appropriate menu based on the information on purchased ingredients received by the reception unit and the information on the user's lifestyle or preferences learned by the learning unit. For example, if the user purchases tomatoes, chicken, and pasta and it is a busy day, the generation unit suggests a simple pasta dish using tomatoes and chicken.

[0060] (Example 2) The menu generation system according to the embodiment of the present invention is a system that generates an optimal menu based on ingredients purchased by a user. In this system, the user provides information on purchased ingredients through voice input, and acquires information on the user's lifestyle, preferences, and health condition from other apps (such as a calendar or health care). In this way, a menu is generated that matches the user's lifestyle and preferences. For example, the user inputs information on the ingredients purchased by voice. For example, information such as "I bought tomatoes, chicken, and pasta" is input by voice. This information is converted into text data using voice recognition technology. Next, the system acquires information from other apps used by the user. For example, the user's schedule is acquired from a calendar app, and the user's amount of exercise and sleep state are acquired from a health care app. In this way, information on the user's lifestyle and health condition is collected. The system learns the user's lifestyle and preferences based on the collected information. For example, the system learns that the user prefers dishes with shorter cooking times on busy days, and prefers dishes with fewer calories on days when the user exercises less. Finally, the system generates an optimal menu based on the purchased ingredients and the learned information. For example, if the user purchases tomatoes, chicken, and pasta, and it is a busy day, the system suggests a simple pasta dish using tomatoes and chicken. It is also possible to suggest nutritionally balanced menus based on the user's health condition. As a result, the menu generation system automatically generates menus that match the user's lifestyle and preferences, allowing the user to enjoy healthy and delicious meals without any hassle. Furthermore, by using voice input, the user can easily provide ingredient information, improving convenience. As a result, the menu generation system can generate optimal menus based on the user's purchased ingredients and lifestyle.

[0061] The menu creation system according to the embodiment includes a reception unit, an acquisition unit, a learning unit, and a generation unit. The reception unit receives information about ingredients purchased by the user through voice. The ingredients purchased by the user include, but are not limited to, tomatoes, chicken, and pasta. For example, the reception unit inputs by voice, "I bought tomatoes, chicken, and pasta." This information is converted into text data using voice recognition technology. The acquisition unit acquires information about the user from an app used by the user. For example, the acquisition unit acquires the user's schedule from a calendar app, and the user's amount of exercise and sleep state from a health care app. This allows information about the user's lifestyle and health condition to be collected. The learning unit learns the user's lifestyle or preferences based on the information acquired by the acquisition unit. For example, the learning unit learns that the user prefers dishes with shorter cooking times on busy days and prefers dishes with fewer calories on days when the user exercises less. The generation unit generates an appropriate menu based on the information about purchased ingredients received by the reception unit and the information about the user's lifestyle or preferences learned by the learning unit. For example, if a user purchases tomatoes, chicken, and pasta and it is a busy day, the generation unit suggests a simple pasta dish using tomatoes and chicken. The generation unit can also suggest a nutritionally balanced menu based on the user's health condition. As a result, the menu generation system according to the embodiment can generate an optimal menu based on the user's purchased ingredients and lifestyle. Some or all of the above-mentioned processes in the reception unit, acquisition unit, learning unit, and generation unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can use an AI model that converts the user's voice input into text data using voice recognition technology. The acquisition unit can use an AI model that acquires information from a calendar app or a health care app. The learning unit can use an AI model that learns the user's lifestyle and preferences. The generation unit can use an AI model that generates a menu based on purchased ingredients and learned information.As a result, the menu creation system according to the embodiment can create an optimal menu based on the user's purchased ingredients and lifestyle.

[0062] The reception unit receives information about ingredients purchased by the user through voice. The ingredients purchased by the user include, but are not limited to, tomatoes, chicken, and pasta. For example, the user inputs "I bought tomatoes, chicken, and pasta" through voice into the reception unit. This information is converted into text data using voice recognition technology. Specifically, a voice recognition model using deep learning is used for the voice recognition technology. This model has learned a large amount of voice data and can convert voice into text with high accuracy. When the user inputs voice, the reception unit transmits the voice data to a server, where the voice recognition model processes the voice data. The voice data is first subjected to noise removal and volume normalization as preprocessing, and then input into the voice recognition model. The model analyzes the voice waveform, identifies phonemes and words, and converts them into text data. This text data is saved as a list of ingredients purchased by the user and is used for subsequent processing. Furthermore, the reception unit saves the user's voice input history and can improve the accuracy of voice recognition based on past input data. For example, by learning the ingredients that a particular user frequently purchases and their particular pronunciation habits, the accuracy of the voice recognition can be individually optimized. This allows the reception unit to convert the user's voice input into text data with high accuracy, improving the performance of the entire system.

[0063] The acquisition unit acquires information about the user from the application that the user is using. For example, the acquisition unit acquires the user's schedule from a calendar application, and acquires the user's amount of exercise and sleep state from a health application. This allows information about the user's lifestyle and health condition to be collected. Specifically, the acquisition unit acquires data using the API of each application. In the case of a calendar application, the acquisition unit acquires the user's schedule and event information, and identifies busy days and days when the user has free time. In the case of a health application, the acquisition unit acquires data such as the user's number of steps, calories burned, and sleep time, and grasps the user's health condition. These data are centrally managed by the acquisition unit and used to comprehensively evaluate the user's lifestyle and health condition. Furthermore, the acquisition unit can acquire data from other related applications and devices with the user's consent. For example, data from a smart watch or fitness tracker can be acquired to collect more detailed health information. This allows the acquisition unit to collect a variety of data about the user's lifestyle and health condition, and improve the accuracy and reliability of the entire system.

[0064] The learning unit learns the lifestyle or preferences of the user based on the information acquired by the acquisition unit. For example, the learning unit learns that the user prefers dishes with a short cooking time on busy days and prefers dishes with low calories on days when the user exercises less. Specifically, the learning unit uses a machine learning algorithm to analyze the user's behavioral patterns and preferences. For example, based on past data, a model is constructed that predicts what dishes the user will select under certain conditions. This model uses the user's schedule, health condition, past selection history, etc. as input data and can predict the user's preferences with high accuracy. Furthermore, the learning unit can continuously update the model based on the user's feedback to improve accuracy. For example, the user evaluates the proposed menu, and the evaluation data is reflected in the learning to improve the accuracy of the next proposal. In addition, the learning unit can also identify common patterns and trends by comparing and analyzing data from different users and build a more general preference model. This allows the learning unit to learn the user's lifestyle and preferences with high accuracy and improve the degree of personalization of the entire system.

[0065] The generation unit generates an appropriate menu based on the information on the purchased ingredients received by the reception unit and the information on the user's lifestyle or preferences learned by the learning unit. For example, if the user purchases tomatoes, chicken, and pasta and it is a busy day, the generation unit suggests a simple pasta dish using tomatoes and chicken. The generation unit can also suggest a nutritionally balanced menu based on the user's health condition. Specifically, the generation unit refers to a recipe database and searches for recipes that match the purchased ingredients. Furthermore, the generation unit filters and customizes recipes based on the user's lifestyle and preferences. For example, recipes with short cooking times and recipes with low calories are preferentially suggested. The generation unit can also generate and customize recipes using AI. For example, the generation unit can automatically generate recipe steps and ingredients using natural language processing technology and provide them to the user. Furthermore, the generation unit can continuously improve the proposed content based on user feedback and provide more appropriate menus. As a result, the generation unit can generate optimal menus based on the user's purchased ingredients and lifestyle, and support the user's eating habits.

[0066] The acquisition unit acquires the user's schedule from a calendar app, and the generation unit can generate a menu of dishes with short cooking times on busy days. The acquisition unit acquires the user's schedule from, for example, a calendar app. The user's schedule includes, for example, work plans and private plans, but is not limited to such examples. The generation unit generates, for example, a menu of dishes with short cooking times on busy days. Dishes with short cooking times include, for example, within 30 minutes, within 15 minutes, etc., but are not limited to such examples. For example, the generation unit can suggest a simple pasta dish using tomatoes and chicken on busy days. This makes it possible to reduce cooking time according to the user's schedule. A part or all of the above-mentioned processes in the acquisition unit and the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that acquires the user's schedule from a calendar app. The generation unit can use an AI model that generates a menu of dishes with short cooking times based on the user's schedule.

[0067] The acquisition unit acquires the user's amount of exercise and sleep state from a health care application, and the generation unit can propose a menu taking into account the health condition. The acquisition unit acquires, for example, the user's amount of exercise and sleep state from a health care application. The amount of exercise and sleep state include, for example, the number of steps, sleep time, and sleep quality, but are not limited to such examples. The generation unit proposes a menu taking into account, for example, the health condition of the user. Consideration of the health condition includes, for example, weight management, blood pressure management, and the like, but are not limited to such examples. For example, the generation unit can propose low-calorie dishes on days when the user does not exercise much. This makes it possible to propose a menu according to the user's health condition. A part or all of the above-mentioned processes in the acquisition unit and the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that acquires the user's amount of exercise and sleep state from a health care application. The generation unit can use an AI model that proposes a menu based on the user's health condition.

[0068] The reception unit can estimate the user's emotion and adjust the timing of the voice input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of the voice input based on the estimated user's emotion. The user's emotion includes, for example, joy, sadness, anger, etc., but is not limited to such examples. For example, when the user is stressed, the reception unit delays the timing of the voice input so that the user can input in a relaxed state. Furthermore, when the user is relaxed, the reception unit advances the timing of the voice input so that the input can proceed smoothly. Furthermore, when the user is in a hurry, the reception unit optimizes the timing of the voice input so that the input can be completed quickly. This makes it possible to optimize the timing of the voice input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can use an AI model to estimate the user's emotions.

[0069] The reception unit can improve the accuracy of the input by referring to the user's past input history during voice input. The reception unit can improve the accuracy of the input by referring to the user's past input history during voice input, for example. The past input history includes, for example, past voice commands, input contents, etc., but is not limited to such examples. For example, the reception unit automatically displays ingredients names that the user has frequently used in the past as candidates. The reception unit can also predict a specific combination of ingredients from the user's past input history and complement the input. The reception unit can also correct an ingredient name that the user has input incorrectly in the past and prompt the user to input it correctly. As a result, the accuracy of the voice input is improved by referring to the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that improves the accuracy of the input by referring to the user's past input history.

[0070] The reception unit can adjust the accuracy of the input based on the user's speech speed and volume when inputting voice. The reception unit adjusts the accuracy of the input based on the user's speech speed and volume when inputting voice, for example. The speech speed and volume include, but are not limited to, a range of speech speeds and a range of volume. For example, when the user speaks quickly, the reception unit increases the sensitivity of the voice recognition to accurately recognize the input. Furthermore, when the user speaks slowly, the reception unit can adjust the sensitivity of the voice recognition to improve the accuracy of the input. Furthermore, when the user speaks softly, the reception unit can automatically amplify the volume to accurately recognize the input. This makes it possible to adjust the accuracy of the voice input according to the speech speed and volume. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that adjusts the accuracy of the input based on the user's speech speed and volume.

[0071] The reception unit can estimate the user's emotion and select the contents of the voice input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and selects the contents of the voice input based on the estimated user's emotion. The selection of the contents of the voice input includes, for example, filtering criteria based on emotion, but is not limited to such an example. For example, when the user is stressed, the reception unit simplifies and accepts complex input contents. Furthermore, when the user is relaxed, the reception unit can accept detailed input contents. Furthermore, when the user is in a hurry, the reception unit can preferentially accept only important information. This enables filtering of the voice input contents according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such an example. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can use an AI model that estimates the user's emotions and selects the content of the voice input based on the estimated emotions.

[0072] The reception unit can preferentially accept highly relevant ingredient information in consideration of the geographical location information of the user when the voice is input. For example, the reception unit preferentially accepts highly relevant ingredient information in consideration of the geographical location information of the user when the voice is input. The geographical location information includes, for example, the current location and local specialty product information, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially accepts ingredient information that is often used in that area. Furthermore, when the user is traveling, the reception unit can preferentially accept ingredient information related to specialty products of the travel destination. Furthermore, when the user is at home, the reception unit can preferentially accept ingredient information that can be purchased at a nearby supermarket. This makes it possible to preferentially accept highly relevant ingredient information based on geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that preferentially accepts highly relevant ingredient information in consideration of the geographical location information of the user.

[0073] The reception unit can analyze the social media activity of the user at the time of voice input and accept related ingredient information. The reception unit can analyze the social media activity of the user at the time of voice input, for example, and accept related ingredient information. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. For example, the reception unit accepts related ingredient information based on recipes shared by the user on social media. In addition, the reception unit can analyze the content of posts of cooking accounts that the user follows on social media and accept related ingredient information. In addition, the reception unit can accept related ingredient information based on dishes that the user has "liked" on social media. This makes it possible to accept related ingredient information based on social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can use an AI model that analyzes the social media activity of the user and accepts related ingredient information.

[0074] The acquisition unit can analyze the user's past ingredient purchase history and select an appropriate acquisition method. The acquisition unit, for example, analyzes the user's past ingredient purchase history and selects an appropriate acquisition method. The past ingredient purchase history includes, for example, purchase date, purchase amount, purchase frequency, etc., but is not limited to such examples. For example, the acquisition unit preferentially acquires ingredients that the user has frequently purchased in the past. In addition, the acquisition unit can predict and acquire ingredients to be purchased in a specific season from the user's past purchase history. In addition, the acquisition unit can analyze the user's past purchase history and preferentially acquire ingredients that are purchased frequently. This makes it possible to select an optimal acquisition method based on the past purchase history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that analyzes the user's past ingredient purchase history and selects an appropriate acquisition method.

[0075] The acquisition unit can estimate the user's emotion and set a priority order of information to be acquired based on the estimated user's emotion. The acquisition unit can estimate the user's emotion, for example, and set a priority order of information to be acquired based on the estimated user's emotion. The priority order of information includes, for example, the intensity of emotion and the type of emotion, but is not limited to such examples. For example, when the user is stressed, the acquisition unit can preferentially acquire ingredient information that has a relaxing effect. Also, when the user is relaxed, the acquisition unit can preferentially acquire ingredient information that allows the user to try a new recipe. Also, when the user is in a hurry, the acquisition unit can preferentially acquire ingredient information that can be cooked in a short time. This makes it possible to determine the priority order of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can use an AI model that estimates a user's emotions and sets priorities for information to be acquired based on the estimated emotions.

[0076] The acquisition unit can prioritize acquisition of highly relevant information in consideration of the geographical location information of the user at the time of acquisition. For example, the acquisition unit prioritizes acquisition of highly relevant information in consideration of the geographical location information of the user at the time of acquisition. Highly relevant information includes, for example, geographically close information, local specialty product information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the acquisition unit prioritizes acquisition of food ingredient information that is often used in that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquisition of information on local specialty products of the travel destination. Furthermore, when the user is at home, the acquisition unit can prioritize acquisition of food ingredient information that can be purchased at a nearby supermarket. This makes it possible to prioritize acquisition of highly relevant information based on geographical location information. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that prioritizes acquisition of highly relevant information in consideration of the geographical location information of the user.

[0077] The acquisition unit can analyze the user's social media activity at the time of acquisition and collect related information. The acquisition unit, for example, analyzes the user's social media activity at the time of acquisition and collects related information. The related information includes, for example, the content of social media posts and the user's interests, but is not limited to such examples. For example, the acquisition unit acquires related ingredient information based on a recipe shared by the user on social media. In addition, the acquisition unit can analyze the content of posts of cooking accounts that the user follows on social media and acquire related ingredient information. In addition, the acquisition unit can acquire related ingredient information based on dishes that the user has "liked" on social media. This makes it possible to acquire related information based on social media activities. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can use an AI model that analyzes the user's social media activity and collects related information.

[0078] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. The learning unit, for example, estimates the user's emotion and selects learning data based on the estimated user's emotion. The learning data includes, for example, past purchase history, user's preference data, and the like, but is not limited to such examples. For example, when the user is stressed, the learning unit selects a recipe with a relaxing effect as learning data. Also, when the user is relaxed, the learning unit can select data that allows the user to try a new recipe as learning data. Also, when the user is in a hurry, the learning unit can select a recipe that can be cooked in a short time as learning data. This makes it possible to select learning data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can use an AI model that estimates a user's emotions and selects learning data based on the estimated emotions.

[0079] The learning unit can improve the learning algorithm by referring to past learning data during learning. The learning unit, for example, improves the learning algorithm by referring to past learning data during learning. The learning algorithm includes, for example, a neural network, a decision tree, and the like, but is not limited to such examples. For example, the learning unit preferentially learns recipes that match the user's preferences from the past learning data. In addition, the learning unit can analyze the past learning data and improve the accuracy of the learning algorithm. In addition, the learning unit can learn recipes that match the user's lifestyle based on the past learning data. This makes it possible to optimize the learning algorithm based on the past learning data. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that improves the learning algorithm by referring to past learning data.

[0080] The learning unit can revise the learning content in consideration of the user's ingredient purchase history and changes in lifestyle during learning. For example, the learning unit revise the learning content in consideration of the user's ingredient purchase history and changes in lifestyle during learning. Changes in lifestyle include, for example, changes in eating habits and changes in exercise habits, but are not limited to such examples. For example, the learning unit preferentially learns ingredients that are frequently purchased based on the user's ingredient purchase history. In addition, the learning unit can update the learning content in consideration of changes in the user's lifestyle (for example, moving or changing jobs). In addition, the learning unit can learn optimal recipes by combining the user's ingredient purchase history and changes in lifestyle. This makes it possible to update the learning content based on the ingredient purchase history and changes in lifestyle. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that revise the learning content in consideration of the user's ingredient purchase history and changes in lifestyle.

[0081] The learning unit can estimate the user's emotion and change the learning frequency based on the estimated user's emotion. The learning unit, for example, estimates the user's emotion and changes the learning frequency based on the estimated user's emotion. The learning frequency includes, for example, daily and weekly, but is not limited to such examples. For example, when the user is stressed, the learning unit reduces the learning frequency and performs learning in a relaxed state. Furthermore, when the user is relaxed, the learning unit can increase the learning frequency and efficiently proceed with the learning. Furthermore, when the user is in a hurry, the learning unit can optimize the learning frequency and quickly complete the learning. This makes it possible to adjust the learning frequency according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can use an AI model that estimates the user's emotions and changes the frequency of learning based on the estimated emotions.

[0082] The learning unit can weight the learning data in consideration of the geographical location information of the user during learning. For example, the learning unit weights the learning data in consideration of the geographical location information of the user during learning. Weighting of the learning data includes, for example, geographical proximity and regional characteristics, but is not limited to such examples. For example, when the user is in a specific region, the learning unit weights and learns ingredients that are frequently used in that region. In addition, when the user is traveling, the learning unit can weight and learn data related to local products of the travel destination. In addition, when the user is at home, the learning unit can weight and learn ingredients that can be purchased at a nearby supermarket. This makes it possible to weight the learning data based on the geographical location information. Part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that weights the learning data in consideration of the geographical location information of the user.

[0083] The learning unit can analyze the user's social media activities during learning and incorporate related data into the learning. For example, the learning unit can analyze the user's social media activities during learning and incorporate related data into the learning. The related data includes, for example, social media posting content, the user's interests, and the like, but is not limited to such examples. For example, the learning unit incorporates recipes shared by the user on social media as learning data. In addition, the learning unit can incorporate posting content of cooking accounts that the user follows on social media as learning data. In addition, the learning unit can incorporate dishes that the user "liked" on social media as learning data. This makes it possible to learn related data based on social media activities. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can use an AI model that analyzes the user's social media activities and incorporates related data into the learning.

[0084] The generation unit can estimate the user's emotion and change the way of expressing the menu based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and changes the way of expressing the menu based on the estimated user's emotion. The way of expressing the menu includes, for example, a text format, an image format, and the like, but is not limited to such examples. For example, when the user is stressed, the generation unit generates a simple and easy-to-read menu. Furthermore, when the user is relaxed, the generation unit can generate a menu including detailed explanations. Furthermore, when the user is in a hurry, the generation unit can generate a concise menu that focuses on the main points. This makes it possible to adjust the way of expressing the menu according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can use an AI model that estimates a user's emotions and changes the way the menu is presented based on the estimated emotions.

[0085] The generating unit can change the level of detail of the menu based on the importance of the ingredients when generating the menu. For example, the generating unit changes the level of detail of the menu based on the importance of the ingredients when generating the menu. The importance of ingredients includes, for example, nutritional value, frequency of use, etc., but is not limited to such examples. For example, the generating unit generates a detailed menu that focuses on main ingredients. In addition, the generating unit can generate a menu by simplifying information about supplementary ingredients. In addition, the generating unit can generate a menu that includes detailed explanations according to the importance of the ingredients. This makes it possible to adjust the level of detail of the menu according to the importance of the ingredients. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can use an AI model that changes the level of detail of the menu based on the importance of the ingredients.

[0086] The generating unit can use different generation algorithms depending on the category of ingredients when generating a menu. For example, the generating unit uses different generation algorithms depending on the category of ingredients when generating a menu. The categories of ingredients include, for example, vegetables, meat, fish, etc., but are not limited to such examples. For example, the generating unit applies a generation algorithm that emphasizes nutritional balance to a menu that is mainly made of vegetables. In addition, the generating unit can apply a generation algorithm that emphasizes cooking time to a menu that is mainly made of meat. In addition, the generating unit can apply a generation algorithm that emphasizes visual beauty to a menu that is mainly made of dessert. This makes it possible to apply an optimal generation algorithm depending on the category of ingredients. Part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can use an AI model that uses different generation algorithms depending on the category of ingredients.

[0087] The generation unit can estimate the user's emotion and change the length of the menu based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and changes the length of the menu based on the estimated user's emotion. The length of the menu includes, for example, the number of dishes and the level of detail of the cooking procedure, but is not limited to such examples. For example, the generation unit generates a short and concise menu when the user is stressed. Also, the generation unit can generate a longer menu including detailed explanations when the user is relaxed. Also, the generation unit can generate a short menu that focuses on the main points when the user is in a hurry. This makes it possible to adjust the length of the menu according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generator can use an AI model that estimates a user's emotions and changes the length of the menu based on the estimated emotions.

[0088] The generating unit can set the priority order of the menu based on the purchase time of the ingredients when generating the menu. For example, the generating unit sets the priority order of the menu based on the purchase time of the ingredients when generating the menu. The purchase time of the ingredients includes, for example, the purchase date, the expiration date, and the like, but is not limited to such examples. For example, the generating unit generates a menu that uses ingredients that have just been purchased preferentially. In addition, the generating unit can generate a menu that uses ingredients that have been purchased a long time ago as soon as possible. In addition, the generating unit can generate a menu that takes into account the freshness of the ingredients based on the purchase time. This makes it possible to determine the priority order of the menu based on the purchase time of the ingredients. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can use an AI model that sets the priority order of the menu based on the purchase time of the ingredients.

[0089] The generating unit can change the order of the menu based on the relevance of ingredients when generating the menu. The generating unit, for example, changes the order of the menu based on the relevance of ingredients when generating the menu. The relevance of ingredients includes, for example, food compatibility and nutritional balance, but is not limited to such examples. For example, the generating unit generates a menu that combines related ingredients with a main ingredient as the center. The generating unit can also generate a menu with optimized cooking procedures based on the relevance of ingredients. The generating unit can also generate a balanced menu taking into account the relevance of ingredients. This makes it possible to adjust the order of the menu based on the relevance of ingredients. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can use an AI model that changes the order of the menu based on the relevance of ingredients.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The acquisition unit can analyze the user's past food ingredient purchase history and grasp the seasonal trends in food ingredient usage. For example, the acquisition unit can identify ingredients that the user frequently purchased in the summer in the past and generate a menu suitable for the summer. The acquisition unit can also analyze ingredients that the user prefers in the winter and propose a menu suitable for the winter. Furthermore, the acquisition unit can identify ingredients used for a specific event (e.g., a birthday or a holiday) from the user's past purchase history and generate a menu suitable for the event. This makes it possible to propose an optimal menu according to the season or event.

[0092] The generation unit can estimate the user's emotions and adjust the difficulty of the menu based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit can suggest a menu that is simple and easy to make. When the user is relaxed, the generation unit can suggest a dish that requires a little effort. Furthermore, when the user is feeling adventurous, the generation unit can suggest a menu that uses new techniques or ingredients. This makes it possible to adjust the difficulty of the menu according to the user's emotions.

[0093] The reception unit can analyze background sounds and remove noise when the user inputs voice. For example, when the user inputs voice in a noisy environment, the reception unit filters out background sounds to obtain accurate voice data. When the user inputs voice in a quiet environment, the reception unit can adjust the sensitivity of voice recognition to improve input accuracy. Furthermore, when the user inputs voice while listening to music, the reception unit can minimize the influence of music to obtain accurate voice data. This improves the accuracy of voice input in various environments.

[0094] The acquisition unit can estimate the user's emotions and change the type of information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit can preferentially acquire ingredients and recipe information that have a relaxing effect. Also, when the user is relaxed, the acquisition unit can acquire new and challenging recipe information. Furthermore, when the user is in a hurry, the acquisition unit can preferentially acquire ingredients and recipe information that can be cooked in a short time. This makes it possible to acquire information according to the user's emotions.

[0095] When generating a menu, the generation unit can generate the menu taking into consideration the user's ingredient allergy information. For example, if the user is allergic to a specific ingredient, the generation unit can propose a menu that does not include that ingredient. Also, if the user has multiple allergies, the generation unit can generate a menu that avoids those ingredients. Furthermore, the generation unit can propose a menu that uses alternative ingredients based on the user's allergy information. This makes it possible to propose a safe menu that takes the user's health into consideration.

[0096] The reception unit can estimate the emotion of the user's speech during voice input and provide feedback on the input according to the emotion. For example, if the user feels anxious, the reception unit can display an encouraging message to encourage input. If the user feels happy, the reception unit can provide positive feedback to reinforce the input. If the user feels angry, the reception unit can support the input by displaying a message to calm down. This provides appropriate feedback according to the user's emotion, improving the voice input experience.

[0097] The acquisition unit can analyze the user's past ingredient purchase history and preferentially acquire ingredients that are purchased frequently. For example, the acquisition unit can identify ingredients that the user frequently purchases and generate a menu using those ingredients. The acquisition unit can also analyze ingredients that the user purchases in a particular season and suggest a menu according to the season. Furthermore, the acquisition unit can identify ingredients used for a particular event (e.g., a birthday or a holiday) from the user's past purchase history and generate a menu suitable for the event. This makes it possible to suggest an optimal menu based on the user's purchase history.

[0098] The generation unit can estimate the user's emotions and change the presentation method of the menu based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit generates a menu with a simple and easy-to-read layout. When the user is relaxed, the generation unit can generate a rich menu including detailed explanations and images. Furthermore, when the user is in a hurry, the generation unit can generate a concise menu that focuses on the main points. This makes it possible to present an optimal menu according to the user's emotions.

[0099] The reception unit can analyze the tone and pitch of the user's speech during voice input to improve the accuracy of the input. For example, when the user speaks in a high tone, the reception unit adjusts the sensitivity of the voice recognition to accurately recognize the input. Also, when the user speaks in a low tone, the reception unit can adjust the sensitivity of the voice recognition to improve the accuracy of the input. Furthermore, the reception unit can analyze the pitch of the user's speech and optimize the voice recognition algorithm to improve the accuracy of the input. This makes it possible to improve the accuracy of the voice input according to the tone and pitch of the speech.

[0100] When generating a menu, the generation unit can generate the menu taking into consideration the storage conditions of ingredients of the user. For example, the generation unit can consider the freshness of ingredients stored by the user in the refrigerator and propose a menu that will be used soon. The generation unit can also propose a menu that uses ingredients stored by the user in the freezer. Furthermore, the generation unit can suggest an optimal cooking method according to the storage method used by the user. This makes it possible to propose an optimal menu based on the storage conditions of ingredients.

[0101] The process flow of the second embodiment will be briefly described below.

[0102] Step 1: The reception unit receives information about purchased ingredients by voice from the user. For example, when the user inputs by voice, "I bought tomatoes, chicken, and pasta," this information is converted into text data using voice recognition technology. Step 2: The acquisition unit acquires information about the user from an application that the user is using. For example, the acquisition unit acquires the user's schedule from a calendar application, and the user's amount of exercise and sleep state from a health care application. Step 3: The learning unit learns the lifestyle or preferences of the user based on the information acquired by the acquisition unit. For example, the learning unit learns that the user prefers food with a short cooking time on busy days, or prefers food with fewer calories on days when the user exercises less. Step 4: The generation unit generates an appropriate menu based on the information on purchased ingredients received by the reception unit and the information on the user's lifestyle or preferences learned by the learning unit. For example, if the user purchases tomatoes, chicken, and pasta and it is a busy day, the generation unit suggests a simple pasta dish using tomatoes and chicken.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0105] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0106] For example, the reception unit is realized by the microphone 38B and the control unit 46A of the smart device 14. For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires information from a calendar application or a health care application. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the lifestyle and preferences of the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a menu based on purchased ingredients and learned information. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0114] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and the control unit 46A. For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires information from a calendar application or a health care application. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the lifestyle and preferences of the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a menu based on purchased ingredients and learned information. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and the control unit 46A. For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires information from a calendar application or a health care application. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the lifestyle and preferences of the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a menu based on purchased ingredients and learned information. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] For example, the reception unit is realized by the microphone 238 and the control unit 46A of the robot 414. For example, the acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires information from a calendar application or a health care application. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the lifestyle and preferences of the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a menu based on purchased ingredients and learned information. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0157] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0160] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0161] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0163] 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.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0166] 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.

[0167] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0168] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0172] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0174] (Appendix 1) a reception unit that receives information about purchased ingredients by voice from a user; An acquisition unit that acquires information about the user from an application that the user is using; A learning unit that learns a lifestyle or a preference of the user based on the information acquired by the acquisition unit; a generation unit that generates a menu based on information about purchased ingredients received by the reception unit and information about the lifestyle or preferences of the user learned by the learning unit. A system characterized by: (Appendix 2) The acquisition unit is Get the user's schedule from the calendar app, The generation unit is Generate menus with quick cooking times for busy days 2. The system of claim 1, (Appendix 3) The acquisition unit is Obtain the user's exercise and sleep status from the health app, The generation unit is Suggest a menu taking into account health conditions 2. The system of claim 1, (Appendix 4) The reception unit is Estimate the user's emotions, Adjusting the timing of voice input based on estimated user emotions 2. The system of claim 1, (Appendix 5) The reception unit is When entering voice, Improve input accuracy by referring to the user's past input history 2. The system of claim 1, (Appendix 6) The reception unit is When entering voice, Vary input accuracy based on the user's speech rate and volume 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions, Filtering voice input based on estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is When entering voice, Prioritize relevant food information based on the user's geographic location 2. The system of claim 1, (Appendix 9) The reception unit is When entering voice, Analyze users' social media activity and receive relevant food information 2. The system of claim 1, (Appendix 10) The acquisition unit is Analyze the user's past food purchase history, Choose the appropriate acquisition method 2. The system of claim 1, (Appendix 11) The acquisition unit is Estimate the user's emotions, Prioritize the information to be retrieved based on the estimated user's emotions 2. The system of claim 1, (Appendix 12) The acquisition unit is Upon acquisition, Prioritize the most relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 13) The acquisition unit is Upon acquisition, Analyze users' social media activities and collect related information 2. The system of claim 1, (Appendix 14) The learning unit is Estimate the user's emotions, Selecting training data based on estimated user emotions 2. The system of claim 1, (Appendix 15) The learning unit is When studying, Referencing past learning data to improve learning algorithms 2. The system of claim 1, (Appendix 16) The learning unit is When studying, Revise learning content based on user's food purchasing history and lifestyle changes 2. The system of claim 1, (Appendix 17) The learning unit is Estimate the user's emotions, Change the frequency of learning based on estimated user sentiment 2. The system of claim 1, (Appendix 18) The learning unit is When studying, Weighting training data based on user geographic location 2. The system of claim 1, (Appendix 19) The learning unit is When studying, Analyze users' social media activity and incorporate relevant data into learning 2. The system of claim 1, (Appendix 20) The generation unit is Estimate the user's emotions, Change the way the menu is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 21) The generation unit is When generating a menu, Change the level of detail in your menu based on the importance of ingredients 2. The system of claim 1, (Appendix 22) The generation unit is When generating a menu, Use different generation algorithms depending on the ingredient category 2. The system of claim 1, (Appendix 23) The generation unit is Estimate the user's emotions, Change the length of the menu based on the estimated user's emotions 2. The system of claim 1, (Appendix 24) The generation unit is When generating a menu, Prioritize your meals based on when you buy ingredients 2. The system of claim 1, (Appendix 25) The generation unit is When generating a menu, Reorder your meals based on ingredient relationships 2. The system of claim 1, [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about purchased ingredients by voice from a user; An acquisition unit that acquires information about the user from an application that the user is using; A learning unit that learns a lifestyle or a preference of the user based on the information acquired by the acquisition unit; and a generation unit that generates a menu based on information about purchased ingredients received by the reception unit and information about the lifestyle or preferences of the user learned by the learning unit. A system characterized by:

2. The acquisition unit is Obtaining the user's schedule from a calendar application; The generation unit is Generate menus with quick cooking times for busy days 2. The system of claim 1 .

3. The acquisition unit is Acquire the amount of exercise and sleep state of the user from a health care application; The generation unit is Suggest a menu taking into account health conditions 2. The system of claim 1 .

4. The reception unit is Estimating the user's emotion; The timing of the voice input is adjusted based on the estimated emotion of the user.

2. The system of claim 1 .

5. The reception unit is When inputting voice, the accuracy of the input is improved by referring to the user's past input history.

2. The system of claim 1 .

6. The reception unit is During voice input, the accuracy of the input is changed based on the user's speech rate and volume.

2. The system of claim 1 .

7. The reception unit is Estimating the user's emotion; Filtering the content of the voice input based on the estimated emotion of the user.

2. The system of claim 1 .

8. The reception unit is When the user inputs a voice message, the geographical location information of the user is taken into consideration and highly relevant ingredient information is preferentially accepted.

2. The system of claim 1 .

9. The reception unit is Upon voice input, the user's social media activity is analyzed and relevant food information is received.

2. The system of claim 1 .

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