System

Abstract

An object of a system according to an embodiment is to create a menu according to a request of an individual household on the basis of a voice input and support shopping.SOLUTION: A system includes a voice input part, an analysis part, a menu creation part, a correction part, and a shopping support part. The voice input unit receives a voice input. The analysis unit analyzes the request received by the voice input unit. The menu creation unit creates a menu based on the request analyzed by the analysis unit. The correction part corrects the menu prepared by the menu preparation part. The shopping support part supports shopping on the Internet on the basis of the menu corrected by the correction 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 technology does not adequately support menu planning and shopping according to the needs of individual households, and there is room for improvement.

[0005] The system according to the embodiment aims to create a menu that meets the needs of each individual household based on voice input and to support shopping. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, an analysis unit, a menu creation unit, a correction unit, and a shopping support unit. The voice input unit accepts voice input. The analysis unit analyzes the requests accepted by the voice input unit. The menu creation unit creates a menu based on the requests analyzed by the analysis unit. The correction unit corrects the menu created by the menu creation unit. The shopping support unit supports online shopping based on the menu corrected by the correction unit. [Effects of the Invention]

[0007] The system according to the embodiment can create a menu tailored to the needs of each individual household based on voice input and provide support for shopping. [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 system according to an embodiment of the present invention allows AI to create menus simply by communicating requests via voice. This system analyzes users' requests, including likes and dislikes, allergies, nutritional information, refrigerator contents, cooking utensils, recipe websites, and lunch box preferences, and then creates an optimal menu. Furthermore, the system features an AI menu tuning function that allows users to request unlimited revisions to find the perfect menu. The system not only creates menus but also fully supports online shopping within a single app. This allows the system to eliminate the menu planning fiasco that plagues many housewives (and husbands). For example, users can receive menus tailored to their needs simply by communicating their requests via voice, and the system even supports online shopping, making daily menu planning significantly easier.

[0029] A menu creation system according to an embodiment includes a voice input unit, an analysis unit, a menu creation unit, a correction unit, and a shopping support unit. The voice input unit accepts a user's request. The user's request may include detailed information such as likes and dislikes, allergies, nutritional information, refrigerator contents, cooking utensils, recipe websites, and lunch boxes. For example, the voice input unit may accept a request such as, "My child doesn't like green peppers, so please make a menu without them." The analysis unit analyzes the request accepted by the voice input unit. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm. For example, the analysis unit may understand the user's request and extract information for creating an optimal menu. The menu creation unit creates a menu based on the request analyzed by the analysis unit. The menu is created taking into consideration, for example, the type of dish, the combination of ingredients, and nutritional balance. For example, the menu creation unit may suggest a menu without green peppers based on the ingredients in the refrigerator. The correction unit corrects the menu created by the menu creation unit. The correction may be performed based on, for example, user feedback. For example, the correction unit accepts a request for correction, such as "I'd like the menu to use more vegetables," and creates an optimal menu again. The shopping support unit supports online shopping based on the menu corrected by the correction unit. Shopping support includes, for example, linking with online shopping sites and generating a shopping list. For example, the shopping support unit can purchase ingredients needed for the proposed menu online. As a result, the menu creation system according to the embodiment can create and correct menus based on the user's requests and support online shopping.

[0030] The voice input unit analyzes the user's past voice input history and selects an appropriate voice input method. The voice input unit, for example, prioritizes recognition of phrases that the user has frequently used in the past. The voice input unit can also suggest an input method suitable for a specific time period based on the user's past voice input history. The voice input unit can also adjust the optimal voice input timing based on the user's past voice input history. This makes it possible to provide an optimal voice input method based on the user's past voice input history. The voice input history is analyzed using, for example, a machine learning algorithm. For example, the voice input unit analyzes past voice commands, input frequency, usage status, etc., and selects the optimal voice input method.

[0031] The voice input unit filters voice input based on the user's current living situation and areas of interest. For example, if the user is busy, the voice input unit prioritizes concise voice input. Furthermore, if the user is interested in a specific area of ​​interest, the voice input unit can also prioritize requests related to that area. The voice input unit can also suggest an appropriate voice input method according to the user's living situation. This makes it possible to provide an appropriate voice input method according to the user's living situation and areas of interest. The filtering of living situations and areas of interest is performed based on, for example, the user's schedule, home environment, hobbies, and topics of interest. For example, the voice input unit suggests the optimal timing for voice input taking the user's schedule into consideration.

[0032] The voice input unit selects an appropriate input means according to the user's input method when receiving voice input. For example, if the user prefers voice input, the voice input unit may provide voice input with priority. Furthermore, if the user prefers text input, the voice input unit may provide text input with priority. Furthermore, if the user prefers gesture input, the voice input unit may provide gesture input with priority. This makes it possible to provide the optimal input means according to the user's input method. The input method is selected based on, for example, the user's past input history and usage status. For example, the voice input unit analyzes the user's past input history and selects the optimal input means.

[0033] The voice input unit, when inputting voice, takes into consideration the user's geographical location information and preferentially inputs highly relevant requests. For example, when the user is in a specific area, the voice input unit preferentially accepts requests related to that area. Furthermore, when the user is traveling, the voice input unit can also preferentially accept requests related to the travel destination. Furthermore, when the user is at home, the voice input unit can also preferentially accept requests related to the user's home. This makes it possible to preferentially accept highly relevant requests based on the user's geographical location information. Consideration of geographical location information is performed, for example, using GPS data or location information services. For example, the voice input unit acquires the user's current location and preferentially accepts requests based on information related to the area.

[0034] The voice input unit analyzes the user's social media activity when the voice is input, and inputs related requests. The voice input unit, for example, prioritizes receiving related requests based on information shared by the user on social media. The voice input unit can also analyze the user's social media activity and suggest related requests. The voice input unit can also input related requests with reference to the activity of the user's friends on social media. This makes it possible to prioritize receiving related requests based on the user's social media activity. Analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc. For example, the voice input unit analyzes the user's social media trends and interests, and suggests related requests.

[0035] The voice input unit customizes the input method by reflecting the user's past feedback when inputting voice. The voice input unit suggests an optimal voice input method, for example, based on feedback provided by the user in the past. The voice input unit can also improve the accuracy of voice input by reflecting the user's past feedback. The voice input unit can also adjust the timing of voice input based on the user's past feedback. This makes it possible to provide an optimal voice input method based on the user's past feedback. Feedback is reflected based on, for example, the user's ratings and comments, past usage history, etc. For example, the voice input unit analyzes the user's past feedback and customizes the optimal voice input method.

[0036] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis on important requests. The analysis unit can also perform a concise analysis on requests with low priority. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the request. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the request. The importance of the request is evaluated based on, for example, the user's priority, urgency, and scope of impact. For example, the analysis unit performs a detailed analysis on important requests based on the user's priority.

[0037] During analysis, the analysis unit applies different analysis algorithms depending on the category of the request. For example, the analysis unit applies a nutrition analysis algorithm to a request related to nutrition. The analysis unit can also apply an allergy analysis algorithm to a request related to allergies. The analysis unit can also apply a cookware analysis algorithm to a request related to cookware. In this way, by applying an appropriate analysis algorithm depending on the category of the request, it is possible to provide more accurate analysis results. The analysis algorithm is applied using, for example, a machine learning algorithm or a statistical analysis method. For example, the analysis unit selects and applies an optimal analysis algorithm depending on the category of the request.

[0038] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also reduce analysis errors by using the user's past analysis results. This makes it possible to improve the accuracy of the current analysis based on the user's past analysis results. The past analysis results are referred to based on, for example, past data sets, analysis reports, feedback, etc. For example, the analysis unit analyzes the user's past analysis results and reflects them in the current analysis.

[0039] During analysis, the analysis unit determines the priority of analysis based on the time of submission of the request. For example, the analysis unit prioritizes analysis of requests that have been submitted recently. The analysis unit can also postpone requests that have been submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of the request, more appropriate analysis results can be provided. The evaluation of the submission time is based on, for example, the submission date and time, deadline, urgency, etc. For example, the analysis unit prioritizes analysis of requests that have been submitted recently based on the submission date and time.

[0040] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also postpone less relevant requests. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the requests. In this way, by adjusting the order of analysis based on the relevance of the requests, more appropriate analysis results can be provided. The evaluation of relevance is performed based on, for example, the similarity of content, the scope of influence, the user's interest, etc. For example, the analysis unit prioritizes analysis of highly relevant requests.

[0041] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simpler terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the analysis unit selects the optimal expression method according to the user's level of expertise.

[0042] When creating a menu, the menu creation unit adjusts the level of detail of the menu based on the importance of the ingredients. For example, the menu creation unit provides a detailed menu for important ingredients. The menu creation unit can also provide a concise menu for ingredients with low priority. The menu creation unit can also dynamically adjust the level of detail of the menu according to the importance of the ingredients. In this way, by adjusting the level of detail of the menu according to the importance of the ingredients, it is possible to provide a more appropriate menu. The importance of ingredients is evaluated based on, for example, nutritional value, frequency of use, storage period, etc. For example, the menu creation unit provides a menu that prioritizes the use of ingredients with high nutritional value.

[0043] When creating a menu, the menu creation unit applies different menu algorithms depending on the category of ingredients. For example, the menu creation unit applies a vegetable menu algorithm to a menu related to vegetables. The menu creation unit can also apply a meat menu algorithm to a menu related to meat. The menu creation unit can also apply a fish menu algorithm to a menu related to fish. In this way, by applying an appropriate menu algorithm depending on the category of ingredients, it is possible to provide a more accurate menu. The menu algorithm is applied using, for example, a nutritional balance algorithm or a calorie calculation algorithm. For example, the menu creation unit applies a vegetable menu algorithm to provide a menu that uses a lot of vegetables.

[0044] When creating a menu, the menu creation unit refers to the user's past menu creation results to improve the accuracy of the menu. The menu creation unit, for example, improves the accuracy of the current menu based on the user's past menu creation results. The menu creation unit can also optimize the menu algorithm by referring to the user's past menu creation results. The menu creation unit can also reduce menu errors by using the user's past menu creation results. This makes it possible to improve the accuracy of the current menu based on the user's past menu creation results. The past menu creation results are referred to based on, for example, past recipes, user ratings, feedback, etc. For example, the menu creation unit analyzes the user's past menu creation results and reflects them in the current menu.

[0045] When creating a menu, the menu creation unit determines the priority of the menu based on the time of submission of ingredients. For example, the menu creation unit provides a menu that prioritizes the use of recently purchased ingredients. The menu creation unit can also provide a menu that postpones ingredients purchased recently. The menu creation unit can also dynamically adjust the priority of the menu based on the time of submission. In this way, by determining the priority of the menu based on the time of submission of ingredients, it is possible to provide a more appropriate menu. The evaluation of the time of submission is performed based on, for example, the harvest time, the time of purchase, the expiration date, etc. For example, the menu creation unit provides a menu that prioritizes the use of ingredients that are recently harvested.

[0046] The menu creation unit adjusts the order of the menu based on the relevance of ingredients when creating a menu. For example, the menu creation unit provides a menu that prioritizes the use of highly related ingredients. The menu creation unit can also provide a menu that postpones less related ingredients. The menu creation unit can also dynamically adjust the order of the menu based on the relevance of ingredients. In this way, by adjusting the order of the menu based on the relevance of ingredients, a more appropriate menu can be provided. The evaluation of the relevance is performed based on, for example, the type of dish, nutritional value, cooking method, etc. For example, the menu creation unit provides a menu that prioritizes the use of highly related ingredients.

[0047] When creating a menu, the menu creation unit adjusts the use of technical terms in the menu according to the user's level of expertise. For example, if the user has technical expertise, the menu creation unit provides a menu that makes extensive use of technical terms. Furthermore, if the user does not have technical expertise, the menu creation unit can also provide a menu in simple language. Furthermore, the menu creation unit can dynamically adjust the use of technical terms in the menu according to the user's level of expertise. In this way, by adjusting the use of technical terms in the menu according to the user's level of expertise, a more appropriate menu can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the menu creation unit selects the optimal expression method according to the user's level of expertise.

[0048] The correction unit adjusts the level of detail of the correction based on the importance of the menu when making the correction. For example, the correction unit makes detailed corrections to important menus. The correction unit can also make simple corrections to menus with low priority. The correction unit can also dynamically adjust the level of detail of the correction according to the importance of the menu. In this way, by adjusting the level of detail of the correction according to the importance of the menu, it is possible to provide a more appropriate correction suggestion. The importance of the menu is evaluated based on, for example, nutritional value, frequency of use, shelf life, etc. For example, the correction unit makes detailed corrections to menus with high nutritional value.

[0049] During correction, the correction unit applies different correction algorithms depending on the menu category. For example, the correction unit applies a vegetable correction algorithm to a menu related to vegetables. The correction unit can also apply a meat correction algorithm to a menu related to meat. The correction unit can also apply a fish correction algorithm to a menu related to fish. In this way, by applying an appropriate correction algorithm depending on the menu category, it is possible to provide a more accurate correction suggestion. The correction algorithm is applied using, for example, a nutritional balance algorithm or a calorie calculation algorithm. For example, the correction unit applies a vegetable correction algorithm to correct a menu that uses a lot of vegetables.

[0050] The correction unit improves the accuracy of correction by referring to the user's past correction results when making corrections. For example, the correction unit improves the accuracy of the current correction based on the user's past correction results. The correction unit can also optimize the correction algorithm by referring to the user's past correction results. The correction unit can also reduce correction errors by using the user's past correction results. This makes it possible to improve the accuracy of the current correction based on the user's past correction results. The past correction results are referred to based on, for example, past data sets, analysis reports, feedback, etc. For example, the correction unit analyzes the user's past correction results and reflects them in the current correction.

[0051] When making revisions, the revision unit determines the priority of revisions based on the time of submission of the menu. For example, the revision unit prioritizes revising recently submitted menus. The revision unit can also postpone menus that have been submitted recently. The revision unit can also dynamically adjust the priority of revisions based on the time of submission. In this way, by determining the priority of revisions based on the time of submission of the menu, more appropriate revision suggestions can be provided. The evaluation of the submission time is based on, for example, the harvest time, the purchase time, the expiration date, etc. For example, the revision unit prioritizes revising recently submitted menus.

[0052] The correction unit adjusts the order of corrections based on the relevance of the menu items during correction. For example, the correction unit prioritizes correcting highly relevant menu items. The correction unit can also postpone less relevant menu items. The correction unit can also dynamically adjust the order of corrections based on the relevance of the menu items. In this way, by adjusting the order of corrections based on the relevance of the menu items, more appropriate correction suggestions can be provided. The evaluation of relevance is performed based on, for example, the type of food, nutritional value, cooking method, etc. For example, the correction unit prioritizes correcting highly relevant menu items.

[0053] During correction, the correction unit adjusts the use of technical terms in the correction according to the user's level of expertise. For example, if the user has technical expertise, the correction unit provides a correction suggestion that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the correction unit can also provide a correction suggestion in simpler terms. Furthermore, the correction unit can dynamically adjust the use of technical terms in the correction according to the user's level of expertise. In this way, by adjusting the use of technical terms in the correction according to the user's level of expertise, more appropriate correction suggestions can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the correction unit selects an optimal expression method according to the user's level of expertise.

[0054] During shopping support, the shopping support unit adjusts the level of detail of support based on the importance of the product. For example, the shopping support unit provides detailed support for important products. The shopping support unit can also provide brief support for low-priority products. The shopping support unit can also dynamically adjust the level of detail of support according to the importance of the product. This makes it possible to provide more appropriate shopping support by adjusting the level of detail of support according to the importance of the product. The importance of a product is evaluated based on, for example, nutritional value, frequency of use, shelf life, etc. For example, the shopping support unit provides detailed support for products with high nutritional value.

[0055] During shopping support, the shopping support unit applies different support algorithms depending on the product category. For example, the shopping support unit applies a food support algorithm to shopping for food. The shopping support unit can also apply a daily necessities support algorithm to shopping for daily necessities. The shopping support unit can also apply an electrical appliance support algorithm to shopping for electrical appliances. This allows for more accurate shopping support to be provided by applying an appropriate support algorithm depending on the product category. The support algorithm is applied using, for example, a recommendation algorithm or an inventory management algorithm. For example, the shopping support unit applies a food support algorithm to support food shopping.

[0056] When providing shopping support, the shopping support unit improves the accuracy of the support by referring to the user's past shopping results. For example, the shopping support unit improves the accuracy of the current support based on the user's past shopping results. The shopping support unit can also optimize the support algorithm by referring to the user's past shopping results. The shopping support unit can also reduce support errors by using the user's past shopping results. This makes it possible to improve the accuracy of the current support based on the user's past shopping results. The past shopping results are referred to, for example, based on past purchase history, user ratings, feedback, etc. For example, the shopping support unit analyzes the user's past shopping results and reflects them in the current support.

[0057] During shopping support, the shopping support unit determines support priorities based on the time of product submission. For example, the shopping support unit prioritizes support for recently purchased products. The shopping support unit can also postpone support for older purchased products. The shopping support unit can also dynamically adjust support priorities based on the time of submission. This allows for more appropriate shopping support to be provided by determining support priorities based on the time of product submission. The evaluation of submission time is based on, for example, the harvest time, purchase time, expiration date, etc. For example, the shopping support unit prioritizes support for recently harvested products.

[0058] During shopping support, the shopping support unit adjusts the order of support based on the relevance of products. For example, the shopping support unit prioritizes support for highly relevant products. The shopping support unit can also postpone support for less relevant products. The shopping support unit can also dynamically adjust the order of support based on the relevance of products. This makes it possible to provide more appropriate shopping support by adjusting the order of support based on the relevance of products. The evaluation of relevance is based on, for example, the purpose of use, nutritional value, cooking method, etc. For example, the shopping support unit prioritizes support for highly relevant products.

[0059] During shopping support, the shopping support unit adjusts the use of technical terms in the support according to the user's level of expertise. For example, if the user has technical knowledge, the shopping support unit provides support that makes heavy use of technical terms. Furthermore, if the user does not have technical knowledge, the shopping support unit can also provide support in simple language. Furthermore, the shopping support unit can dynamically adjust the use of technical terms in the support according to the user's level of expertise. In this way, by adjusting the use of technical terms in the support according to the user's level of expertise, more appropriate shopping support can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the shopping support unit selects the optimal expression method according to the user's level of expertise.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze the user's past dietary history and automatically update the user's preferences and allergy information. For example, if the user has avoided a particular ingredient in the past, the analysis unit records that information and reflects it in future menu creation. Also, if the user tries a new ingredient, the results can be analyzed and the user's preference trends can be updated. Furthermore, if the user wants to consume more of a particular nutrient, the analysis unit can automatically recognize that request and reflect it in menu creation. This makes it possible to provide more personalized menus based on the user's dietary history.

[0062] The correction unit can analyze the user's past correction history and automate correction suggestions. For example, if the user has frequently made a specific correction in the past, the correction unit can learn that pattern and automatically suggest similar corrections. Also, if the user wants to add a specific ingredient, the correction unit can predict that request and make suggestions. Furthermore, if the user prefers a specific cooking method, correction suggestions can be provided based on that information. This allows for more efficient correction suggestions based on the user's past correction history.

[0063] The voice input unit can analyze the characteristics of the user's voice and create an individual voice profile for the user. For example, the voice input unit can analyze the tone, pitch, speed, etc. of the user's voice to create an individual profile. The voice input unit can also improve the accuracy of voice recognition based on the user's voice profile. Furthermore, the user's voice profile can be used to identify multiple users and process their requests individually. This can improve the accuracy and efficiency of voice input.

[0064] The menu creation unit can suggest menus according to the season or event based on the user's past meal history. For example, if the user has made a specific dish for Christmas in the past, the menu creation unit can suggest a Christmas menu based on that information. It can also provide a menu using ingredients that the user prefers in the summer. Furthermore, if the user requests a menu tailored to a specific event, it can suggest a menu that reflects that request. This makes it possible to provide personalized menus according to the season or event.

[0065] The shopping support unit can make suggestions to improve shopping efficiency based on the user's past shopping history. For example, if the user has shopped at a specific store in the past, the shopping support unit can provide sale information for that store. The shopping support unit can also suggest related products based on products the user has previously purchased. Furthermore, the shopping support unit can suggest high-quality products based on the user's evaluations of products they have previously purchased. This makes it possible to provide more efficient shopping support based on the user's past shopping history.

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

[0067] Step 1: The voice input unit accepts a user's request. The user's request may include detailed information such as likes and dislikes, allergies, nutrition, the contents of the refrigerator, cooking utensils, recipe sites, lunch boxes, etc. For example, the voice input unit can accept a request such as, "My child doesn't like green peppers, so please make a menu without them." Step 2: The analysis unit analyzes the request received by the voice input unit. The analysis is performed using, for example, natural language processing technology or machine learning algorithms. For example, the analysis unit understands the user's request and extracts information to create an optimal menu. Step 3: The menu creation unit creates a menu based on the requests analyzed by the analysis unit. The menu is created taking into consideration, for example, the type of food, the combination of ingredients, and nutritional balance. For example, the menu creation unit can suggest a menu that does not use green peppers based on the ingredients in the refrigerator. Step 4: The correction unit corrects the menu created by the menu creation unit. The correction is made based on, for example, user feedback. For example, the correction unit receives a request for correction, such as "I would like the menu to use more vegetables," and creates an optimal menu again. Step 5: The shopping support unit supports online shopping based on the menu revised by the revision unit. The shopping support includes, for example, linking with an online shopping site and generating a shopping list. For example, the shopping support unit can purchase ingredients needed for the proposed menu online.

[0068] (Example 2) A system according to an embodiment of the present invention allows AI to create menus simply by communicating requests via voice. This system analyzes users' requests, including likes and dislikes, allergies, nutritional information, refrigerator contents, cooking utensils, recipe websites, and lunch box preferences, and then creates an optimal menu. Furthermore, the system features an AI menu tuning function that allows users to request unlimited revisions to find the perfect menu. The system not only creates menus but also fully supports online shopping within a single app. This allows the system to eliminate the menu planning fiasco that plagues many housewives (and husbands). For example, users can receive menus tailored to their needs simply by communicating their requests via voice, and the system even supports online shopping, making daily menu planning significantly easier.

[0069] A menu creation system according to an embodiment includes a voice input unit, an analysis unit, a menu creation unit, a correction unit, and a shopping support unit. The voice input unit accepts a user's request. The user's request may include detailed information such as likes and dislikes, allergies, nutritional information, refrigerator contents, cooking utensils, recipe websites, and lunch boxes. For example, the voice input unit may accept a request such as, "My child doesn't like green peppers, so please make a menu without them." The analysis unit analyzes the request accepted by the voice input unit. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm. For example, the analysis unit may understand the user's request and extract information for creating an optimal menu. The menu creation unit creates a menu based on the request analyzed by the analysis unit. The menu is created taking into consideration, for example, the type of dish, the combination of ingredients, and nutritional balance. For example, the menu creation unit may suggest a menu without green peppers based on the ingredients in the refrigerator. The correction unit corrects the menu created by the menu creation unit. The correction may be performed based on, for example, user feedback. For example, the correction unit accepts a request for correction, such as "I'd like the menu to use more vegetables," and creates an optimal menu again. The shopping support unit supports online shopping based on the menu corrected by the correction unit. Shopping support includes, for example, linking with online shopping sites and generating a shopping list. For example, the shopping support unit can purchase ingredients needed for the proposed menu online. As a result, the menu creation system according to the embodiment can create and correct menus based on the user's requests and support online shopping.

[0070] The voice input unit estimates the user's emotion and adjusts the timing of the voice input based on the estimated user emotion. For example, if the user is feeling stressed, the voice input unit delays the timing of the voice input to relax the user. Furthermore, if the user is relaxed, the voice input unit can also advance the timing of the voice input to allow the user to proceed smoothly. Furthermore, if the user is in a hurry, the voice input unit can optimize the timing of the voice input to quickly accept the user's request. In this way, by adjusting the timing of the voice input according to the user's emotion, the user's request can be accepted at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0071] The voice input unit analyzes the user's past voice input history and selects an appropriate voice input method. The voice input unit, for example, prioritizes recognition of phrases that the user has frequently used in the past. The voice input unit can also suggest an input method suitable for a specific time period based on the user's past voice input history. The voice input unit can also adjust the optimal voice input timing based on the user's past voice input history. This makes it possible to provide an optimal voice input method based on the user's past voice input history. The voice input history is analyzed using, for example, a machine learning algorithm. For example, the voice input unit analyzes past voice commands, input frequency, usage status, etc., and selects the optimal voice input method.

[0072] The voice input unit filters voice input based on the user's current living situation and areas of interest. For example, if the user is busy, the voice input unit prioritizes concise voice input. Furthermore, if the user is interested in a specific area of ​​interest, the voice input unit can also prioritize requests related to that area. The voice input unit can also suggest an appropriate voice input method according to the user's living situation. This makes it possible to provide an appropriate voice input method according to the user's living situation and areas of interest. The filtering of living situations and areas of interest is performed based on, for example, the user's schedule, home environment, hobbies, and topics of interest. For example, the voice input unit suggests the optimal timing for voice input taking the user's schedule into consideration.

[0073] The voice input unit selects an appropriate input means according to the user's input method when receiving voice input. For example, if the user prefers voice input, the voice input unit may provide voice input with priority. Furthermore, if the user prefers text input, the voice input unit may provide text input with priority. Furthermore, if the user prefers gesture input, the voice input unit may provide gesture input with priority. This makes it possible to provide the optimal input means according to the user's input method. The input method is selected based on, for example, the user's past input history and usage status. For example, the voice input unit analyzes the user's past input history and selects the optimal input means.

[0074] The voice input unit estimates the user's emotions and determines the priority of input requests based on the estimated user emotions. For example, when the user is feeling stressed, the voice input unit prioritizes receiving important requests. Furthermore, when the user is relaxed, the voice input unit can also prioritize receiving detailed requests. Furthermore, when the user is in a hurry, the voice input unit can also prioritize receiving requests that require a quick response. In this way, by determining the priority of requests according to the user's emotions, more appropriate requests can be received. 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.

[0075] The voice input unit, when inputting voice, takes into consideration the user's geographical location information and preferentially inputs highly relevant requests. For example, when the user is in a specific area, the voice input unit preferentially accepts requests related to that area. Furthermore, when the user is traveling, the voice input unit can also preferentially accept requests related to the travel destination. Furthermore, when the user is at home, the voice input unit can also preferentially accept requests related to the user's home. This makes it possible to preferentially accept highly relevant requests based on the user's geographical location information. Consideration of geographical location information is performed, for example, using GPS data or location information services. For example, the voice input unit acquires the user's current location and preferentially accepts requests based on information related to the area.

[0076] The voice input unit analyzes the user's social media activity when the voice is input, and inputs related requests. The voice input unit, for example, prioritizes receiving related requests based on information shared by the user on social media. The voice input unit can also analyze the user's social media activity and suggest related requests. The voice input unit can also input related requests with reference to the activity of the user's friends on social media. This makes it possible to prioritize receiving related requests based on the user's social media activity. Analysis of social media activity is performed based on, for example, the content of posts, the number of likes, the number of followers, etc. For example, the voice input unit analyzes the user's social media trends and interests, and suggests related requests.

[0077] The voice input unit customizes the input method by reflecting the user's past feedback when inputting voice. The voice input unit suggests an optimal voice input method, for example, based on feedback provided by the user in the past. The voice input unit can also improve the accuracy of voice input by reflecting the user's past feedback. The voice input unit can also adjust the timing of voice input based on the user's past feedback. This makes it possible to provide an optimal voice input method based on the user's past feedback. Feedback is reflected based on, for example, the user's ratings and comments, past usage history, etc. For example, the voice input unit analyzes the user's past feedback and customizes the optimal voice input method.

[0078] The analysis unit estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user emotion. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. This allows for adjusting the way the analysis is presented according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] During analysis, the analysis unit adjusts the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis on important requests. The analysis unit can also perform a concise analysis on requests with low priority. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the request. This makes it possible to provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the request. The importance of the request is evaluated based on, for example, the user's priority, urgency, and scope of impact. For example, the analysis unit performs a detailed analysis on important requests based on the user's priority.

[0080] During analysis, the analysis unit applies different analysis algorithms depending on the category of the request. For example, the analysis unit applies a nutrition analysis algorithm to a request related to nutrition. The analysis unit can also apply an allergy analysis algorithm to a request related to allergies. The analysis unit can also apply a cookware analysis algorithm to a request related to cookware. In this way, by applying an appropriate analysis algorithm depending on the category of the request, it is possible to provide more accurate analysis results. The analysis algorithm is applied using, for example, a machine learning algorithm or a statistical analysis method. For example, the analysis unit selects and applies an optimal analysis algorithm depending on the category of the request.

[0081] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also reduce analysis errors by using the user's past analysis results. This makes it possible to improve the accuracy of the current analysis based on the user's past analysis results. The past analysis results are referred to based on, for example, past data sets, analysis reports, feedback, etc. For example, the analysis unit analyzes the user's past analysis results and reflects them in the current analysis.

[0082] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a concise and easy-to-understand analysis result if the user is stressed. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] During analysis, the analysis unit determines the priority of analysis based on the time of submission of the request. For example, the analysis unit prioritizes analysis of requests that have been submitted recently. The analysis unit can also postpone requests that have been submitted earlier. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of the request, more appropriate analysis results can be provided. The evaluation of the submission time is based on, for example, the submission date and time, deadline, urgency, etc. For example, the analysis unit prioritizes analysis of requests that have been submitted recently based on the submission date and time.

[0084] During analysis, the analysis unit adjusts the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also postpone less relevant requests. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the requests. In this way, by adjusting the order of analysis based on the relevance of the requests, more appropriate analysis results can be provided. The evaluation of relevance is performed based on, for example, the similarity of content, the scope of influence, the user's interest, etc. For example, the analysis unit prioritizes analysis of highly relevant requests.

[0085] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simpler terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the analysis unit selects the optimal expression method according to the user's level of expertise.

[0086] The menu creation unit estimates the user's emotions and adjusts the way the menu is presented based on the estimated user emotions. For example, if the user is relaxed, the menu creation unit provides a detailed menu. Furthermore, if the user is in a hurry, the menu creation unit can also provide a concise menu. Furthermore, if the user is feeling stressed, the menu creation unit can also provide a visually easy-to-understand menu. In this way, by adjusting the way the menu is presented according to the user's emotions, a more appropriate menu can be provided. 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 such examples.

[0087] When creating a menu, the menu creation unit adjusts the level of detail of the menu based on the importance of the ingredients. For example, the menu creation unit provides a detailed menu for important ingredients. The menu creation unit can also provide a concise menu for ingredients with low priority. The menu creation unit can also dynamically adjust the level of detail of the menu according to the importance of the ingredients. In this way, by adjusting the level of detail of the menu according to the importance of the ingredients, it is possible to provide a more appropriate menu. The importance of ingredients is evaluated based on, for example, nutritional value, frequency of use, storage period, etc. For example, the menu creation unit provides a menu that prioritizes the use of ingredients with high nutritional value.

[0088] When creating a menu, the menu creation unit applies different menu algorithms depending on the category of ingredients. For example, the menu creation unit applies a vegetable menu algorithm to a menu related to vegetables. The menu creation unit can also apply a meat menu algorithm to a menu related to meat. The menu creation unit can also apply a fish menu algorithm to a menu related to fish. In this way, by applying an appropriate menu algorithm depending on the category of ingredients, it is possible to provide a more accurate menu. The menu algorithm is applied using, for example, a nutritional balance algorithm or a calorie calculation algorithm. For example, the menu creation unit applies a vegetable menu algorithm to provide a menu that uses a lot of vegetables.

[0089] When creating a menu, the menu creation unit refers to the user's past menu creation results to improve the accuracy of the menu. The menu creation unit, for example, improves the accuracy of the current menu based on the user's past menu creation results. The menu creation unit can also optimize the menu algorithm by referring to the user's past menu creation results. The menu creation unit can also reduce menu errors by using the user's past menu creation results. This makes it possible to improve the accuracy of the current menu based on the user's past menu creation results. The past menu creation results are referred to based on, for example, past recipes, user ratings, feedback, etc. For example, the menu creation unit analyzes the user's past menu creation results and reflects them in the current menu.

[0090] The menu creation unit estimates the user's emotions and adjusts the length of the menu based on the estimated user emotions. For example, if the user is in a hurry, the menu creation unit provides a short menu. Furthermore, if the user is relaxed, the menu creation unit can also provide a detailed menu. Furthermore, if the user is feeling stressed, the menu creation unit can also provide a concise and easy-to-understand menu. In this way, by adjusting the length of the menu according to the user's emotions, a more appropriate menu can be provided. 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 such examples.

[0091] When creating a menu, the menu creation unit determines the priority of the menu based on the time of submission of ingredients. For example, the menu creation unit provides a menu that prioritizes the use of recently purchased ingredients. The menu creation unit can also provide a menu that postpones ingredients purchased recently. The menu creation unit can also dynamically adjust the priority of the menu based on the time of submission. In this way, by determining the priority of the menu based on the time of submission of ingredients, it is possible to provide a more appropriate menu. The evaluation of the time of submission is performed based on, for example, the harvest time, the time of purchase, the expiration date, etc. For example, the menu creation unit provides a menu that prioritizes the use of ingredients that are recently harvested.

[0092] The menu creation unit adjusts the order of the menu based on the relevance of ingredients when creating a menu. For example, the menu creation unit provides a menu that prioritizes the use of highly related ingredients. The menu creation unit can also provide a menu that postpones less related ingredients. The menu creation unit can also dynamically adjust the order of the menu based on the relevance of ingredients. In this way, by adjusting the order of the menu based on the relevance of ingredients, a more appropriate menu can be provided. The evaluation of the relevance is performed based on, for example, the type of dish, nutritional value, cooking method, etc. For example, the menu creation unit provides a menu that prioritizes the use of highly related ingredients.

[0093] When creating a menu, the menu creation unit adjusts the use of technical terms in the menu according to the user's level of expertise. For example, if the user has technical expertise, the menu creation unit provides a menu that makes extensive use of technical terms. Furthermore, if the user does not have technical expertise, the menu creation unit can also provide a menu in simple language. Furthermore, the menu creation unit can dynamically adjust the use of technical terms in the menu according to the user's level of expertise. In this way, by adjusting the use of technical terms in the menu according to the user's level of expertise, a more appropriate menu can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the menu creation unit selects the optimal expression method according to the user's level of expertise.

[0094] The correction unit estimates the user's emotion and adjusts the way in which the correction is expressed based on the estimated user's emotion. For example, if the user is relaxed, the correction unit provides detailed correction suggestions. If the user is in a hurry, the correction unit can also provide concise correction suggestions. If the user is stressed, the correction unit can also provide visually easy-to-understand correction suggestions. This allows for adjusting the way in which the correction is expressed according to the user's emotion, thereby providing more appropriate correction suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] The correction unit adjusts the level of detail of the correction based on the importance of the menu when making the correction. For example, the correction unit makes detailed corrections to important menus. The correction unit can also make simple corrections to menus with low priority. The correction unit can also dynamically adjust the level of detail of the correction according to the importance of the menu. In this way, by adjusting the level of detail of the correction according to the importance of the menu, it is possible to provide a more appropriate correction suggestion. The importance of the menu is evaluated based on, for example, nutritional value, frequency of use, shelf life, etc. For example, the correction unit makes detailed corrections to menus with high nutritional value.

[0096] During correction, the correction unit applies different correction algorithms depending on the menu category. For example, the correction unit applies a vegetable correction algorithm to a menu related to vegetables. The correction unit can also apply a meat correction algorithm to a menu related to meat. The correction unit can also apply a fish correction algorithm to a menu related to fish. In this way, by applying an appropriate correction algorithm depending on the menu category, it is possible to provide a more accurate correction suggestion. The correction algorithm is applied using, for example, a nutritional balance algorithm or a calorie calculation algorithm. For example, the correction unit applies a vegetable correction algorithm to correct a menu that uses a lot of vegetables.

[0097] The correction unit improves the accuracy of correction by referring to the user's past correction results when making corrections. For example, the correction unit improves the accuracy of the current correction based on the user's past correction results. The correction unit can also optimize the correction algorithm by referring to the user's past correction results. The correction unit can also reduce correction errors by using the user's past correction results. This makes it possible to improve the accuracy of the current correction based on the user's past correction results. The past correction results are referred to based on, for example, past data sets, analysis reports, feedback, etc. For example, the correction unit analyzes the user's past correction results and reflects them in the current correction.

[0098] The correction unit estimates the user's emotion and adjusts the length of the correction based on the estimated user emotion. For example, if the user is in a hurry, the correction unit provides a short correction suggestion. Furthermore, if the user is relaxed, the correction unit can provide a detailed correction suggestion. Furthermore, if the user is stressed, the correction unit can provide a concise and easy-to-understand correction suggestion. In this way, by adjusting the length of the correction according to the user's emotion, more appropriate correction suggestions can be provided. The emotion estimation is realized using an emotion estimation function, for example, using 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] When making revisions, the revision unit determines the priority of revisions based on the time of submission of the menu. For example, the revision unit prioritizes revising recently submitted menus. The revision unit can also postpone menus that have been submitted recently. The revision unit can also dynamically adjust the priority of revisions based on the time of submission. In this way, by determining the priority of revisions based on the time of submission of the menu, more appropriate revision suggestions can be provided. The evaluation of the submission time is based on, for example, the harvest time, the purchase time, the expiration date, etc. For example, the revision unit prioritizes revising recently submitted menus.

[0100] The correction unit adjusts the order of corrections based on the relevance of the menu items during correction. For example, the correction unit prioritizes correcting highly relevant menu items. The correction unit can also postpone less relevant menu items. The correction unit can also dynamically adjust the order of corrections based on the relevance of the menu items. In this way, by adjusting the order of corrections based on the relevance of the menu items, more appropriate correction suggestions can be provided. The evaluation of relevance is performed based on, for example, the type of food, nutritional value, cooking method, etc. For example, the correction unit prioritizes correcting highly relevant menu items.

[0101] During correction, the correction unit adjusts the use of technical terms in the correction according to the user's level of expertise. For example, if the user has technical expertise, the correction unit provides a correction suggestion that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the correction unit can also provide a correction suggestion in simpler terms. Furthermore, the correction unit can dynamically adjust the use of technical terms in the correction according to the user's level of expertise. In this way, by adjusting the use of technical terms in the correction according to the user's level of expertise, more appropriate correction suggestions can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the correction unit selects an optimal expression method according to the user's level of expertise.

[0102] The shopping support unit estimates the user's emotions and adjusts the way shopping support is presented based on the estimated user emotions. For example, the shopping support unit provides detailed shopping support when the user is relaxed. The shopping support unit can also provide concise shopping support when the user is in a hurry. The shopping support unit can also provide visually easy-to-understand shopping support when the user is stressed. This allows for more appropriate shopping support to be provided by adjusting the way shopping support is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] During shopping support, the shopping support unit adjusts the level of detail of support based on the importance of the product. For example, the shopping support unit provides detailed support for important products. The shopping support unit can also provide brief support for low-priority products. The shopping support unit can also dynamically adjust the level of detail of support according to the importance of the product. This makes it possible to provide more appropriate shopping support by adjusting the level of detail of support according to the importance of the product. The importance of a product is evaluated based on, for example, nutritional value, frequency of use, shelf life, etc. For example, the shopping support unit provides detailed support for products with high nutritional value.

[0104] During shopping support, the shopping support unit applies different support algorithms depending on the product category. For example, the shopping support unit applies a food support algorithm to shopping for food. The shopping support unit can also apply a daily necessities support algorithm to shopping for daily necessities. The shopping support unit can also apply an electrical appliance support algorithm to shopping for electrical appliances. This allows for more accurate shopping support to be provided by applying an appropriate support algorithm depending on the product category. The support algorithm is applied using, for example, a recommendation algorithm or an inventory management algorithm. For example, the shopping support unit applies a food support algorithm to support food shopping.

[0105] When providing shopping support, the shopping support unit improves the accuracy of the support by referring to the user's past shopping results. For example, the shopping support unit improves the accuracy of the current support based on the user's past shopping results. The shopping support unit can also optimize the support algorithm by referring to the user's past shopping results. The shopping support unit can also reduce support errors by using the user's past shopping results. This makes it possible to improve the accuracy of the current support based on the user's past shopping results. The past shopping results are referred to, for example, based on past purchase history, user ratings, feedback, etc. For example, the shopping support unit analyzes the user's past shopping results and reflects them in the current support.

[0106] The shopping support unit estimates the user's emotions and adjusts the length of the shopping support based on the estimated user emotions. For example, the shopping support unit provides short shopping support when the user is in a hurry. The shopping support unit can also provide detailed shopping support when the user is relaxed. The shopping support unit can also provide concise and easy-to-understand shopping support when the user is stressed. This allows for adjusting the length of shopping support according to the user's emotions, thereby providing more appropriate shopping support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] During shopping support, the shopping support unit determines support priorities based on the time of product submission. For example, the shopping support unit prioritizes support for recently purchased products. The shopping support unit can also postpone support for older purchased products. The shopping support unit can also dynamically adjust support priorities based on the time of submission. This allows for more appropriate shopping support to be provided by determining support priorities based on the time of product submission. The evaluation of submission time is based on, for example, the harvest time, purchase time, expiration date, etc. For example, the shopping support unit prioritizes support for recently harvested products.

[0108] During shopping support, the shopping support unit adjusts the order of support based on the relevance of products. For example, the shopping support unit prioritizes support for highly relevant products. The shopping support unit can also postpone support for less relevant products. The shopping support unit can also dynamically adjust the order of support based on the relevance of products. This makes it possible to provide more appropriate shopping support by adjusting the order of support based on the relevance of products. The evaluation of relevance is based on, for example, the purpose of use, nutritional value, cooking method, etc. For example, the shopping support unit prioritizes support for highly relevant products.

[0109] During shopping support, the shopping support unit adjusts the use of technical terms in the support according to the user's level of expertise. For example, if the user has technical knowledge, the shopping support unit provides support that makes heavy use of technical terms. Furthermore, if the user does not have technical knowledge, the shopping support unit can also provide support in simple language. Furthermore, the shopping support unit can dynamically adjust the use of technical terms in the support according to the user's level of expertise. In this way, by adjusting the use of technical terms in the support according to the user's level of expertise, more appropriate shopping support can be provided. The level of expertise is evaluated based on, for example, occupation, educational background, past experience, etc. For example, the shopping support unit selects the optimal expression method according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the voice input unit, analysis unit, menu creation unit, correction unit, and shopping support unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice input unit can receive a user's request using the microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request received from the voice input unit. The menu creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a menu based on the analyzed request. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the menu based on user feedback. The shopping support unit is realized by the control unit 46A of the smart device 14 and supports online shopping based on the corrected menu. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned voice input unit, analysis unit, menu creation unit, correction unit, and shopping support unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice input unit can receive a user's request using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request received from the voice input unit. The menu creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a menu based on the analyzed request. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the menu based on user feedback. The shopping support unit is realized by the control unit 46A of the smart glasses 214 and supports online shopping based on the corrected menu. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice input unit, analysis unit, menu creation unit, correction unit, and shopping support unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the voice input unit can receive a user's request using the microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request received from the voice input unit. The menu creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a menu based on the analyzed request. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the menu based on user feedback. The shopping support unit is realized by the control unit 46A of the headset terminal 314 and supports online shopping based on the corrected menu. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice input unit, analysis unit, menu creation unit, correction unit, and shopping support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice input unit can receive a user's request using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request received from the voice input unit. The menu creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a menu based on the analyzed request. The correction unit is realized by the specific processing unit 290 of the data processing device 12 and corrects the menu based on user feedback. The shopping support unit is realized by the control unit 46A of the robot 414 and supports online shopping based on the corrected menu.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The voice input unit can analyze the tone and speed of the user's voice to estimate the user's emotions. For example, if the user is excited, the voice input unit detects the tone and prioritizes requests that require a quick response. If the user is calm, the voice input unit can accept detailed requests. Furthermore, if the user is tired, the voice input unit can prioritize brief requests. This makes it possible to optimize voice input processing according to the user's emotions.

[0112] The analysis unit can analyze the user's past dietary history and automatically update the user's preferences and allergy information. For example, if the user has avoided a particular ingredient in the past, the analysis unit records that information and reflects it in future menu creation. Also, if the user tries a new ingredient, the results can be analyzed and the user's preference trends can be updated. Furthermore, if the user wants to consume more of a particular nutrient, the analysis unit can automatically recognize that request and reflect it in menu creation. This makes it possible to provide more personalized menus based on the user's dietary history.

[0113] The menu creation unit can estimate the user's emotions and provide menu variations based on the estimated emotions. For example, if the user is feeling stressed, the menu creation unit can suggest a menu using ingredients that have a relaxing effect. If the user is feeling energetic, the menu creation unit can suggest a high-calorie menu to replenish energy. Furthermore, if the user is tired, the menu creation unit can suggest a menu that is easy to prepare. In this way, the optimal menu can be provided according to the user's emotions.

[0114] The correction unit can analyze the user's past correction history and automate correction suggestions. For example, if the user has frequently made a specific correction in the past, the correction unit can learn that pattern and automatically suggest similar corrections. Also, if the user wants to add a specific ingredient, the correction unit can predict that request and make suggestions. Furthermore, if the user prefers a specific cooking method, correction suggestions can be provided based on that information. This allows for more efficient correction suggestions based on the user's past correction history.

[0115] The shopping support unit can estimate the user's emotions and adjust the priorities of the shopping list based on the estimated emotions. For example, if the user is in a hurry, the shopping support unit can prioritize the list of necessary ingredients. If the user is relaxed, the shopping support unit can provide a detailed shopping list. If the user is stressed, the shopping support unit can provide a concise and easy-to-understand shopping list. This makes it possible to provide an optimal shopping list according to the user's emotions.

[0116] The voice input unit can analyze the characteristics of the user's voice and create an individual voice profile for the user. For example, the voice input unit can analyze the tone, pitch, speed, etc. of the user's voice to create an individual profile. The voice input unit can also improve the accuracy of voice recognition based on the user's voice profile. Furthermore, the user's voice profile can be used to identify multiple users and process their requests individually. This can improve the accuracy and efficiency of voice input.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. Furthermore, if the user is stressed, analysis results that are visually easy to understand can be provided. This makes it possible to provide optimal analysis results according to the user's emotions.

[0118] The menu creation unit can suggest menus according to the season or event based on the user's past meal history. For example, if the user has made a specific dish for Christmas in the past, the menu creation unit can suggest a Christmas menu based on that information. It can also provide a menu using ingredients that the user prefers in the summer. Furthermore, if the user requests a menu tailored to a specific event, it can suggest a menu that reflects that request. This makes it possible to provide personalized menus according to the season or event.

[0119] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is feeling stressed, important corrections can be made with priority. If the user is relaxed, detailed corrections can be made. Furthermore, if the user is in a hurry, corrections that require a quick response can be made with priority. This makes it possible to provide optimal corrections according to the user's emotions.

[0120] The shopping support unit can make suggestions to improve shopping efficiency based on the user's past shopping history. For example, if the user has shopped at a specific store in the past, the shopping support unit can provide sale information for that store. The shopping support unit can also suggest related products based on products the user has previously purchased. Furthermore, the shopping support unit can suggest high-quality products based on the user's evaluations of products they have previously purchased. This makes it possible to provide more efficient shopping support based on the user's past shopping history.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The voice input unit accepts a user's request. The user's request may include detailed information such as likes and dislikes, allergies, nutrition, the contents of the refrigerator, cooking utensils, recipe sites, lunch boxes, etc. For example, the voice input unit can accept a request such as, "My child doesn't like green peppers, so please make a menu without them." Step 2: The analysis unit analyzes the request received by the voice input unit. The analysis is performed using, for example, natural language processing technology or machine learning algorithms. For example, the analysis unit understands the user's request and extracts information to create an optimal menu. Step 3: The menu creation unit creates a menu based on the requests analyzed by the analysis unit. The menu is created taking into consideration, for example, the type of food, the combination of ingredients, and nutritional balance. For example, the menu creation unit can suggest a menu that does not use green peppers based on the ingredients in the refrigerator. Step 4: The correction unit corrects the menu created by the menu creation unit. The correction is made based on, for example, user feedback. For example, the correction unit receives a request for correction, such as "I would like the menu to use more vegetables," and creates an optimal menu again. Step 5: The shopping support unit supports online shopping based on the menu revised by the revision unit. The shopping support includes, for example, linking with an online shopping site and generating a shopping list. For example, the shopping support unit can purchase ingredients needed for the proposed menu online.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 audio input unit; an analysis unit that analyzes the request received by the voice input unit; A menu creation unit that creates a menu based on the requests analyzed by the analysis unit; A correction unit that corrects the menu created by the menu creation unit; a shopping support unit that supports online shopping based on the menu corrected by the correction unit; Equipped with A system characterized by:

2. The voice input unit Estimate the user's emotions and adjust the timing of voice input based on the estimated user emotions.

2. The system of claim 1.

3. The voice input unit Analyze the user's past voice input history and select the appropriate voice input method 2. The system of claim 1.

4. The voice input unit When you speak, it filters based on the user's current life situation and interests.

2. The system of claim 1.

5. The voice input unit When inputting voice, select the appropriate input method according to the user's input method.

2. The system of claim 1.

6. The voice input unit Estimate the user's emotions and prioritize the requests to be input based on the estimated user emotions.

2. The system of claim 1.

7. The voice input unit Prioritize relevant requests based on your geographic location when you speak 2. The system of claim 1.

8. The voice input unit Analyzes your social media activity and inputs relevant requests when you speak 2. The system of claim 1.

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