System

Abstract

An object of the system according to the embodiment is to generate a menu according to individual needs of a user and create a shopping list.SOLUTION: A system according to an embodiment includes an input unit, a reception unit, a reference unit, a generation unit, and a list creation unit. The input unit inputs prior information. The reception unit receives a user's question by voice. The reference unit refers to the information received by the input unit. The generation unit generates a menu based on the information obtained by the reference unit. The list creation part creates a shopping list based on the menu created by the creation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently generate menus and create shopping lists that meet the individual needs of users.

[0005] The system according to the embodiment aims to generate a menu and create a shopping list according to the individual needs of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a reception unit, a reference unit, a generation unit, and a list creation unit. The input unit inputs advance information. The reception unit receives a user's questions by voice. The reference unit references the information received by the input unit. The creation unit creates a menu based on the information obtained by the reference unit. The list creation unit creates a shopping list based on the menu created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate menus and create shopping lists according to the individual needs of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A shopping assistance system according to an embodiment of the present invention allows users to shop while consulting with an AI via earphones. The shopping assistance system allows users to input information into the AI ​​in advance, such as the number of family members, each family member's likes and dislikes, allergies, and food intake, as well as their health status (e.g., obesity or high blood pressure). The AI ​​uses this information to suggest optimal menus for the user. For example, the user puts on earphones and consults with the AI ​​while shopping. For example, the user may ask a question such as, "What should I make for dinner tonight?" This question is input to the AI ​​via voice. The AI ​​then suggests optimal menus for the user based on the family information previously input. For example, if a family member has allergies, the AI ​​will suggest a menu that takes those allergies into consideration. It can also suggest low-calorie and low-salt menus based on the family's health status. Furthermore, the AI ​​creates a shopping list for the user and lists the necessary ingredients. For example, specific ingredients can be added to the list, such as "chicken, broccoli, carrots, soy sauce, and mirin." The user can use this list as a reference to efficiently proceed with their shopping. This allows the shopping support system to easily decide on a menu that takes into consideration the health and preferences of the family, without the user having to worry about shopping. In addition, by automatically creating a shopping list, users can prevent forgetting to buy items and shop efficiently. This allows the shopping support system to easily decide on a menu that takes into consideration the health and preferences of the family, and shop efficiently. For example, if there is someone in the family with high blood pressure, the AI ​​will suggest low-salt menus and list the necessary ingredients. The user can use this list as a reference when shopping in a health-conscious manner. In addition, if there is someone in the family with allergies, the AI ​​will suggest ingredients that take that allergy into consideration, preventing allergic reactions.

[0029] A shopping assistance system according to an embodiment includes an input unit, a reception unit, a reference unit, a generation unit, and a list creation unit. The input unit allows a user to input information such as the number of family members, likes and dislikes, allergies, food intake, and health status in advance. For example, the input unit may receive the number of family members via numerical input or voice input. The input unit may also receive likes and dislikes and allergy information in multiple-choice or free-form text. The input unit may also receive food intake via numerical input or voice input. For example, the input unit allows a user to input the number of family members numerically and likes and dislikes and allergy information in multiple-choice text. The input unit may also input food intake numerically and health status in free-form text. The reception unit is used when a user asks an AI question through earphones. For example, the reception unit may receive voice input from the earphones via Bluetooth (registered trademark). The reception unit may also analyze the user's question using voice recognition technology. The reception unit may also process voice data according to a voice input protocol. For example, the reception unit receives voice input from earphones via a Bluetooth connection and analyzes the user's question using voice recognition technology. The reception unit can also process the voice data according to a voice input protocol. The reference unit suggests an optimal menu in response to the user's question based on the information received by the input unit. For example, the reference unit searches for the input information using a database search method. The reference unit can also select the type of information to reference in response to the user's question. The reference unit can also search for the input information using a database search method and select the type of information to reference in response to the user's question. For example, the reference unit searches for the input information using a database search method and selects the type of information to reference in response to the user's question. The generation unit generates a specific menu based on the information obtained by the reference unit. For example, the generation unit generates a menu based on nutritional balance and the user's preferences. The generation unit can also generate a menu based on recipe details and ingredient selection criteria.Furthermore, the generation unit can generate a menu based on nutritional balance and user preferences, and can generate the menu based on recipe details and ingredient selection criteria. For example, the generation unit can generate a menu based on nutritional balance and user preferences, and can generate the menu based on recipe details and ingredient selection criteria. The list creation unit lists necessary ingredients based on the menu generated by the generation unit. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients. The list creation unit can also create a shopping list based on the list format and a method for selecting the necessary ingredients. The list creation unit can also create a shopping list based on the types and quantities of ingredients, and can also create a shopping list based on the list format and a method for selecting the necessary ingredients. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients, and can also create a shopping list based on the list format and a method for selecting the necessary ingredients. In this way, the shopping assistance system according to the embodiment allows the user to efficiently shop while consulting with AI during shopping.

[0030] The input unit can accept information on the number of family members, likes and dislikes, allergies, amount eaten, and health condition. The input unit accepts the number of family members using, for example, numerical input or voice input. For example, the input unit allows the user to numerically input the number of family members. The input unit can also accept likes and dislikes and allergy information in multiple-choice format or free-form text. For example, the input unit allows the user to input likes and dislikes and allergy information in multiple-choice format. The input unit can also accept the amount eaten in numerical input or multiple-choice format. For example, the input unit allows the user to numerically input the amount eaten. The input unit can also input the health condition in free-form text. For example, the input unit allows the user to input the health condition in free-form text. This allows the user to input detailed information about their family members in advance.

[0031] The reception unit can be used when a user asks an AI a question through earphones. The reception unit, for example, accepts voice input from the earphones using a Bluetooth connection. For example, the reception unit accepts voice input from the earphones using a Bluetooth connection. The reception unit can also analyze the user's question using voice recognition technology. For example, the reception unit analyzes the user's question using voice recognition technology. The reception unit can also process voice data according to a voice input protocol. For example, the reception unit processes voice data according to the voice input protocol. This allows the user to ask the AI ​​a question through the earphones.

[0032] The reference unit can suggest an appropriate menu in response to the user's question based on the information received by the input unit. The reference unit, for example, searches for input information using a database search method. For example, the reference unit searches for input information using a database search method. The reference unit can also select the type of information to reference in response to the user's question. For example, the reference unit can select the type of information to reference in response to the user's question. The reference unit can also search for input information using a database search method and select the type of information to reference in response to the user's question. For example, the reference unit can search for input information using a database search method and select the type of information to reference in response to the user's question. This makes it possible to suggest an optimal menu in response to the user's question.

[0033] The generation unit can generate a specific menu based on the information obtained by the reference unit. The generation unit generates a menu based on, for example, nutritional balance and user preferences. For example, the generation unit generates a menu based on nutritional balance and user preferences. The generation unit can also generate a menu based on recipe details and ingredient selection criteria. For example, the generation unit generates a menu based on recipe details and ingredient selection criteria. The generation unit can also generate a menu based on nutritional balance and user preferences, and generate a menu based on recipe details and ingredient selection criteria. For example, the generation unit generates a menu based on nutritional balance and user preferences, and generate a menu based on recipe details and ingredient selection criteria. In this way, a specific menu can be generated.

[0034] The list creation unit can list the necessary ingredients based on the menu generated by the generation unit. The list creation unit creates a shopping list based on, for example, the types and quantities of ingredients. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients. The list creation unit can also create a shopping list based on the format of the list and the method of selecting the necessary ingredients. For example, the list creation unit creates a shopping list based on the format of the list and the method of selecting the necessary ingredients. The list creation unit can also create a shopping list based on the types and quantities of ingredients, and create a shopping list based on the format of the list and the method of selecting the necessary ingredients. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients, and create a shopping list based on the format of the list and the method of selecting the necessary ingredients. In this way, the necessary ingredients can be listed.

[0035] The input unit can analyze the user's past input history at the time of input and suggest an appropriate input method. For example, the input unit automatically displays information that the user has frequently input in the past as a candidate. For example, the input unit automatically displays information that the user has frequently input in the past as a candidate. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest information to be input in a specific time period based on the user's past input history. For example, the input unit predicts and suggests information to be input in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0036] The input unit can adjust the timing of input based on the user's lifestyle rhythm when inputting data. For example, if the user has a habit of inputting data after breakfast, the input unit sends a notification prompting the user to input data at that time. For example, if the user has a habit of inputting data after breakfast, the input unit sends a notification prompting the user to input data at that time. In addition, if the user often inputs data at night, the input unit can also send a notification prompting the user to input data at that time. For example, if the user often inputs data at night, the input unit sends a notification prompting the user to input data at that time. In addition, the input unit can analyze the user's lifestyle rhythm and suggest the optimal input timing. For example, the input unit analyzes the user's lifestyle rhythm and suggests the optimal input timing. This makes it possible to adjust the timing of input based on the user's lifestyle rhythm.

[0037] The input unit can monitor fluctuations in the user's health condition in real time during input and automatically update the input content. For example, if the user's health condition worsens, the input unit automatically updates the input content and provides appropriate information. For example, if the user's health condition worsens, the input unit automatically updates the input content and provides appropriate information. The input unit can also automatically update the input content and provide appropriate information if the user's health condition improves. For example, if the user's health condition improves, the input unit automatically updates the input content and provides appropriate information. The input unit can also monitor fluctuations in the user's health condition in real time and automatically update the input content. For example, the input unit monitors fluctuations in the user's health condition in real time and automatically updates the input content. This makes it possible to monitor fluctuations in the user's health condition in real time and automatically update the input content.

[0038] The input unit can add region-specific ingredient information taking into account the user's geographical location information during input. For example, when the user is in a specific region, the input unit inputs region-specific ingredient information. For example, when the user is in a specific region, the input unit inputs region-specific ingredient information. Furthermore, when the user is traveling, the input unit can also input region-specific ingredient information for the user's travel destination. For example, when the user is traveling, the input unit inputs region-specific ingredient information for the user's travel destination. Furthermore, the input unit can also input optimal ingredient information based on the user's geographical location information. For example, the input unit inputs optimal ingredient information based on the user's geographical location information. This makes it possible to input optimal ingredient information based on the user's geographical location information.

[0039] The input unit can analyze the user's social media activity at the time of input and automatically input related information. The input unit, for example, inputs information about places where the user has checked in on social media. For example, the input unit inputs information about places where the user has checked in on social media. The input unit can also analyze the content of the user's posts on social media and input related information. For example, the input unit analyzes the content of the user's posts on social media and inputs related information. The input unit can also input related information by referring to the activities of the user's friends on social media. For example, the input unit inputs related information by referring to the activities of the user's friends on social media. This makes it possible to input related information based on the user's social media activity.

[0040] The input unit can customize the input method by reflecting the user's past feedback at the time of input. The input unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, the input unit suggests the optimal input method based on feedback provided by the user in the past. The input unit can also analyze the user's past feedback and customize the input method. For example, the input unit analyzes the user's past feedback and customizes the input method. The input unit can also reflect the user's feedback in real time and optimize the input method. For example, the input unit reflects the user's feedback in real time and optimizes the input method. In this way, the input method can be customized based on the user's past feedback.

[0041] The reception unit can analyze the user's past question history at the time of reception and suggest an optimal question format. The reception unit, for example, automatically displays questions that the user has frequently asked in the past as candidates. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the user has used in the past. For example, the reception unit preferentially suggests question formats (audio, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the reception unit predicts and suggests questions that will be asked in a specific time period based on the user's past question history. This makes it possible to suggest an optimal question format based on the user's past question history.

[0042] The reception unit can determine the priority of questions based on the user's current situation (e.g., the degree of congestion while shopping) when receiving the questions. For example, when the user is in a crowded place, the reception unit prioritizes receiving important questions. For example, when the user is in a crowded place, the reception unit prioritizes receiving important questions. The reception unit can also receive detailed questions when the user is in a quiet place. For example, when the user is in a quiet place, the reception unit can receive detailed questions. The reception unit can also analyze the user's current situation in real time and determine the optimal priority of questions. For example, the reception unit analyzes the user's current situation in real time and determines the optimal priority of questions. In this way, the priority of questions can be determined based on the user's current situation.

[0043] The reception unit analyzes the tone and speed of the user's voice when receiving the question, and can more accurately understand the intention of the question. For example, if the user's voice tone is high, the reception unit determines that the question is urgent and prioritizes responding to it. For example, if the user's voice tone is high, the reception unit determines that the question is urgent and prioritizes responding to it. In addition, if the user's voice speed is fast, the reception unit can determine that the user is in a hurry and respond quickly. For example, if the user's voice speed is fast, the reception unit can determine that the user is in a hurry and respond quickly. In addition, the reception unit can analyze the tone and speed of the user's voice in real time to accurately understand the intention of the question. For example, the reception unit analyzes the tone and speed of the user's voice in real time to accurately understand the intention of the question. This makes it possible to accurately understand the intention of the question based on the tone and speed of the user's voice.

[0044] The reception unit can select the optimal voice input method by taking into consideration the device information of the user at the time of reception. For example, if the user is using a smartphone, the reception unit provides voice input preferentially. For example, if the user is using a smartphone, the reception unit provides voice input preferentially. Furthermore, if the user is using a tablet, the reception unit can provide a voice input method optimized for a large screen. For example, if the user is using a tablet, the reception unit provides a voice input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible voice input method. For example, if the user is using a smartwatch, the reception unit provides a simple and highly visible voice input method. This makes it possible to select the optimal voice input method based on the device information of the user.

[0045] The reception unit can accept questions in multiple languages ​​according to the user's language setting at the time of reception. The reception unit automatically sets the language of the question based on, for example, the language setting of the user's device. For example, the reception unit automatically sets the language of the question based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. For example, the reception unit provides a language switching function when the user uses multiple languages. The reception unit can also accept questions in a specific language when the user selects that language. For example, the reception unit accepts questions in that language when the user selects a specific language. This makes it possible to accept questions in multiple languages ​​according to the user's language setting.

[0046] The reception unit can customize the question reception method by reflecting the user's past feedback when receiving a question. The reception unit, for example, suggests an optimal question reception method based on feedback provided by the user in the past. For example, the reception unit suggests an optimal question reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the question reception method. For example, the reception unit analyzes the user's past feedback and customizes the question reception method. The reception unit can also reflect the user's feedback in real time and optimize the question reception method. For example, the reception unit reflects the user's feedback in real time and optimizes the question reception method. In this way, the question reception method can be customized based on the user's past feedback.

[0047] The reference unit can analyze the user's past reference history at the time of reference and suggest the optimal reference method. For example, the reference unit automatically displays information that the user has frequently referenced in the past as a candidate. For example, the reference unit automatically displays information that the user has frequently referenced in the past as a candidate. The reference unit can also preferentially suggest reference methods (audio, text, etc.) that the user has used in the past. For example, the reference unit preferentially suggests reference methods (audio, text, etc.) that the user has used in the past. The reference unit can also predict and suggest information that will be referenced in a specific time period from the user's past reference history. For example, the reference unit predicts and suggests information that will be referenced in a specific time period from the user's past reference history. This makes it possible to suggest the optimal reference method based on the user's past reference history.

[0048] The reference unit can monitor the user's current health condition in real time during reference and automatically update the reference content. For example, if the user's health condition worsens, the reference unit automatically updates the reference content and provides appropriate information. For example, if the user's health condition worsens, the reference unit automatically updates the reference content and provides appropriate information. The reference unit can also automatically update the reference content and provide appropriate information if the user's health condition improves. For example, if the user's health condition improves, the reference unit automatically updates the reference content and provides appropriate information. The reference unit can also monitor fluctuations in the user's health condition in real time and automatically update the reference content. For example, the reference unit monitors fluctuations in the user's health condition in real time and automatically updates the reference content. This makes it possible to monitor fluctuations in the user's health condition in real time and automatically update the reference content.

[0049] The reference unit can adjust the timing of reference based on the user's lifestyle rhythm when making a reference. For example, if the user has a habit of referring to the information after breakfast, the reference unit sends a notification prompting the user to refer to the information during that time period ... In addition, if the user often refers to the information at night, the reference unit can also send a notification prompting the user to refer to the information during that time period. For example, if the user often refers to the information at night, the reference unit sends a notification prompting the user to refer to the information during that time period. In addition, the reference unit can analyze the user's lifestyle rhythm and suggest the optimal timing for reference. For example, the reference unit analyzes the user's lifestyle rhythm and suggests the optimal timing for reference. This makes it possible to adjust the timing for reference based on the user's lifestyle rhythm.

[0050] The reference unit can add region-specific ingredient information taking into account the user's geographical location information when making a reference. For example, when the user is in a specific region, the reference unit references region-specific ingredient information. For example, when the user is in a specific region, the reference unit references region-specific ingredient information. Furthermore, when the user is traveling, the reference unit can also reference region-specific ingredient information for the user's travel destination. For example, when the user is traveling, the reference unit references region-specific ingredient information for the user's travel destination. Furthermore, the reference unit can also reference optimal ingredient information based on the user's geographical location information. For example, the reference unit references optimal ingredient information based on the user's geographical location information. This makes it possible to reference optimal ingredient information based on the user's geographical location information.

[0051] At the time of reference, the reference unit can analyze the user's social media activity and automatically reference related information. The reference unit, for example, references information regarding places where the user has checked in on social media. For example, the reference unit references information regarding places where the user has checked in on social media. The reference unit can also analyze the content of the user's posts on social media and reference related information. For example, the reference unit analyzes the content of the user's posts on social media and reference related information. The reference unit can also refer to related information by referring to the activities of the user's friends on social media. For example, the reference unit refers to related information by referring to the activities of the user's friends on social media. This makes it possible to reference related information based on the user's social media activity.

[0052] The reference unit can customize the reference method by reflecting the user's past feedback at the time of reference. The reference unit, for example, suggests an optimal reference method based on feedback provided by the user in the past. For example, the reference unit suggests an optimal reference method based on feedback provided by the user in the past. The reference unit can also analyze the user's past feedback and customize the reference method. For example, the reference unit analyzes the user's past feedback and customize the reference method. The reference unit can also reflect the user's feedback in real time and optimize the reference method. For example, the reference unit reflects the user's feedback in real time and optimizes the reference method. In this way, the reference method can be customized based on the user's past feedback.

[0053] When generating the menu, the generation unit can analyze the user's past menu history and suggest an optimal menu. The generation unit, for example, suggests an optimal menu based on menus the user has made in the past. For example, the generation unit suggests an optimal menu based on menus the user has made in the past. The generation unit can also suggest a menu that suits the preferences of the family based on the user's past menu history. For example, the generation unit can suggest a menu that suits the preferences of the family based on the user's past menu history. The generation unit can also analyze the user's past menu history and suggest a balanced menu. For example, the generation unit analyzes the user's past menu history and suggests a balanced menu. This makes it possible to suggest an optimal menu based on the user's past menu history.

[0054] The generation unit can monitor the user's current health condition in real time during generation and automatically update the menu contents. For example, if the user's health condition worsens, the generation unit automatically updates the menu contents and suggests an appropriate menu. For example, if the user's health condition worsens, the generation unit automatically updates the menu contents and suggests an appropriate menu. The generation unit can also automatically update the menu contents and suggest an appropriate menu if the user's health condition improves. For example, if the user's health condition improves, the generation unit automatically updates the menu contents and suggests an appropriate menu. The generation unit can also monitor changes in the user's health condition in real time and automatically update the menu contents. For example, the generation unit monitors changes in the user's health condition in real time and automatically updates the menu contents. This makes it possible to monitor changes in the user's health condition in real time and automatically update the menu contents.

[0055] The generation unit can adjust the timing of the menu based on the user's lifestyle rhythm when generating the menu. For example, if the user has a habit of thinking about menus after breakfast, the generation unit suggests a menu for that time period. For example, if the user has a habit of thinking about menus after breakfast, the generation unit suggests a menu for that time period. In addition, if the user often thinks about menus at night, the generation unit can also suggest a menu for that time period. For example, if the user often thinks about menus at night, the generation unit suggests a menu for that time period. In addition, the generation unit can analyze the user's lifestyle rhythm and suggest the optimal menu timing. For example, the generation unit analyzes the user's lifestyle rhythm and suggests the optimal menu timing. In this way, the timing of the menu can be adjusted based on the user's lifestyle rhythm.

[0056] When generating the menu, the generation unit can propose a menu using ingredients specific to the region, taking into account the user's geographical location information. For example, when the user is in a specific region, the generation unit proposes a menu using ingredients specific to the region. For example, when the user is in a specific region, the generation unit proposes a menu using ingredients specific to the region. Furthermore, when the user is traveling, the generation unit can also propose a menu using ingredients specific to the region of the user's travel destination. For example, when the user is traveling, the generation unit proposes a menu using ingredients specific to the region of the user's travel destination. Furthermore, the generation unit can also propose a menu using optimal ingredients based on the user's geographical location information. For example, the generation unit proposes a menu using optimal ingredients based on the user's geographical location information. In this way, a menu using optimal ingredients can be proposed based on the user's geographical location information.

[0057] At the time of generation, the generation unit can analyze the user's social media activity and automatically generate a related menu. The generation unit generates a menu based on, for example, information about places where the user has checked in on social media. For example, the generation unit generates a menu based on information about places where the user has checked in on social media. The generation unit can also analyze the content of the user's posts on social media and generate a related menu. For example, the generation unit can analyze the content of the user's posts on social media and generate a related menu. The generation unit can also generate a related menu by referring to the activities of the user's friends on social media. For example, the generation unit generates a related menu by referring to the activities of the user's friends on social media. In this way, a related menu can be generated based on the user's social media activity.

[0058] The generation unit can customize the menu generation method by reflecting the user's past feedback during generation. The generation unit, for example, proposes an optimal menu generation method based on feedback provided by the user in the past. For example, the generation unit proposes an optimal menu generation method based on feedback provided by the user in the past. The generation unit can also analyze the user's past feedback and customize the menu generation method. For example, the generation unit analyzes the user's past feedback and customizes the menu generation method. The generation unit can also reflect the user's feedback in real time and optimize the menu generation method. For example, the generation unit reflects the user's feedback in real time and optimizes the menu generation method. In this way, the menu generation method can be customized based on the user's past feedback.

[0059] When creating a list, the list creation unit can analyze the user's past shopping history and suggest an optimal list. The list creation unit can, for example, suggest an optimal list based on ingredients purchased by the user in the past. For example, the list creation unit can suggest an optimal list based on ingredients purchased by the user in the past. The list creation unit can also add ingredients that suit the preferences of family members to the list based on the user's past shopping history. For example, the list creation unit can add ingredients that suit the preferences of family members to the list based on the user's past shopping history. The list creation unit can also analyze the user's past shopping history and suggest a balanced list. For example, the list creation unit can analyze the user's past shopping history and suggest a balanced list. In this way, it is possible to suggest an optimal list based on the user's past shopping history.

[0060] When creating the list, the list creation unit can monitor the user's current health condition in real time and automatically update the list contents. For example, if the user's health condition worsens, the list creation unit automatically updates the list contents and suggests appropriate ingredients. For example, if the user's health condition worsens, the list creation unit automatically updates the list contents and suggests appropriate ingredients. The list creation unit can also automatically update the list contents and suggest appropriate ingredients if the user's health condition improves. For example, if the user's health condition improves, the list creation unit automatically updates the list contents and suggests appropriate ingredients. The list creation unit can also monitor changes in the user's health condition in real time and automatically update the list contents. For example, the list creation unit monitors changes in the user's health condition in real time and automatically updates the list contents. This makes it possible to monitor changes in the user's health condition in real time and automatically update the list contents.

[0061] When creating a list, the list creation unit can adjust the timing of the list based on the user's lifestyle rhythm. For example, if the user has a habit of creating a list after breakfast, the list creation unit suggests a list for that time period. For example, if the user has a habit of creating a list after breakfast, the list creation unit suggests a list for that time period. Furthermore, if the user often creates lists at night, the list creation unit can also suggest a list for that time period. For example, if the user often creates lists at night, the list creation unit suggests a list for that time period. Furthermore, the list creation unit can analyze the user's lifestyle rhythm and suggest the optimal timing for creating a list. For example, the list creation unit analyzes the user's lifestyle rhythm and suggests the optimal timing for creating a list. This makes it possible to adjust the timing of the list based on the user's lifestyle rhythm.

[0062] When creating a list, the list creation unit can add ingredients specific to a region to the list taking into consideration the user's geographical location information. For example, when the user is in a specific region, the list creation unit adds ingredients specific to that region to the list. For example, when the user is in a specific region, the list creation unit adds ingredients specific to that region to the list. Furthermore, when the user is traveling, the list creation unit can also add ingredients specific to the region of the user's travel destination to the list. For example, when the user is traveling, the list creation unit adds ingredients specific to the region of the user's travel destination to the list. Furthermore, the list creation unit can also add optimal ingredients to the list based on the user's geographical location information. For example, the list creation unit adds optimal ingredients to the list based on the user's geographical location information. In this way, optimal ingredients can be added to the list based on the user's geographical location information.

[0063] When creating a list, the list creation unit can analyze the user's social media activity and add related ingredients to the list. The list creation unit can add ingredients to the list based on, for example, information about places where the user has checked in on social media. For example, the list creation unit can add ingredients to the list based on information about places where the user has checked in on social media. The list creation unit can also analyze the content of the user's social media posts and add related ingredients to the list. For example, the list creation unit can analyze the content of the user's social media posts and add related ingredients to the list. The list creation unit can also add related ingredients to the list based on the activity of the user's friends on social media. For example, the list creation unit can add related ingredients to the list based on the activity of the user's friends on social media. In this way, related ingredients can be added to the list based on the user's social media activity.

[0064] When creating a list, the list creation unit can customize the list creation method by reflecting the user's past feedback. The list creation unit, for example, suggests an optimal list creation method based on feedback provided by the user in the past. For example, the list creation unit suggests an optimal list creation method based on feedback provided by the user in the past. The list creation unit can also analyze the user's past feedback and customize the list creation method. For example, the list creation unit analyzes the user's past feedback and customizes the list creation method. The list creation unit can also reflect the user's feedback in real time and optimize the list creation method. For example, the list creation unit reflects the user's feedback in real time and optimizes the list creation method. In this way, the list creation method can be customized based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The shopping support system can also analyze the user's purchasing history and predict products that the user is likely to need based on past purchases and frequency of purchases, and add them to the list. For example, necessities such as milk and bread that the user purchases regularly can be automatically added to the list. It can also suggest products related to specific seasons or events (for example, Christmas cakes and decorations during Christmas). It can also add new or discounted products related to products the user has purchased in the past to the list. This allows users to shop efficiently without missing out on any products they need.

[0067] The input unit can suggest recipes for specific ingredients and dishes based on the user's dietary preferences and allergy information. For example, if the user prefers a specific ingredient (such as chicken or tomato), recipes using that ingredient will be suggested preferentially. It can also suggest alternative ingredients to avoid ingredients to which the user has allergies. Furthermore, it can suggest low-calorie or high-protein recipes based on the user's health condition. This allows users to easily prepare meals that suit their own and their family's preferences and health condition.

[0068] The reference unit can suggest regional ingredients and dishes based on the user's current geographic location information. For example, if the user is traveling, it can suggest local specialties and dishes from that region. Also, if the user lives in a specific region, it can suggest recipes using ingredients that are easily available in that region. It can also suggest seasonal ingredients and dishes. This allows the user to enjoy ingredients and dishes that are appropriate for the region and season.

[0069] The list creation unit can predict necessary ingredients based on the user's past shopping history and add them to the list. For example, ingredients that the user purchases regularly (such as milk or bread) can be automatically added to the list. It can also add new products or discounted items related to ingredients that the user has previously purchased to the list. It can also suggest ingredients related to specific seasons or events (such as Christmas cakes or decorations during the Christmas season). This allows the user to shop efficiently without missing any ingredients they need.

[0070] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently input in the past as a candidate. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information that will be input during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0071] The input unit can adjust the timing of input based on the user's lifestyle. For example, if the user has a habit of inputting data after breakfast, it can send a notification prompting them to input data at that time. Also, if the user often inputs data at night, it can send a notification prompting them to input data at that time. Furthermore, it can analyze the user's lifestyle and suggest the optimal input timing. This makes it possible to adjust the timing of input based on the user's lifestyle.

[0072] The input unit can monitor changes in the user's health condition in real time and automatically update the input content. For example, if the user's health condition worsens, the input content can be automatically updated and appropriate information can be provided. Also, if the user's health condition improves, the input content can be automatically updated and appropriate information can be provided. Furthermore, the input unit can monitor changes in the user's health condition in real time and automatically update the input content. This makes it possible to monitor changes in the user's health condition in real time and automatically update the input content.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The input unit receives information such as the number of family members, likes and dislikes, allergies, food intake, and health status in advance. For example, the input unit may accept the number of family members using numerical input or voice input. The input unit may also accept likes and dislikes and allergy information in multiple-choice or free-form text format. Furthermore, the input unit may also accept food intake in numerical input or multiple-choice format. Step 2: The reception unit is used when the user asks the AI ​​a question through earphones. For example, the reception unit receives voice input from the earphones using a Bluetooth connection. The reception unit can also analyze the user's question using voice recognition technology. Furthermore, the reception unit can process the voice data according to the voice input protocol. Step 3: The reference unit proposes the optimal menu based on the information received by the input unit in response to the user's question. For example, the reference unit searches for the input information using a database search method. The reference unit can also select the type of information to reference based on the user's question. Step 4: The generation unit generates a specific menu based on the information obtained by the reference unit. For example, the generation unit generates a menu based on nutritional balance and user preferences. The generation unit can also generate a menu based on recipe details and ingredient selection criteria. Step 5: The list creation unit creates a list of necessary ingredients based on the menu created by the creation unit. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients. The list creation unit can also create a shopping list based on the format of the list or the method of selecting the necessary ingredients.

[0075] (Example 2) A shopping assistance system according to an embodiment of the present invention allows users to shop while consulting with an AI via earphones. The shopping assistance system allows users to input information into the AI ​​in advance, such as the number of family members, each family member's likes and dislikes, allergies, and food intake, as well as their health status (e.g., obesity or high blood pressure). The AI ​​uses this information to suggest optimal menus for the user. For example, the user puts on earphones and consults with the AI ​​while shopping. For example, the user may ask a question such as, "What should I make for dinner tonight?" This question is input to the AI ​​via voice. The AI ​​then suggests optimal menus for the user based on the family information previously input. For example, if a family member has allergies, the AI ​​will suggest a menu that takes those allergies into consideration. It can also suggest low-calorie and low-salt menus based on the family's health status. Furthermore, the AI ​​creates a shopping list for the user and lists the necessary ingredients. For example, specific ingredients can be added to the list, such as "chicken, broccoli, carrots, soy sauce, and mirin." The user can use this list as a reference to efficiently proceed with their shopping. This allows the shopping support system to easily decide on a menu that takes into consideration the health and preferences of the family, without the user having to worry about shopping. In addition, by automatically creating a shopping list, users can prevent forgetting to buy items and shop efficiently. This allows the shopping support system to easily decide on a menu that takes into consideration the health and preferences of the family, and shop efficiently. For example, if there is someone in the family with high blood pressure, the AI ​​will suggest low-salt menus and list the necessary ingredients. The user can use this list as a reference when shopping in a health-conscious manner. In addition, if there is someone in the family with allergies, the AI ​​will suggest ingredients that take that allergy into consideration, preventing allergic reactions.

[0076] A shopping assistance system according to an embodiment includes an input unit, a reception unit, a reference unit, a generation unit, and a list creation unit. The input unit allows a user to input information such as the number of family members, likes and dislikes, allergies, food intake, and health status in advance. For example, the input unit may receive the number of family members via numerical input or voice input. The input unit may also receive likes and dislikes and allergy information in multiple-choice or free-form format. The input unit may also receive food intake via numerical input or multiple-choice format. For example, the input unit allows a user to input the number of family members numerically and likes and dislikes and allergy information in multiple-choice format. The input unit may also input food intake numerically and health status in free-form format. The reception unit is used when a user asks an AI question through earphones. For example, the reception unit may receive voice input from the earphones via a Bluetooth connection. The reception unit may also analyze the user's question using voice recognition technology. The reception unit may also process voice data according to a voice input protocol. For example, the reception unit receives voice input from earphones via a Bluetooth connection and analyzes the user's question using voice recognition technology. The reception unit can also process the voice data according to a voice input protocol. The reference unit suggests an optimal menu in response to the user's question based on the information received by the input unit. For example, the reference unit searches for the input information using a database search method. The reference unit can also select the type of information to reference in response to the user's question. The reference unit can also search for the input information using a database search method and select the type of information to reference in response to the user's question. For example, the reference unit searches for the input information using a database search method and selects the type of information to reference in response to the user's question. The generation unit generates a specific menu based on the information obtained by the reference unit. For example, the generation unit generates a menu based on nutritional balance and the user's preferences. The generation unit can also generate a menu based on recipe details and ingredient selection criteria.Furthermore, the generation unit can generate a menu based on nutritional balance and user preferences, and can generate the menu based on recipe details and ingredient selection criteria. For example, the generation unit can generate a menu based on nutritional balance and user preferences, and can generate the menu based on recipe details and ingredient selection criteria. The list creation unit lists necessary ingredients based on the menu generated by the generation unit. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients. The list creation unit can also create a shopping list based on the list format and a method for selecting the necessary ingredients. The list creation unit can also create a shopping list based on the types and quantities of ingredients, and can also create a shopping list based on the list format and a method for selecting the necessary ingredients. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients, and can also create a shopping list based on the list format and a method for selecting the necessary ingredients. In this way, the shopping assistance system according to the embodiment allows the user to efficiently shop while consulting with AI during shopping.

[0077] The input unit can accept information on the number of family members, likes and dislikes, allergies, amount eaten, and health condition. The input unit accepts the number of family members using, for example, numerical input or voice input. For example, the input unit allows the user to numerically input the number of family members. The input unit can also accept likes and dislikes and allergy information in multiple-choice format or free-form text. For example, the input unit allows the user to input likes and dislikes and allergy information in multiple-choice format. The input unit can also accept the amount eaten in numerical input or multiple-choice format. For example, the input unit allows the user to numerically input the amount eaten. The input unit can also input the health condition in free-form text. For example, the input unit allows the user to input the health condition in free-form text. This allows the user to input detailed information about their family members in advance.

[0078] The reception unit can be used when a user asks an AI a question through earphones. The reception unit, for example, accepts voice input from the earphones using a Bluetooth connection. For example, the reception unit accepts voice input from the earphones using a Bluetooth connection. The reception unit can also analyze the user's question using voice recognition technology. For example, the reception unit analyzes the user's question using voice recognition technology. The reception unit can also process voice data according to a voice input protocol. For example, the reception unit processes voice data according to the voice input protocol. This allows the user to ask the AI ​​a question through the earphones.

[0079] The reference unit can suggest an appropriate menu in response to the user's question based on the information received by the input unit. The reference unit, for example, searches for input information using a database search method. For example, the reference unit searches for input information using a database search method. The reference unit can also select the type of information to reference in response to the user's question. For example, the reference unit can select the type of information to reference in response to the user's question. The reference unit can also search for input information using a database search method and select the type of information to reference in response to the user's question. For example, the reference unit can search for input information using a database search method and select the type of information to reference in response to the user's question. This makes it possible to suggest an optimal menu in response to the user's question.

[0080] The generation unit can generate a specific menu based on the information obtained by the reference unit. The generation unit generates a menu based on, for example, nutritional balance and user preferences. For example, the generation unit generates a menu based on nutritional balance and user preferences. The generation unit can also generate a menu based on recipe details and ingredient selection criteria. For example, the generation unit generates a menu based on recipe details and ingredient selection criteria. The generation unit can also generate a menu based on nutritional balance and user preferences, and generate a menu based on recipe details and ingredient selection criteria. For example, the generation unit generates a menu based on nutritional balance and user preferences, and generate a menu based on recipe details and ingredient selection criteria. In this way, a specific menu can be generated.

[0081] The list creation unit can list the necessary ingredients based on the menu generated by the generation unit. The list creation unit creates a shopping list based on, for example, the types and quantities of ingredients. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients. The list creation unit can also create a shopping list based on the format of the list and the method of selecting the necessary ingredients. For example, the list creation unit creates a shopping list based on the format of the list and the method of selecting the necessary ingredients. The list creation unit can also create a shopping list based on the types and quantities of ingredients, and create a shopping list based on the format of the list and the method of selecting the necessary ingredients. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients, and create a shopping list based on the format of the list and the method of selecting the necessary ingredients. In this way, the necessary ingredients can be listed.

[0082] The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user's emotions. For example, when the user is feeling stressed, the input unit prioritizes input of important information (allergies and health condition). For example, when the user is feeling stressed, the input unit prioritizes input of important information (allergies and health condition). The input unit can also prioritize input of detailed information (likes and dislikes and amount of food eaten) when the user is relaxed. For example, when the user is relaxed, the input unit prioritizes input of detailed information (likes and dislikes and amount of food eaten). The input unit can also prioritize input of minimum information (number of family members and allergies) when the user is in a hurry. For example, when the user is in a hurry, the input unit prioritizes input of minimum information (number of family members and allergies). This makes it possible to determine the priority of information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The input unit can analyze the user's past input history at the time of input and suggest an appropriate input method. For example, the input unit automatically displays information that the user has frequently input in the past as a candidate. For example, the input unit automatically displays information that the user has frequently input in the past as a candidate. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest information to be input in a specific time period based on the user's past input history. For example, the input unit predicts and suggests information to be input in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0084] The input unit can adjust the timing of input based on the user's lifestyle rhythm when inputting data. For example, if the user has a habit of inputting data after breakfast, the input unit sends a notification prompting the user to input data at that time. For example, if the user has a habit of inputting data after breakfast, the input unit sends a notification prompting the user to input data at that time. In addition, if the user often inputs data at night, the input unit can also send a notification prompting the user to input data at that time. For example, if the user often inputs data at night, the input unit sends a notification prompting the user to input data at that time. In addition, the input unit can analyze the user's lifestyle rhythm and suggest the optimal input timing. For example, the input unit analyzes the user's lifestyle rhythm and suggests the optimal input timing. This makes it possible to adjust the timing of input based on the user's lifestyle rhythm.

[0085] The input unit can monitor fluctuations in the user's health condition in real time during input and automatically update the input content. For example, if the user's health condition worsens, the input unit automatically updates the input content and provides appropriate information. For example, if the user's health condition worsens, the input unit automatically updates the input content and provides appropriate information. The input unit can also automatically update the input content and provide appropriate information if the user's health condition improves. For example, if the user's health condition improves, the input unit automatically updates the input content and provides appropriate information. The input unit can also monitor fluctuations in the user's health condition in real time and automatically update the input content. For example, the input unit monitors fluctuations in the user's health condition in real time and automatically updates the input content. This makes it possible to monitor fluctuations in the user's health condition in real time and automatically update the input content.

[0086] The input unit can estimate the user's emotions and adjust the level of detail of the information to be input based on the estimated user's emotions. For example, when the user is feeling stressed, the input unit provides a concise input method. For example, when the user is feeling stressed, the input unit provides a concise input method. The input unit can also provide a detailed input method when the user is relaxed. For example, when the user is relaxed, the input unit provides a detailed input method. The input unit can also provide a method for inputting minimal information when the user is in a hurry. For example, when the user is in a hurry, the input unit provides a method for inputting minimal information. This makes it possible to adjust the level of detail of the information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The input unit can add region-specific ingredient information taking into account the user's geographical location information during input. For example, when the user is in a specific region, the input unit inputs region-specific ingredient information. For example, when the user is in a specific region, the input unit inputs region-specific ingredient information. Furthermore, when the user is traveling, the input unit can also input region-specific ingredient information for the user's travel destination. For example, when the user is traveling, the input unit inputs region-specific ingredient information for the user's travel destination. Furthermore, the input unit can also input optimal ingredient information based on the user's geographical location information. For example, the input unit inputs optimal ingredient information based on the user's geographical location information. This makes it possible to input optimal ingredient information based on the user's geographical location information.

[0088] The input unit can analyze the user's social media activity at the time of input and automatically input related information. The input unit, for example, inputs information about places where the user has checked in on social media. For example, the input unit inputs information about places where the user has checked in on social media. The input unit can also analyze the content of the user's posts on social media and input related information. For example, the input unit analyzes the content of the user's posts on social media and inputs related information. The input unit can also input related information by referring to the activities of the user's friends on social media. For example, the input unit inputs related information by referring to the activities of the user's friends on social media. This makes it possible to input related information based on the user's social media activity.

[0089] The input unit can customize the input method by reflecting the user's past feedback at the time of input. The input unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, the input unit suggests the optimal input method based on feedback provided by the user in the past. The input unit can also analyze the user's past feedback and customize the input method. For example, the input unit analyzes the user's past feedback and customizes the input method. The input unit can also reflect the user's feedback in real time and optimize the input method. For example, the input unit reflects the user's feedback in real time and optimizes the input method. In this way, the input method can be customized based on the user's past feedback.

[0090] The reception unit can estimate the user's emotions and adjust the method for accepting questions based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the number of question steps. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes the number of question steps. Furthermore, when the user is relaxed, the reception unit can provide detailed question options and suggest a customizable question method. For example, when the user is relaxed, the reception unit can provide detailed question options and suggest a customizable question method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly accept questions. For example, when the user is in a hurry, the reception unit prioritizes voice input and quickly accepts questions. This makes it possible to adjust the method for accepting questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The reception unit can analyze the user's past question history at the time of reception and suggest an optimal question format. The reception unit, for example, automatically displays questions that the user has frequently asked in the past as candidates. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the user has used in the past. For example, the reception unit preferentially suggests question formats (audio, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the reception unit predicts and suggests questions that will be asked in a specific time period based on the user's past question history. This makes it possible to suggest an optimal question format based on the user's past question history.

[0092] The reception unit can determine the priority of questions based on the user's current situation (e.g., the degree of congestion while shopping) when receiving the questions. For example, when the user is in a crowded place, the reception unit prioritizes receiving important questions. For example, when the user is in a crowded place, the reception unit prioritizes receiving important questions. The reception unit can also receive detailed questions when the user is in a quiet place. For example, when the user is in a quiet place, the reception unit can receive detailed questions. The reception unit can also analyze the user's current situation in real time and determine the optimal priority of questions. For example, the reception unit analyzes the user's current situation in real time and determines the optimal priority of questions. In this way, the priority of questions can be determined based on the user's current situation.

[0093] The reception unit analyzes the tone and speed of the user's voice when receiving the question, and can more accurately understand the intention of the question. For example, if the user's voice tone is high, the reception unit determines that the question is urgent and prioritizes responding to it. For example, if the user's voice tone is high, the reception unit determines that the question is urgent and prioritizes responding to it. In addition, if the user's voice speed is fast, the reception unit can determine that the user is in a hurry and respond quickly. For example, if the user's voice speed is fast, the reception unit can determine that the user is in a hurry and respond quickly. In addition, the reception unit can analyze the tone and speed of the user's voice in real time to accurately understand the intention of the question. For example, the reception unit analyzes the tone and speed of the user's voice in real time to accurately understand the intention of the question. This makes it possible to accurately understand the intention of the question based on the tone and speed of the user's voice.

[0094] The reception unit can estimate the user's emotions and filter the content of questions based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides concise questions. For example, when the user is feeling stressed, the reception unit provides concise questions. The reception unit can also provide detailed questions when the user is relaxed. For example, when the user is relaxed, the reception unit provides detailed questions. The reception unit can also prioritize providing important questions when the user is in a hurry. For example, when the user is in a hurry, the reception unit prioritizes providing important questions. This makes it possible to filter the content of questions according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The reception unit can select the optimal voice input method by taking into consideration the device information of the user at the time of reception. For example, if the user is using a smartphone, the reception unit provides voice input preferentially. For example, if the user is using a smartphone, the reception unit provides voice input preferentially. Furthermore, if the user is using a tablet, the reception unit can provide a voice input method optimized for a large screen. For example, if the user is using a tablet, the reception unit provides a voice input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible voice input method. For example, if the user is using a smartwatch, the reception unit provides a simple and highly visible voice input method. This makes it possible to select the optimal voice input method based on the device information of the user.

[0096] The reception unit can accept questions in multiple languages ​​according to the user's language setting at the time of reception. The reception unit automatically sets the language of the question based on, for example, the language setting of the user's device. For example, the reception unit automatically sets the language of the question based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. For example, the reception unit provides a language switching function when the user uses multiple languages. The reception unit can also accept questions in a specific language when the user selects that language. For example, the reception unit accepts questions in that language when the user selects a specific language. This makes it possible to accept questions in multiple languages ​​according to the user's language setting.

[0097] The reception unit can customize the question reception method by reflecting the user's past feedback when receiving a question. The reception unit, for example, suggests an optimal question reception method based on feedback provided by the user in the past. For example, the reception unit suggests an optimal question reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the question reception method. For example, the reception unit analyzes the user's past feedback and customizes the question reception method. The reception unit can also reflect the user's feedback in real time and optimize the question reception method. For example, the reception unit reflects the user's feedback in real time and optimizes the question reception method. In this way, the question reception method can be customized based on the user's past feedback.

[0098] The reference unit can estimate the user's emotions and determine the priority of information to be referenced based on the estimated user's emotions. For example, when the user is feeling stressed, the reference unit prioritizes reference to important information (allergies and health status). For example, when the user is feeling stressed, the reference unit prioritizes reference to important information (allergies and health status). In addition, when the user is relaxed, the reference unit can prioritize reference to detailed information (likes and dislikes and amount of food eaten). For example, when the user is relaxed, the reference unit prioritizes reference to detailed information (likes and dislikes and amount of food eaten). In addition, when the user is in a hurry, the reference unit can prioritize reference to minimum information (number of family members and allergies). For example, when the user is in a hurry, the reference unit prioritizes reference to minimum information (number of family members and allergies). This makes it possible to determine the priority of information to be referenced according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The reference unit can analyze the user's past reference history at the time of reference and suggest the optimal reference method. For example, the reference unit automatically displays information that the user has frequently referenced in the past as a candidate. For example, the reference unit automatically displays information that the user has frequently referenced in the past as a candidate. The reference unit can also preferentially suggest reference methods (audio, text, etc.) that the user has used in the past. For example, the reference unit preferentially suggests reference methods (audio, text, etc.) that the user has used in the past. The reference unit can also predict and suggest information that will be referenced in a specific time period from the user's past reference history. For example, the reference unit predicts and suggests information that will be referenced in a specific time period from the user's past reference history. This makes it possible to suggest the optimal reference method based on the user's past reference history.

[0100] The reference unit can monitor the user's current health condition in real time during reference and automatically update the reference content. For example, if the user's health condition worsens, the reference unit automatically updates the reference content and provides appropriate information. For example, if the user's health condition worsens, the reference unit automatically updates the reference content and provides appropriate information. The reference unit can also automatically update the reference content and provide appropriate information if the user's health condition improves. For example, if the user's health condition improves, the reference unit automatically updates the reference content and provides appropriate information. The reference unit can also monitor fluctuations in the user's health condition in real time and automatically update the reference content. For example, the reference unit monitors fluctuations in the user's health condition in real time and automatically updates the reference content. This makes it possible to monitor fluctuations in the user's health condition in real time and automatically update the reference content.

[0101] The reference unit can adjust the timing of reference based on the user's lifestyle rhythm when making a reference. For example, if the user has a habit of referring to the information after breakfast, the reference unit sends a notification prompting the user to refer to the information during that time period ... In addition, if the user often refers to the information at night, the reference unit can also send a notification prompting the user to refer to the information during that time period. For example, if the user often refers to the information at night, the reference unit sends a notification prompting the user to refer to the information during that time period. In addition, the reference unit can analyze the user's lifestyle rhythm and suggest the optimal timing for reference. For example, the reference unit analyzes the user's lifestyle rhythm and suggests the optimal timing for reference. This makes it possible to adjust the timing for reference based on the user's lifestyle rhythm.

[0102] The reference unit can estimate the user's emotions and adjust the level of detail of the referenced information based on the estimated user's emotions. For example, if the user is feeling stressed, the reference unit provides concise information. For example, if the user is feeling stressed, the reference unit provides concise information. The reference unit can also provide detailed information if the user is relaxed. For example, if the user is feeling relaxed, the reference unit provides detailed information. The reference unit can also prioritize providing important information if the user is in a hurry. For example, if the user is in a hurry, the reference unit prioritizes providing important information. This makes it possible to adjust the level of detail of the referenced information depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The reference unit can add region-specific ingredient information taking into account the user's geographical location information when making a reference. For example, when the user is in a specific region, the reference unit references region-specific ingredient information. For example, when the user is in a specific region, the reference unit references region-specific ingredient information. Furthermore, when the user is traveling, the reference unit can also reference region-specific ingredient information for the user's travel destination. For example, when the user is traveling, the reference unit references region-specific ingredient information for the user's travel destination. Furthermore, the reference unit can also reference optimal ingredient information based on the user's geographical location information. For example, the reference unit references optimal ingredient information based on the user's geographical location information. This makes it possible to reference optimal ingredient information based on the user's geographical location information.

[0104] At the time of reference, the reference unit can analyze the user's social media activity and automatically reference related information. The reference unit, for example, references information regarding places where the user has checked in on social media. For example, the reference unit references information regarding places where the user has checked in on social media. The reference unit can also analyze the content of the user's posts on social media and reference related information. For example, the reference unit analyzes the content of the user's posts on social media and reference related information. The reference unit can also refer to related information by referring to the activities of the user's friends on social media. For example, the reference unit refers to related information by referring to the activities of the user's friends on social media. This makes it possible to reference related information based on the user's social media activity.

[0105] The reference unit can customize the reference method by reflecting the user's past feedback at the time of reference. The reference unit, for example, suggests an optimal reference method based on feedback provided by the user in the past. For example, the reference unit suggests an optimal reference method based on feedback provided by the user in the past. The reference unit can also analyze the user's past feedback and customize the reference method. For example, the reference unit analyzes the user's past feedback and customize the reference method. The reference unit can also reflect the user's feedback in real time and optimize the reference method. For example, the reference unit reflects the user's feedback in real time and optimizes the reference method. In this way, the reference method can be customized based on the user's past feedback.

[0106] The generation unit can estimate the user's emotions and adjust the menu generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit suggests a menu that is simple and easy to make. For example, if the user is feeling stressed, the generation unit suggests a menu that is simple and easy to make. The generation unit can also suggest a menu that can be made over time if the user is relaxed. For example, if the user is relaxed, the generation unit suggests a menu that can be made over time. The generation unit can also suggest a menu that can be made in a short amount of time if the user is in a hurry. For example, if the user is in a hurry, the generation unit suggests a menu that can be made in a short amount of time. This makes it possible to adjust the menu generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] When generating the menu, the generation unit can analyze the user's past menu history and suggest an optimal menu. The generation unit, for example, suggests an optimal menu based on menus the user has made in the past. For example, the generation unit suggests an optimal menu based on menus the user has made in the past. The generation unit can also suggest a menu that suits the preferences of the family based on the user's past menu history. For example, the generation unit can suggest a menu that suits the preferences of the family based on the user's past menu history. The generation unit can also analyze the user's past menu history and suggest a balanced menu. For example, the generation unit analyzes the user's past menu history and suggests a balanced menu. This makes it possible to suggest an optimal menu based on the user's past menu history.

[0108] The generation unit can monitor the user's current health condition in real time during generation and automatically update the menu contents. For example, if the user's health condition worsens, the generation unit automatically updates the menu contents and suggests an appropriate menu. For example, if the user's health condition worsens, the generation unit automatically updates the menu contents and suggests an appropriate menu. The generation unit can also automatically update the menu contents and suggest an appropriate menu if the user's health condition improves. For example, if the user's health condition improves, the generation unit automatically updates the menu contents and suggests an appropriate menu. The generation unit can also monitor changes in the user's health condition in real time and automatically update the menu contents. For example, the generation unit monitors changes in the user's health condition in real time and automatically updates the menu contents. This makes it possible to monitor changes in the user's health condition in real time and automatically update the menu contents.

[0109] The generation unit can adjust the timing of the menu based on the user's lifestyle rhythm when generating the menu. For example, if the user has a habit of thinking about menus after breakfast, the generation unit suggests a menu for that time period. For example, if the user has a habit of thinking about menus after breakfast, the generation unit suggests a menu for that time period. In addition, if the user often thinks about menus at night, the generation unit can also suggest a menu for that time period. For example, if the user often thinks about menus at night, the generation unit suggests a menu for that time period. In addition, the generation unit can analyze the user's lifestyle rhythm and suggest the optimal menu timing. For example, the generation unit analyzes the user's lifestyle rhythm and suggests the optimal menu timing. In this way, the timing of the menu can be adjusted based on the user's lifestyle rhythm.

[0110] The generation unit can estimate the user's emotions and adjust the level of detail of the menu based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit provides a concise menu. For example, if the user is feeling stressed, the generation unit provides a concise menu. The generation unit can also provide a detailed menu when the user is relaxed. For example, if the user is relaxed, the generation unit provides a detailed menu. The generation unit can also prioritize providing important information when the user is in a hurry. For example, if the user is in a hurry, the generation unit prioritizes providing important information. This makes it possible to adjust the level of detail of the menu according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] When generating the menu, the generation unit can propose a menu using ingredients specific to the region, taking into account the user's geographical location information. For example, when the user is in a specific region, the generation unit proposes a menu using ingredients specific to the region. For example, when the user is in a specific region, the generation unit proposes a menu using ingredients specific to the region. Furthermore, when the user is traveling, the generation unit can also propose a menu using ingredients specific to the region of the user's travel destination. For example, when the user is traveling, the generation unit proposes a menu using ingredients specific to the region of the user's travel destination. Furthermore, the generation unit can also propose a menu using optimal ingredients based on the user's geographical location information. For example, the generation unit proposes a menu using optimal ingredients based on the user's geographical location information. In this way, a menu using optimal ingredients can be proposed based on the user's geographical location information.

[0112] At the time of generation, the generation unit can analyze the user's social media activity and automatically generate a related menu. The generation unit generates a menu based on, for example, information about places where the user has checked in on social media. For example, the generation unit generates a menu based on information about places where the user has checked in on social media. The generation unit can also analyze the content of the user's posts on social media and generate a related menu. For example, the generation unit can analyze the content of the user's posts on social media and generate a related menu. The generation unit can also generate a related menu by referring to the activities of the user's friends on social media. For example, the generation unit generates a related menu by referring to the activities of the user's friends on social media. In this way, a related menu can be generated based on the user's social media activity.

[0113] The generation unit can customize the menu generation method by reflecting the user's past feedback during generation. The generation unit, for example, proposes an optimal menu generation method based on feedback provided by the user in the past. For example, the generation unit proposes an optimal menu generation method based on feedback provided by the user in the past. The generation unit can also analyze the user's past feedback and customize the menu generation method. For example, the generation unit analyzes the user's past feedback and customizes the menu generation method. The generation unit can also reflect the user's feedback in real time and optimize the menu generation method. For example, the generation unit reflects the user's feedback in real time and optimizes the menu generation method. In this way, the menu generation method can be customized based on the user's past feedback.

[0114] The list creation unit can estimate the user's emotions and adjust the shopping list creation method based on the estimated user emotions. For example, the list creation unit provides a concise shopping list when the user is feeling stressed. For example, the list creation unit provides a concise shopping list when the user is feeling stressed. The list creation unit can also provide a detailed shopping list when the user is relaxed. For example, the list creation unit provides a detailed shopping list when the user is relaxed. The list creation unit can also prioritize adding important ingredients to the list when the user is in a hurry. For example, the list creation unit prioritizes adding important ingredients to the list when the user is in a hurry. This makes it possible to adjust the shopping list creation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] When creating a list, the list creation unit can analyze the user's past shopping history and suggest an optimal list. The list creation unit can, for example, suggest an optimal list based on ingredients purchased by the user in the past. For example, the list creation unit can suggest an optimal list based on ingredients purchased by the user in the past. The list creation unit can also add ingredients that suit the preferences of family members to the list based on the user's past shopping history. For example, the list creation unit can add ingredients that suit the preferences of family members to the list based on the user's past shopping history. The list creation unit can also analyze the user's past shopping history and suggest a balanced list. For example, the list creation unit can analyze the user's past shopping history and suggest a balanced list. In this way, it is possible to suggest an optimal list based on the user's past shopping history.

[0116] When creating the list, the list creation unit can monitor the user's current health condition in real time and automatically update the list contents. For example, if the user's health condition worsens, the list creation unit automatically updates the list contents and suggests appropriate ingredients. For example, if the user's health condition worsens, the list creation unit automatically updates the list contents and suggests appropriate ingredients. The list creation unit can also automatically update the list contents and suggest appropriate ingredients if the user's health condition improves. For example, if the user's health condition improves, the list creation unit automatically updates the list contents and suggests appropriate ingredients. The list creation unit can also monitor changes in the user's health condition in real time and automatically update the list contents. For example, the list creation unit monitors changes in the user's health condition in real time and automatically updates the list contents. This makes it possible to monitor changes in the user's health condition in real time and automatically update the list contents.

[0117] When creating a list, the list creation unit can adjust the timing of the list based on the user's lifestyle rhythm. For example, if the user has a habit of creating a list after breakfast, the list creation unit suggests a list for that time period. For example, if the user has a habit of creating a list after breakfast, the list creation unit suggests a list for that time period. Furthermore, if the user often creates lists at night, the list creation unit can also suggest a list for that time period. For example, if the user often creates lists at night, the list creation unit suggests a list for that time period. Furthermore, the list creation unit can analyze the user's lifestyle rhythm and suggest the optimal timing for creating a list. For example, the list creation unit analyzes the user's lifestyle rhythm and suggests the optimal timing for creating a list. This makes it possible to adjust the timing of the list based on the user's lifestyle rhythm.

[0118] The list creation unit can estimate the user's emotions and adjust the level of detail of the list based on the estimated user's emotions. For example, the list creation unit provides a concise list when the user is feeling stressed. For example, the list creation unit provides a concise list when the user is feeling stressed. The list creation unit can also provide a detailed list when the user is relaxed. For example, the list creation unit provides a detailed list when the user is relaxed. The list creation unit can also prioritize adding important ingredients to the list when the user is in a hurry. For example, the list creation unit prioritizes adding important ingredients to the list when the user is in a hurry. This makes it possible to adjust the level of detail of the list according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] When creating a list, the list creation unit can add ingredients specific to a region to the list taking into consideration the user's geographical location information. For example, when the user is in a specific region, the list creation unit adds ingredients specific to that region to the list. For example, when the user is in a specific region, the list creation unit adds ingredients specific to that region to the list. Furthermore, when the user is traveling, the list creation unit can also add ingredients specific to the region of the user's travel destination to the list. For example, when the user is traveling, the list creation unit adds ingredients specific to the region of the user's travel destination to the list. Furthermore, the list creation unit can also add optimal ingredients to the list based on the user's geographical location information. For example, the list creation unit adds optimal ingredients to the list based on the user's geographical location information. In this way, optimal ingredients can be added to the list based on the user's geographical location information.

[0120] When creating a list, the list creation unit can analyze the user's social media activity and add related ingredients to the list. The list creation unit can add ingredients to the list based on, for example, information about places where the user has checked in on social media. For example, the list creation unit can add ingredients to the list based on information about places where the user has checked in on social media. The list creation unit can also analyze the content of the user's social media posts and add related ingredients to the list. For example, the list creation unit can analyze the content of the user's social media posts and add related ingredients to the list. The list creation unit can also add related ingredients to the list based on the activity of the user's friends on social media. For example, the list creation unit can add related ingredients to the list based on the activity of the user's friends on social media. In this way, related ingredients can be added to the list based on the user's social media activity.

[0121] When creating a list, the list creation unit can customize the list creation method by reflecting the user's past feedback. The list creation unit, for example, suggests an optimal list creation method based on feedback provided by the user in the past. For example, the list creation unit suggests an optimal list creation method based on feedback provided by the user in the past. The list creation unit can also analyze the user's past feedback and customize the list creation method. For example, the list creation unit analyzes the user's past feedback and customizes the list creation method. The list creation unit can also reflect the user's feedback in real time and optimize the list creation method. For example, the list creation unit reflects the user's feedback in real time and optimizes the list creation method. In this way, the list creation method can be customized based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, reception unit, reference unit, generation unit, and list creation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14, and the user inputs family information. The reception unit, for example, receives audio input from earphones via the communication I / F 44 of the smart device 14. The reference unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and proposes an optimal menu based on the input information. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and generates a specific menu. The list creation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and lists the necessary ingredients. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, reception unit, reference unit, generation unit, and list creation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214, and the user inputs family information. The reception unit, for example, receives audio input from earphones via the communication I / F 44 of the smart glasses 214. The reference unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and proposes an optimal menu based on the input information. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and generates a specific menu. The list creation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and lists the necessary ingredients. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, reception unit, reference unit, generation unit, and list creation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset terminal 314, and the user inputs family information. The reception unit, for example, receives audio input from earphones via the communication I / F 44 of the headset terminal 314. The reference unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and proposes an optimal menu based on the input information. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and generates a specific menu. The list creation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and lists the necessary ingredients. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, reception unit, reference unit, generation unit, and list creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414, and the user inputs family information. The reception unit, for example, receives audio input from earphones via the communication I / F 44 of the robot 414. The reference unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and proposes an optimal menu based on the input information. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and generates a specific menu. The list creation unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and lists the necessary ingredients.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The shopping support system can also analyze the user's purchasing history and predict products that the user is likely to need based on past purchases and frequency of purchases, and add them to the list. For example, necessities such as milk and bread that the user purchases regularly can be automatically added to the list. It can also suggest products related to specific seasons or events (for example, Christmas cakes and decorations during Christmas). It can also add new or discounted products related to products the user has purchased in the past to the list. This allows users to shop efficiently without missing out on any products they need.

[0124] The input unit can suggest recipes for specific ingredients and dishes based on the user's dietary preferences and allergy information. For example, if the user prefers a specific ingredient (such as chicken or tomato), recipes using that ingredient will be suggested preferentially. It can also suggest alternative ingredients to avoid ingredients to which the user has allergies. Furthermore, it can suggest low-calorie or high-protein recipes based on the user's health condition. This allows users to easily prepare meals that suit their own and their family's preferences and health condition.

[0125] The reception unit can analyze the user's voice input and the tone and speed of the voice to more accurately understand the user's intention. For example, if the user is in a hurry, the voice speed will likely increase, allowing for a quick response. Also, if the user is relaxed, the voice tone will likely become calmer, allowing for more detailed questions to be received. Furthermore, if the user is feeling stressed, the voice tone will likely increase, allowing for more concise questions to be received preferentially. This makes it possible to provide an appropriate response according to the user's situation.

[0126] The reference unit can suggest regional ingredients and dishes based on the user's current geographic location information. For example, if the user is traveling, it can suggest local specialties and dishes from that region. Also, if the user lives in a specific region, it can suggest recipes using ingredients that are easily available in that region. It can also suggest seasonal ingredients and dishes. This allows the user to enjoy ingredients and dishes that are appropriate for the region and season.

[0127] The generation unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the user is feeling stressed, a menu that is simple and easy to make can be suggested. Also, if the user is relaxed, a menu that can be made over time can be suggested. Furthermore, if the user is in a hurry, a menu that can be made in a short amount of time can be suggested. In this way, it is possible to suggest the optimal menu according to the user's emotions.

[0128] The list creation unit can predict necessary ingredients based on the user's past shopping history and add them to the list. For example, ingredients that the user purchases regularly (such as milk or bread) can be automatically added to the list. It can also add new products or discounted items related to ingredients that the user has previously purchased to the list. It can also suggest ingredients related to specific seasons or events (such as Christmas cakes or decorations during the Christmas season). This allows the user to shop efficiently without missing any ingredients they need.

[0129] The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated emotions. For example, if the user is feeling stressed, important information (allergies and health conditions) can be input with priority. Also, if the user is relaxed, detailed information (likes and dislikes and amount of food eaten) can be input with priority. Furthermore, if the user is in a hurry, minimum information (number of family members and allergies) can be input with priority. In this way, the priority of information to be input can be determined according to the user's emotions.

[0130] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently input in the past as a candidate. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information that will be input during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0131] The input unit can adjust the timing of input based on the user's lifestyle. For example, if the user has a habit of inputting data after breakfast, it can send a notification prompting them to input data at that time. Also, if the user often inputs data at night, it can send a notification prompting them to input data at that time. Furthermore, it can analyze the user's lifestyle and suggest the optimal input timing. This makes it possible to adjust the timing of input based on the user's lifestyle.

[0132] The input unit can monitor changes in the user's health condition in real time and automatically update the input content. For example, if the user's health condition worsens, the input content can be automatically updated and appropriate information can be provided. Also, if the user's health condition improves, the input content can be automatically updated and appropriate information can be provided. Furthermore, the input unit can monitor changes in the user's health condition in real time and automatically update the input content. This makes it possible to monitor changes in the user's health condition in real time and automatically update the input content.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The input unit receives information such as the number of family members, likes and dislikes, allergies, food intake, and health status in advance. For example, the input unit may accept the number of family members using numerical input or voice input. The input unit may also accept likes and dislikes and allergy information in multiple-choice or free-form text format. Furthermore, the input unit may also accept food intake in numerical input or multiple-choice format. Step 2: The reception unit is used when the user asks the AI ​​a question through earphones. For example, the reception unit receives voice input from the earphones using a Bluetooth connection. The reception unit can also analyze the user's question using voice recognition technology. Furthermore, the reception unit can process the voice data according to the voice input protocol. Step 3: The reference unit proposes the optimal menu based on the information received by the input unit in response to the user's question. For example, the reference unit searches for the input information using a database search method. The reference unit can also select the type of information to reference based on the user's question. Step 4: The generation unit generates a specific menu based on the information obtained by the reference unit. For example, the generation unit generates a menu based on nutritional balance and user preferences. The generation unit can also generate a menu based on recipe details and ingredient selection criteria. Step 5: The list creation unit creates a list of necessary ingredients based on the menu created by the creation unit. For example, the list creation unit creates a shopping list based on the types and quantities of ingredients. The list creation unit can also create a shopping list based on the format of the list or the method of selecting the necessary ingredients.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[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 audio according to instructions from the processor 46.

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

[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 carried out in a secure state.

[0146] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] 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.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] 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.

[0180] 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.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0195] 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.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0198] 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.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting prior information; a reception unit that receives a user's question by voice; a reference unit that references the information received by the input unit; A generation unit that generates a menu based on the information obtained by the reference unit; a list creation unit that creates a shopping list based on the menu created by the creation unit; A system characterized by:

2. The input unit includes: Accepts information about the number of family members, likes and dislikes, allergies, food intake, and health conditions 2. The system of claim 1.

3. The reception unit Used when users ask questions to AI through earphones 2. The system of claim 1.

4. The reference portion is Based on the information received by the input unit, the system proposes an appropriate menu in response to the user's question.

2. The system of claim 1.

5. The generation unit Generate a specific menu based on the information obtained in the reference section 2. The system of claim 1.

6. The list creation unit A list of necessary ingredients is made based on the menu generated by the generation unit.

2. The system of claim 1.

7. The input unit includes: Estimate the user's emotions and prioritize the information to be input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit includes: When inputting, the system analyzes the user's past input history and suggests the appropriate input method.

2. The system of claim 1.

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