System

Abstract

An object of a system according to an embodiment is to recommend an optimal order in consideration of a user's personal attributes and past feedback.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects personal attributes of a user. The collection unit collects past feedback. The collection unit collects menu information of a store. The analysis unit analyzes the information collected by the collection unit and recommends an order. The providing unit provides the recommendation result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend optimal orders that take into account the user's personal attributes and past feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal orders by taking into consideration the user's personal attributes and past feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects personal attributes of users. The collection unit collects past feedback. The collection unit collects restaurant menu information. The analysis unit analyzes the information collected by the collection unit and recommends orders. The provision unit provides the recommendation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend optimal orders by taking into consideration the user's personal attributes and past feedback. [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) An AI system according to an embodiment of the present invention recommends optimal health-conscious orders based on a user's personal attributes, past feedback, and restaurant menu information. This AI system provides benefits to both users and restaurants. For example, a user inputs their food preferences and allergy information. The AI ​​system then accumulates the user's past orders and feedback. Furthermore, restaurant menu information is also input into the AI ​​system. Based on this information, the AI ​​system recommends optimal orders for the user. For example, if a user "likes spicy food" but "has a nut allergy," the AI ​​system recommends "spicy but nut-free dishes." Based on past feedback, the system also prioritizes recommendations based on the user's preferences. Furthermore, by sharing information with restaurants, this system can improve their marketing. For example, restaurants can develop new menu items and promote their products based on information such as "user preferences" and "popular dishes." This allows the AI ​​system to recommend optimal health-conscious orders based on the user's personal attributes, past feedback, and restaurant menu information. For example, if a user "likes spicy food" but "has a nut allergy," the AI ​​system will recommend "spicy but nut-free dishes." It also prioritizes recommendations based on past feedback based on the user's preferences. Furthermore, by sharing information with restaurants, this system can also help improve their marketing. For example, restaurants can develop new menu items and promote their products based on information such as "which dishes users like" and "which dishes are popular."

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects personal attributes of a user. Examples of personal attributes include, but are not limited to, age, gender, hobbies, food preferences, and allergy information. For example, the collection unit allows a user to input information such as "I like spicy food" or "I have a nut allergy." The collection unit also collects past feedback. Examples of past feedback include, but are not limited to, past order history and evaluation comments. For example, the collection unit can provide feedback such as "This dish was delicious" or "This dish was too spicy." The collection unit also collects restaurant menu information. Examples of restaurant menu information include, but are not limited to, dish names, prices, and calorie information. For example, the collection unit can provide information such as "A new menu item has been added" or "This dish is spicy." The analysis unit analyzes the information collected by the collection unit and recommends optimal orders. For example, the analysis is performed taking into account calories and nutritional balance, but is not limited to these examples. The analysis unit can perform analysis based on, for example, calories and nutritional balance. The analysis unit can also prioritize recommendations of dishes preferred by the user based on the user's personal attributes and past feedback. The provision unit provides the recommendation results obtained by the analysis unit. The provision unit can, for example, provide the recommendation results to the user. The provision unit can also provide the recommendation results to restaurants, which can be used to improve marketing. This allows the AI ​​system according to the embodiment to recommend optimal orders based on the user's personal attributes, past feedback, and restaurant menu information. For example, if a user "likes spicy food" but "has a nut allergy," the AI ​​system recommends "spicy but nut-free dishes." Based on past feedback, the AI ​​system also prioritizes recommendations of dishes preferred by the user. Furthermore, by sharing information with restaurants, this system can also improve the restaurant's marketing.For example, restaurants can develop new menu items and run promotions based on information such as "which dishes users like" and "which dishes are popular."

[0030] The analysis unit can perform analysis based on calories and nutritional balance. The analysis unit performs analysis based on calorie information, for example. For example, the analysis unit collects calorie information about dishes and recommends optimal dishes based on the user's calorie limit. The analysis unit can also perform analysis taking nutritional balance into consideration. For example, the analysis unit collects nutrient information about dishes and recommends optimal dishes based on the user's nutritional balance. The analysis unit can also perform analysis based on the user's health condition. For example, the analysis unit recommends healthy dishes based on the user's health checkup results. This makes it possible to perform analysis taking calories and nutritional balance into consideration.

[0031] The providing unit can provide the recommendation results to the user. For example, the providing unit notifies the user of the recommendation results to their smartphone. For example, the providing unit provides the recommendation results to the user by push notification. The providing unit can also send the recommendation results to the user's email address. For example, the providing unit provides the recommendation results to the user by email. The providing unit can also display the recommendation results in the user's dedicated app. For example, the providing unit displays the recommendation results on the screen of the dedicated app. This makes it possible to recommend the optimal order to the user.

[0032] The providing unit provides the recommendation results to the store, which can be used to improve marketing. The providing unit, for example, notifies the store's management system of the recommendation results. For example, the providing unit automatically sends the recommendation results to the store's management system. The providing unit can also send the recommendation results to the store's email address. For example, the providing unit provides the recommendation results to the store by email. The providing unit can also display the recommendation results on the store's dedicated app. For example, the providing unit displays the recommendation results on the screen of the dedicated app. This makes it possible to provide information that is useful for improving the store's marketing.

[0033] The collection unit can collect information about the user's dietary preferences and allergies. For example, the user can input information such as "I like spicy food" or "I have a nut allergy" into the collection unit. For example, the user inputs the dietary preference and allergy information into the collection unit through a dedicated app. The collection unit can also automatically collect the dietary preference and allergy information provided by the user in the past. For example, the collection unit extracts the dietary preference and allergy information from the user's past order history. The collection unit can also collect the dietary preference and allergy information by analyzing the user's social media activity. For example, the collection unit analyzes photos of food shared by the user on social media and collects preference trends. This makes it possible to collect the user's dietary preference and allergy information.

[0034] The collection unit can collect past feedback from users. For example, the collection unit can allow users to provide feedback such as "This dish was delicious" or "This dish was too spicy." For example, the collection unit allows users to input feedback through a dedicated app. The collection unit can also automatically collect feedback provided by users in the past. For example, the collection unit extracts feedback from the user's past order history. The collection unit can also collect feedback by analyzing the user's social media activity. For example, the collection unit analyzes photos of meals shared by users on social media and collects feedback. In this way, the user's past feedback can be collected.

[0035] The collection unit can analyze the user's past meal history and select the optimal collection method. The collection unit selects the collection method by analyzing preference trends based on, for example, dishes that the user has frequently ordered in the past. For example, the collection unit analyzes dishes that the user has frequently ordered in the past and asks detailed questions. The collection unit can also determine whether to ask detailed questions or simplify the questions based on feedback provided by the user in the past. For example, the collection unit analyzes feedback provided by the user in the past and adjusts the content of the questions. The collection unit can also adjust the information to be collected during a specific time period based on the user's past meal history. For example, the collection unit analyzes dishes that the user ordered during a specific time period and adjusts the information to be collected during that time period. In this way, the optimal collection method can be selected based on the user's past meal history.

[0036] The collection unit can filter the personal attributes based on the user's current health condition and lifestyle habits when collecting the personal attributes. For example, when the user inputs the results of a health checkup, the collection unit filters the personal attributes to be collected based on that information. For example, when the user inputs the results of a health checkup, the collection unit prioritizes collecting information about specific nutrients based on that information. Furthermore, when the user is on a specific diet, the collection unit can adjust the personal attributes to be collected based on that information. For example, when the user is on a specific diet, the collection unit prioritizes collecting information about calories and nutritional balance based on that information. Furthermore, when the user has an exercise habit, the collection unit can filter the personal attributes to be collected based on that information. For example, when the user has an exercise habit, the collection unit prioritizes collecting information about energy consumption based on that information. This makes it possible to filter the personal attributes based on the user's health condition and lifestyle habits.

[0037] When collecting personal attributes, the collection unit can select a collection means according to the user's input method. For example, if the user selects voice input, the collection unit collects personal attributes using voice recognition technology. For example, if the user selects voice input, the collection unit collects the user's dietary preferences and allergy information using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect personal attributes using text analysis technology. For example, if the user selects text input, the collection unit collects the user's dietary preferences and allergy information using text analysis technology. Furthermore, if the user selects image input, the collection unit can also collect personal attributes using image analysis technology. For example, if the user selects image input, the collection unit collects the user's dietary preferences and allergy information using image analysis technology. This makes it possible to select the optimal collection means according to the user's input method.

[0038] When collecting personal attributes, the collection unit can prioritize collecting highly relevant attributes in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting personal attributes related to the food culture of the area. For example, when the user is in a specific area, the collection unit collects information related to the food culture of the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting personal attributes related to meals at the travel destination. For example, when the user is traveling, the collection unit collects information related to meals at the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting personal attributes related to daily meals. For example, when the user is at home, the collection unit collects information related to daily meals. In this way, it is possible to prioritize collecting highly relevant attributes in consideration of the user's geographical location information.

[0039] When collecting personal attributes, the collection unit can analyze the user's social media activities and collect related attributes. For example, the collection unit analyzes photos of food shared by the user on social media and collects preference trends. For example, the collection unit analyzes photos of food shared by the user on social media and collects preference trends. The collection unit can also collect information about dietary preferences and allergies based on the content of the user's posts on social media. For example, the collection unit analyzes the content of the user's posts on social media and collects information about dietary preferences and allergies. The collection unit can also collect related personal attributes by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends on social media and collects related personal attributes. In this way, it is possible to analyze the user's social media activities and collect related attributes.

[0040] When collecting personal attributes, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit determines whether to ask detailed questions or simplify questions based on feedback provided by the user in the past. For example, the collection unit analyzes feedback provided by the user in the past and determines whether to ask detailed questions or simplify questions. The collection unit can also preferentially collect specific information from the user's past feedback. For example, the collection unit preferentially collects specific information based on the user's past feedback. The collection unit can also customize the collection method by referring to the user's past feedback. For example, the collection unit customizes the collection method based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on calories and nutritional balance. For example, if a user is on a calorie restriction, the analysis unit analyzes calorie information in detail. For example, if a user is on a calorie restriction, the analysis unit analyzes calorie information in detail. Furthermore, if a user places importance on nutritional balance, the analysis unit can also perform a detailed analysis of nutrients. For example, if a user places importance on nutritional balance, the analysis unit can perform a detailed analysis of nutrients. Furthermore, if a user wants to ingest a specific nutrient, the analysis unit can prioritize analyzing information about that nutrient. For example, if a user wants to ingest a specific nutrient, the analysis unit prioritizes analyzing information about that nutrient. This makes it possible to adjust the level of detail of the analysis based on calories and nutritional balance.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of cuisine. For example, in the case of Japanese cuisine, the analysis unit applies an analysis algorithm specialized for Japanese cuisine. For example, the analysis unit applies an analysis algorithm specialized for Japanese cuisine. Furthermore, in the case of Western cuisine, the analysis unit can also apply an analysis algorithm specialized for Western cuisine. For example, the analysis unit applies an analysis algorithm specialized for Western cuisine. Furthermore, in the case of Chinese cuisine, the analysis unit can also apply an analysis algorithm specialized for Chinese cuisine. For example, the analysis unit applies an analysis algorithm specialized for Chinese cuisine. This makes it possible to apply different analysis algorithms depending on the category of cuisine.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the user in the past. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also improve the accuracy of the analysis by extracting a specific pattern from the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by extracting a specific pattern from the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the food is served. For example, in the case of breakfast, the analysis unit prioritizes analyzing information related to breakfast. For example, the analysis unit prioritizes analyzing information related to breakfast. Furthermore, in the case of lunch, the analysis unit can also prioritize analyzing information related to lunch. For example, the analysis unit prioritizes analyzing information related to lunch. Furthermore, in the case of dinner, the analysis unit can also prioritize analyzing information related to dinner. For example, the analysis unit prioritizes analyzing information related to dinner. In this way, the priority of analysis can be determined based on the time when the food is served.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of dishes. For example, in the case of a main dish, the analysis unit prioritizes analyzing information related to the main dish. For example, the analysis unit prioritizes analyzing information related to the main dish. Furthermore, in the case of a side dish, the analysis unit can also prioritize analyzing information related to the side dish. For example, the analysis unit prioritizes analyzing information related to the side dish. Furthermore, in the case of a dessert, the analysis unit can also prioritize analyzing information related to the dessert. For example, the analysis unit prioritizes analyzing information related to the dessert. This makes it possible to adjust the order of analysis based on the relevance of dishes.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides the analysis result using detailed technical terminology. For example, if the user has technical expertise, the analysis unit provides the analysis result using detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis result using concise and easy-to-understand language. For example, if the user does not have technical expertise, the analysis unit provides the analysis result using concise and easy-to-understand language. Furthermore, the analysis unit can adjust the way in which the analysis result is expressed according to the user's level of expertise. For example, the analysis unit adjusts the way in which the analysis result is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise.

[0047] When providing a recommendation result, the providing unit can select an optimal display method by referring to the user's past feedback. The providing unit, for example, provides a detailed recommendation result based on feedback provided by the user in the past. For example, the providing unit provides a detailed recommendation result based on feedback provided by the user in the past. The providing unit can also preferentially display specific information from the user's past feedback. For example, the providing unit preferentially displays specific information based on the user's past feedback. The providing unit can also customize the display method by referring to the user's past feedback. For example, the providing unit customizes the display method based on the user's past feedback. This makes it possible to select an optimal display method by referring to the user's past feedback.

[0048] When providing a recommendation result, the providing unit can customize the display content according to the user's current health condition. For example, if the user inputs the results of a health checkup, the providing unit customizes the recommendation result based on that information. For example, if the user inputs the results of a health checkup, the providing unit customizes the recommendation result based on that information. In addition, if the user is on a specific diet, the providing unit can adjust the recommendation result based on that information. For example, if the user is on a specific diet, the providing unit adjusts the recommendation result based on that information. In addition, if the user has an exercise habit, the providing unit can customize the recommendation result based on that information. For example, if the user has an exercise habit, the providing unit customizes the recommendation result based on that information. In this way, the recommendation result can be customized according to the user's current health condition.

[0049] The providing unit can improve the display method by reflecting user feedback when providing recommendation results. The providing unit, for example, adjusts the display method based on feedback previously provided by the user. For example, the providing unit adjusts the display method based on feedback previously provided by the user. The providing unit can also preferentially display specific information based on user feedback. For example, the providing unit preferentially displays specific information based on user feedback. The providing unit can also customize the display method by referring to user feedback. For example, the providing unit customizes the display method based on user feedback. This makes it possible to improve the display method by reflecting user feedback.

[0050] When providing recommendation results, the providing unit can select an optimal display method taking into consideration the user's geographical location information. For example, when the user is in a specific region, the providing unit preferentially displays recommendation results related to the food culture of that region. For example, when the user is in a specific region, the providing unit preferentially displays recommendation results related to the food culture of that region. Furthermore, when the user is traveling, the providing unit can also preferentially display recommendation results related to meals at the travel destination. For example, when the user is traveling, the providing unit preferentially displays recommendation results related to meals at the travel destination. Furthermore, when the user is at home, the providing unit can also preferentially display recommendation results related to everyday meals. For example, when the user is at home, the providing unit preferentially displays recommendation results related to everyday meals. This makes it possible to select an optimal display method taking into consideration the user's geographical location information.

[0051] When providing recommendation results, the providing unit can analyze the user's social media activity and suggest display content. The providing unit, for example, analyzes photos of meals shared by the user on social media and provides recommendation results that reflect the user's preference trends. For example, the providing unit analyzes photos of meals shared by the user on social media and provides recommendation results that reflect the user's preference trends. The providing unit can also provide related recommendation results based on the content posted by the user on social media. For example, the providing unit provides related recommendation results based on the content posted by the user on social media. The providing unit can also provide related recommendation results by referring to the activities of the user's friends on social media. For example, the providing unit provides related recommendation results by referring to the activities of the user's friends on social media. In this way, display content can be suggested by analyzing the user's social media activity.

[0052] When providing a recommendation result, the providing unit can customize the display method by reflecting the user's past feedback. The providing unit, for example, adjusts the display method based on feedback provided by the user in the past. For example, the providing unit adjusts the display method based on feedback provided by the user in the past. The providing unit can also preferentially display specific information based on the user's past feedback. For example, the providing unit preferentially display specific information based on the user's past feedback. The providing unit can also customize the display method by referring to the user's past feedback. For example, the providing unit customizes the display method based on the user's past feedback. In this way, the display method can be customized by reflecting the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can adjust the analysis algorithm based on the user's past feedback. For example, the analysis algorithm can be adjusted based on feedback provided by the user in the past. In addition, specific patterns can be extracted from the user's past analysis results to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis by referring to the user's past feedback.

[0055] The providing unit can adjust the display method of the recommendation results based on the user's past feedback. For example, detailed recommendation results can be provided based on the user's past feedback. Also, specific information can be displayed preferentially based on the user's past feedback. This makes it possible to select the optimal display method by referring to the user's past feedback.

[0056] The collection unit can prioritize collection of highly relevant attributes in consideration of the user's geographical location information. For example, if the user is in a specific area, personal attributes related to the food culture of that area can be prioritized. Also, if the user is traveling, personal attributes related to the food at the travel destination can be prioritized. This makes it possible to prioritize collection of highly relevant attributes in consideration of the user's geographical location information.

[0057] The analysis unit can apply different analysis algorithms depending on the category of cuisine. For example, in the case of Japanese cuisine, an analysis algorithm specialized for Japanese cuisine can be applied. In the case of Western cuisine, an analysis algorithm specialized for Western cuisine can be applied. In addition, in the case of Chinese cuisine, an analysis algorithm specialized for Chinese cuisine can be applied. This makes it possible to apply different analysis algorithms depending on the category of cuisine.

[0058] The providing unit can analyze the user's social media activity and suggest display content. For example, it can analyze photos of food shared by the user on social media and provide recommendation results that reflect the user's preferences. It can also provide related recommendation results based on the content posted by the user on social media. This makes it possible to analyze the user's social media activity and suggest display content.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects the user's personal attributes. Personal attributes include age, gender, hobbies, food preferences, allergy information, etc. For example, the user can enter information such as "I like spicy food" or "I have a nut allergy." Step 2: The collection unit collects past feedback. The past feedback includes past order history and evaluation comments. For example, a user can provide feedback such as "This dish was delicious" or "This dish was too spicy." Step 3: The collection unit collects restaurant menu information. Restaurant menu information includes the name of the dish, price, calorie information, etc. For example, the restaurant can provide information such as "We've added a new menu item" or "This dish is spicy." Step 4: The analysis unit analyzes the information collected by the collection unit and recommends the optimal order. The analysis takes into account calories and nutritional balance. The analysis unit can prioritize recommendations based on the user's personal attributes and past feedback. Step 5: The providing unit provides the recommendation results obtained by the analysis unit. The providing unit can provide the recommendation results to the user. The providing unit can also provide the recommendation results to the store, which can use them to improve marketing.

[0061] (Example 2) An AI system according to an embodiment of the present invention recommends optimal health-conscious orders based on a user's personal attributes, past feedback, and restaurant menu information. This AI system provides benefits to both users and restaurants. For example, a user inputs their food preferences and allergy information. The AI ​​system then accumulates the user's past orders and feedback. Furthermore, restaurant menu information is also input into the AI ​​system. Based on this information, the AI ​​system recommends optimal orders for the user. For example, if a user "likes spicy food" but "has a nut allergy," the AI ​​system recommends "spicy but nut-free dishes." Based on past feedback, the system also prioritizes recommendations based on the user's preferences. Furthermore, by sharing information with restaurants, this system can improve their marketing. For example, restaurants can develop new menu items and promote their products based on information such as "user preferences" and "popular dishes." This allows the AI ​​system to recommend optimal health-conscious orders based on the user's personal attributes, past feedback, and restaurant menu information. For example, if a user "likes spicy food" but "has a nut allergy," the AI ​​system will recommend "spicy but nut-free dishes." It also prioritizes recommendations based on past feedback based on the user's preferences. Furthermore, by sharing information with restaurants, this system can also help improve their marketing. For example, restaurants can develop new menu items and promote their products based on information such as "which dishes users like" and "which dishes are popular."

[0062] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects personal attributes of a user. Examples of personal attributes include, but are not limited to, age, gender, hobbies, food preferences, and allergy information. For example, the collection unit allows a user to input information such as "I like spicy food" or "I have a nut allergy." The collection unit also collects past feedback. Examples of past feedback include, but are not limited to, past order history and evaluation comments. For example, the collection unit can provide feedback such as "This dish was delicious" or "This dish was too spicy." The collection unit also collects restaurant menu information. Examples of restaurant menu information include, but are not limited to, dish names, prices, and calorie information. For example, the collection unit can provide information such as "A new menu item has been added" or "This dish is spicy." The analysis unit analyzes the information collected by the collection unit and recommends optimal orders. For example, the analysis is performed taking into account calories and nutritional balance, but is not limited to these examples. The analysis unit can perform analysis based on, for example, calories and nutritional balance. The analysis unit can also prioritize recommendations of dishes preferred by the user based on the user's personal attributes and past feedback. The provision unit provides the recommendation results obtained by the analysis unit. The provision unit can, for example, provide the recommendation results to the user. The provision unit can also provide the recommendation results to restaurants, which can be used to improve marketing. This allows the AI ​​system according to the embodiment to recommend optimal orders based on the user's personal attributes, past feedback, and restaurant menu information. For example, if a user "likes spicy food" but "has a nut allergy," the AI ​​system recommends "spicy but nut-free dishes." Based on past feedback, the AI ​​system also prioritizes recommendations of dishes preferred by the user. Furthermore, by sharing information with restaurants, this system can also improve the restaurant's marketing.For example, restaurants can develop new menu items and run promotions based on information such as "which dishes users like" and "which dishes are popular."

[0063] The analysis unit can perform analysis based on calories and nutritional balance. The analysis unit performs analysis based on calorie information, for example. For example, the analysis unit collects calorie information about dishes and recommends optimal dishes based on the user's calorie limit. The analysis unit can also perform analysis taking nutritional balance into consideration. For example, the analysis unit collects nutrient information about dishes and recommends optimal dishes based on the user's nutritional balance. The analysis unit can also perform analysis based on the user's health condition. For example, the analysis unit recommends healthy dishes based on the user's health checkup results. This makes it possible to perform analysis taking calories and nutritional balance into consideration.

[0064] The providing unit can provide the recommendation results to the user. For example, the providing unit notifies the user of the recommendation results to their smartphone. For example, the providing unit provides the recommendation results to the user by push notification. The providing unit can also send the recommendation results to the user's email address. For example, the providing unit provides the recommendation results to the user by email. The providing unit can also display the recommendation results in the user's dedicated app. For example, the providing unit displays the recommendation results on the screen of the dedicated app. This makes it possible to recommend the optimal order to the user.

[0065] The providing unit provides the recommendation results to the store, which can be used to improve marketing. The providing unit, for example, notifies the store's management system of the recommendation results. For example, the providing unit automatically sends the recommendation results to the store's management system. The providing unit can also send the recommendation results to the store's email address. For example, the providing unit provides the recommendation results to the store by email. The providing unit can also display the recommendation results on the store's dedicated app. For example, the providing unit displays the recommendation results on the screen of the dedicated app. This makes it possible to provide information that is useful for improving the store's marketing.

[0066] The collection unit can collect information about the user's dietary preferences and allergies. For example, the user can input information such as "I like spicy food" or "I have a nut allergy" into the collection unit. For example, the user inputs the dietary preference and allergy information into the collection unit through a dedicated app. The collection unit can also automatically collect the dietary preference and allergy information provided by the user in the past. For example, the collection unit extracts the dietary preference and allergy information from the user's past order history. The collection unit can also collect the dietary preference and allergy information by analyzing the user's social media activity. For example, the collection unit analyzes photos of food shared by the user on social media and collects preference trends. This makes it possible to collect the user's dietary preference and allergy information.

[0067] The collection unit can collect past feedback from users. For example, the collection unit can allow users to provide feedback such as "This dish was delicious" or "This dish was too spicy." For example, the collection unit allows users to input feedback through a dedicated app. The collection unit can also automatically collect feedback provided by users in the past. For example, the collection unit extracts feedback from the user's past order history. The collection unit can also collect feedback by analyzing the user's social media activity. For example, the collection unit analyzes photos of meals shared by users on social media and collects feedback. In this way, the user's past feedback can be collected.

[0068] The collection unit can estimate the user's emotions and adjust the timing of collecting personal attributes based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects detailed information about the user's food preferences and allergies. For example, the collection unit asks detailed questions when the user is relaxed. The collection unit can also collect personal attributes in the form of simple questions when the user is feeling stressed. For example, the collection unit asks simple questions when the user is feeling stressed. The collection unit can also quickly collect only minimal information when the user is in a hurry. For example, the collection unit asks minimal questions when the user is in a hurry. This makes it possible to adjust the timing of collecting personal attributes according to the user's emotions. 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.

[0069] The collection unit can analyze the user's past meal history and select the optimal collection method. The collection unit selects the collection method by analyzing preference trends based on, for example, dishes that the user has frequently ordered in the past. For example, the collection unit analyzes dishes that the user has frequently ordered in the past and asks detailed questions. The collection unit can also determine whether to ask detailed questions or simplify the questions based on feedback provided by the user in the past. For example, the collection unit analyzes feedback provided by the user in the past and adjusts the content of the questions. The collection unit can also adjust the information to be collected during a specific time period based on the user's past meal history. For example, the collection unit analyzes dishes that the user ordered during a specific time period and adjusts the information to be collected during that time period. In this way, the optimal collection method can be selected based on the user's past meal history.

[0070] The collection unit can filter the personal attributes based on the user's current health condition and lifestyle habits when collecting the personal attributes. For example, when the user inputs the results of a health checkup, the collection unit filters the personal attributes to be collected based on that information. For example, when the user inputs the results of a health checkup, the collection unit prioritizes collecting information about specific nutrients based on that information. Furthermore, when the user is on a specific diet, the collection unit can adjust the personal attributes to be collected based on that information. For example, when the user is on a specific diet, the collection unit prioritizes collecting information about calories and nutritional balance based on that information. Furthermore, when the user has an exercise habit, the collection unit can filter the personal attributes to be collected based on that information. For example, when the user has an exercise habit, the collection unit prioritizes collecting information about energy consumption based on that information. This makes it possible to filter the personal attributes based on the user's health condition and lifestyle habits.

[0071] When collecting personal attributes, the collection unit can select a collection means according to the user's input method. For example, if the user selects voice input, the collection unit collects personal attributes using voice recognition technology. For example, if the user selects voice input, the collection unit collects the user's dietary preferences and allergy information using voice recognition technology. Furthermore, if the user selects text input, the collection unit can also collect personal attributes using text analysis technology. For example, if the user selects text input, the collection unit collects the user's dietary preferences and allergy information using text analysis technology. Furthermore, if the user selects image input, the collection unit can also collect personal attributes using image analysis technology. For example, if the user selects image input, the collection unit collects the user's dietary preferences and allergy information using image analysis technology. This makes it possible to select the optimal collection means according to the user's input method.

[0072] The collection unit can estimate the user's emotions and determine the priority of personal attributes to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed personal attributes. For example, the collection unit asks detailed questions when the user is relaxed. The collection unit can also prioritize collecting only important personal attributes when the user is stressed. For example, the collection unit asks important questions when the user is stressed. The collection unit can also prioritize collecting a minimum number of important personal attributes when the user is in a hurry. For example, the collection unit asks a minimum number of questions when the user is in a hurry. This makes it possible to determine the priority of personal attributes to be collected according to the user's emotions. 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.

[0073] When collecting personal attributes, the collection unit can prioritize collecting highly relevant attributes in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting personal attributes related to the food culture of the area. For example, when the user is in a specific area, the collection unit collects information related to the food culture of the area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting personal attributes related to meals at the travel destination. For example, when the user is traveling, the collection unit collects information related to meals at the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting personal attributes related to daily meals. For example, when the user is at home, the collection unit collects information related to daily meals. In this way, it is possible to prioritize collecting highly relevant attributes in consideration of the user's geographical location information.

[0074] When collecting personal attributes, the collection unit can analyze the user's social media activities and collect related attributes. For example, the collection unit analyzes photos of food shared by the user on social media and collects preference trends. For example, the collection unit analyzes photos of food shared by the user on social media and collects preference trends. The collection unit can also collect information about dietary preferences and allergies based on the content of the user's posts on social media. For example, the collection unit analyzes the content of the user's posts on social media and collects information about dietary preferences and allergies. The collection unit can also collect related personal attributes by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends on social media and collects related personal attributes. In this way, it is possible to analyze the user's social media activities and collect related attributes.

[0075] When collecting personal attributes, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit determines whether to ask detailed questions or simplify questions based on feedback provided by the user in the past. For example, the collection unit analyzes feedback provided by the user in the past and determines whether to ask detailed questions or simplify questions. The collection unit can also preferentially collect specific information from the user's past feedback. For example, the collection unit preferentially collects specific information based on the user's past feedback. The collection unit can also customize the collection method by referring to the user's past feedback. For example, the collection unit customizes the collection method based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. For example, when the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit can also provide concise analysis results when the user is stressed. For example, when the user is stressed, the analysis unit provides concise analysis results. The analysis unit can also provide analysis results that are concise when the user is in a hurry. For example, when the user is in a hurry, the analysis unit provides analysis results that are concise. This makes it possible to adjust the way the analysis is presented depending on the user's emotions. Emotion estimation is achieved 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.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on calories and nutritional balance. For example, if a user is on a calorie restriction, the analysis unit analyzes calorie information in detail. For example, if a user is on a calorie restriction, the analysis unit analyzes calorie information in detail. Furthermore, if a user places importance on nutritional balance, the analysis unit can also perform a detailed analysis of nutrients. For example, if a user places importance on nutritional balance, the analysis unit can perform a detailed analysis of nutrients. Furthermore, if a user wants to ingest a specific nutrient, the analysis unit can prioritize analyzing information about that nutrient. For example, if a user wants to ingest a specific nutrient, the analysis unit prioritizes analyzing information about that nutrient. This makes it possible to adjust the level of detail of the analysis based on calories and nutritional balance.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of cuisine. For example, in the case of Japanese cuisine, the analysis unit applies an analysis algorithm specialized for Japanese cuisine. For example, the analysis unit applies an analysis algorithm specialized for Japanese cuisine. Furthermore, in the case of Western cuisine, the analysis unit can also apply an analysis algorithm specialized for Western cuisine. For example, the analysis unit applies an analysis algorithm specialized for Western cuisine. Furthermore, in the case of Chinese cuisine, the analysis unit can also apply an analysis algorithm specialized for Chinese cuisine. For example, the analysis unit applies an analysis algorithm specialized for Chinese cuisine. This makes it possible to apply different analysis algorithms depending on the category of cuisine.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the user in the past. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also improve the accuracy of the analysis by extracting a specific pattern from the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by extracting a specific pattern from the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. For example, the analysis unit provides concise analysis results when the user is stressed. The analysis unit can also provide analysis results that focus on the main points when the user is in a hurry. For example, the analysis unit provides analysis results that focus on the main points when the user is in a hurry. This makes it possible to adjust the length of the analysis 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the time when the food is served. For example, in the case of breakfast, the analysis unit prioritizes analyzing information related to breakfast. For example, the analysis unit prioritizes analyzing information related to breakfast. Furthermore, in the case of lunch, the analysis unit can also prioritize analyzing information related to lunch. For example, the analysis unit prioritizes analyzing information related to lunch. Furthermore, in the case of dinner, the analysis unit can also prioritize analyzing information related to dinner. For example, the analysis unit prioritizes analyzing information related to dinner. In this way, the priority of analysis can be determined based on the time when the food is served.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of dishes. For example, in the case of a main dish, the analysis unit prioritizes analyzing information related to the main dish. For example, the analysis unit prioritizes analyzing information related to the main dish. Furthermore, in the case of a side dish, the analysis unit can also prioritize analyzing information related to the side dish. For example, the analysis unit prioritizes analyzing information related to the side dish. Furthermore, in the case of a dessert, the analysis unit can also prioritize analyzing information related to the dessert. For example, the analysis unit prioritizes analyzing information related to the dessert. This makes it possible to adjust the order of analysis based on the relevance of dishes.

[0083] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides the analysis result using detailed technical terminology. For example, if the user has technical expertise, the analysis unit provides the analysis result using detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis result using concise and easy-to-understand language. For example, if the user does not have technical expertise, the analysis unit provides the analysis result using concise and easy-to-understand language. Furthermore, the analysis unit can adjust the way in which the analysis result is expressed according to the user's level of expertise. For example, the analysis unit adjusts the way in which the analysis result is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the analysis according to the user's level of expertise.

[0084] The providing unit can estimate the user's emotions and adjust the display method of the recommendation results based on the estimated user emotions. For example, when the user is relaxed, the providing unit provides detailed recommendation results. For example, when the user is relaxed, the providing unit provides detailed recommendation results. Furthermore, when the user is feeling stressed, the providing unit can also provide concise recommendation results. For example, when the user is feeling stressed, the providing unit provides concise recommendation results. Furthermore, when the user is in a hurry, the providing unit can also provide recommendation results that are concise. For example, when the user is in a hurry, the providing unit provides recommendation results that are concise. This makes it possible to adjust the display method of the recommendation results according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When providing a recommendation result, the providing unit can select an optimal display method by referring to the user's past feedback. The providing unit, for example, provides a detailed recommendation result based on feedback provided by the user in the past. For example, the providing unit provides a detailed recommendation result based on feedback provided by the user in the past. The providing unit can also preferentially display specific information from the user's past feedback. For example, the providing unit preferentially displays specific information based on the user's past feedback. The providing unit can also customize the display method by referring to the user's past feedback. For example, the providing unit customizes the display method based on the user's past feedback. This makes it possible to select an optimal display method by referring to the user's past feedback.

[0086] When providing a recommendation result, the providing unit can customize the display content according to the user's current health condition. For example, if the user inputs the results of a health checkup, the providing unit customizes the recommendation result based on that information. For example, if the user inputs the results of a health checkup, the providing unit customizes the recommendation result based on that information. In addition, if the user is on a specific diet, the providing unit can adjust the recommendation result based on that information. For example, if the user is on a specific diet, the providing unit adjusts the recommendation result based on that information. In addition, if the user has an exercise habit, the providing unit can customize the recommendation result based on that information. For example, if the user has an exercise habit, the providing unit customizes the recommendation result based on that information. In this way, the recommendation result can be customized according to the user's current health condition.

[0087] The providing unit can improve the display method by reflecting user feedback when providing recommendation results. The providing unit, for example, adjusts the display method based on feedback previously provided by the user. For example, the providing unit adjusts the display method based on feedback previously provided by the user. The providing unit can also preferentially display specific information based on user feedback. For example, the providing unit preferentially displays specific information based on user feedback. The providing unit can also customize the display method by referring to user feedback. For example, the providing unit customizes the display method based on user feedback. This makes it possible to improve the display method by reflecting user feedback.

[0088] The providing unit can estimate the user's emotions and determine the priority of recommendation results based on the estimated user emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed recommendation results. For example, when the user is relaxed, the providing unit can prioritize providing detailed recommendation results. Furthermore, when the user is feeling stressed, the providing unit can prioritize providing only important recommendation results. For example, when the user is feeling stressed, the providing unit can prioritize providing only important recommendation results. Furthermore, when the user is in a hurry, the providing unit can prioritize providing a minimum number of important recommendation results. For example, when the user is in a hurry, the providing unit prioritizes providing a minimum number of important recommendation results. This makes it possible to determine the priority of recommendation results according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When providing recommendation results, the providing unit can select an optimal display method taking into consideration the user's geographical location information. For example, when the user is in a specific region, the providing unit preferentially displays recommendation results related to the food culture of that region. For example, when the user is in a specific region, the providing unit preferentially displays recommendation results related to the food culture of that region. Furthermore, when the user is traveling, the providing unit can also preferentially display recommendation results related to meals at the travel destination. For example, when the user is traveling, the providing unit preferentially displays recommendation results related to meals at the travel destination. Furthermore, when the user is at home, the providing unit can also preferentially display recommendation results related to everyday meals. For example, when the user is at home, the providing unit preferentially displays recommendation results related to everyday meals. This makes it possible to select an optimal display method taking into consideration the user's geographical location information.

[0090] When providing recommendation results, the providing unit can analyze the user's social media activity and suggest display content. The providing unit, for example, analyzes photos of meals shared by the user on social media and provides recommendation results that reflect the user's preference trends. For example, the providing unit analyzes photos of meals shared by the user on social media and provides recommendation results that reflect the user's preference trends. The providing unit can also provide related recommendation results based on the content posted by the user on social media. For example, the providing unit provides related recommendation results based on the content posted by the user on social media. The providing unit can also provide related recommendation results by referring to the activities of the user's friends on social media. For example, the providing unit provides related recommendation results by referring to the activities of the user's friends on social media. In this way, display content can be suggested by analyzing the user's social media activity.

[0091] When providing a recommendation result, the providing unit can customize the display method by reflecting the user's past feedback. The providing unit, for example, adjusts the display method based on feedback provided by the user in the past. For example, the providing unit adjusts the display method based on feedback provided by the user in the past. The providing unit can also preferentially display specific information based on the user's past feedback. For example, the providing unit preferentially display specific information based on the user's past feedback. The providing unit can also customize the display method by referring to the user's past feedback. For example, the providing unit customizes the display method based on the user's past feedback. In this way, the display method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal attributes and past feedback of users via the control unit 46A of the smart device 14, and collects restaurant menu information via the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends optimal orders. For example, the provision unit provides the recommendation results to the user via the control unit 46A of the smart device 14, and provides them to the restaurant via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects personal attributes and past feedback of the user via the control unit 46A of the smart glasses 214, and collects restaurant menu information via the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends an optimal order. For example, the provision unit provides the recommendation results to the user via the control unit 46A of the smart glasses 214, and provides the recommendation results to the restaurant via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects personal attributes and past feedback of users via the control unit 46A of the headset type terminal 314, and collects restaurant menu information via the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends optimal orders. For example, the provision unit provides recommendation results to the user via the control unit 46A of the headset type terminal 314, and provides them to the restaurant via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal attributes and past feedback of users via the control unit 46A of the robot 414, and collects restaurant menu information via the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and recommends optimal orders. For example, the provision unit provides the recommendation results to the user via the control unit 46A of the robot 414, and provides them to the restaurant via the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be prioritized. If the user is stressed, a concise analysis can be prioritized. If the user is in a hurry, an analysis that focuses on the main points can be prioritized. This makes it possible to adjust the priority of analysis according to the user's emotions.

[0094] The providing unit can estimate the user's emotions and adjust the display method of the recommendation results based on the estimated user emotions. For example, if the user is relaxed, detailed recommendation results can be provided. If the user is stressed, concise recommendation results can be provided. Furthermore, if the user is in a hurry, recommendation results that focus on the main points can be provided. This makes it possible to adjust the display method of the recommendation results according to the user's emotions.

[0095] The collection unit can estimate the user's emotions and determine the priority of personal attributes to be collected based on the estimated user's emotions. For example, if the user is relaxed, detailed personal attributes can be collected with priority. If the user is stressed, only important personal attributes can be collected with priority. Also, if the user is in a hurry, only the minimum number of important personal attributes can be collected with priority. In this way, the priority of personal attributes to be collected can be determined according to the user's emotions.

[0096] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. If the user is in a hurry, analysis results that focus on the main points can be provided. This makes it possible to adjust the way the analysis is presented depending on the user's emotions.

[0097] The providing unit can estimate the user's emotions and determine the priority of recommendation results based on the estimated user's emotions. For example, if the user is relaxed, detailed recommendation results can be provided preferentially. If the user is stressed, only important recommendation results can be provided preferentially. Also, if the user is in a hurry, only the minimum number of important recommendation results can be provided preferentially. This makes it possible to determine the priority of recommendation results according to the user's emotions.

[0098] The analysis unit can adjust the analysis algorithm based on the user's past feedback. For example, the analysis algorithm can be adjusted based on feedback provided by the user in the past. In addition, specific patterns can be extracted from the user's past analysis results to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis by referring to the user's past feedback.

[0099] The providing unit can adjust the display method of the recommendation results based on the user's past feedback. For example, detailed recommendation results can be provided based on the user's past feedback. Also, specific information can be displayed preferentially based on the user's past feedback. This makes it possible to select the optimal display method by referring to the user's past feedback.

[0100] The collection unit can prioritize collection of highly relevant attributes in consideration of the user's geographical location information. For example, if the user is in a specific area, personal attributes related to the food culture of that area can be prioritized. Also, if the user is traveling, personal attributes related to the food at the travel destination can be prioritized. This makes it possible to prioritize collection of highly relevant attributes in consideration of the user's geographical location information.

[0101] The analysis unit can apply different analysis algorithms depending on the category of cuisine. For example, in the case of Japanese cuisine, an analysis algorithm specialized for Japanese cuisine can be applied. In the case of Western cuisine, an analysis algorithm specialized for Western cuisine can be applied. In addition, in the case of Chinese cuisine, an analysis algorithm specialized for Chinese cuisine can be applied. This makes it possible to apply different analysis algorithms depending on the category of cuisine.

[0102] The providing unit can analyze the user's social media activity and suggest display content. For example, it can analyze photos of food shared by the user on social media and provide recommendation results that reflect the user's preferences. It can also provide related recommendation results based on the content posted by the user on social media. This makes it possible to analyze the user's social media activity and suggest display content.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects the user's personal attributes. Personal attributes include age, gender, hobbies, food preferences, allergy information, etc. For example, the user can enter information such as "I like spicy food" or "I have a nut allergy." Step 2: The collection unit collects past feedback. The past feedback includes past order history and evaluation comments. For example, a user can provide feedback such as "This dish was delicious" or "This dish was too spicy." Step 3: The collection unit collects restaurant menu information. Restaurant menu information includes the name of the dish, price, calorie information, etc. For example, the restaurant can provide information such as "We've added a new menu item" or "This dish is spicy." Step 4: The analysis unit analyzes the information collected by the collection unit and recommends the optimal order. The analysis takes into account calories and nutritional balance. The analysis unit can prioritize recommendations based on the user's personal attributes and past feedback. Step 5: The providing unit provides the recommendation results obtained by the analysis unit. The providing unit can provide the recommendation results to the user. The providing unit can also provide the recommendation results to the store, which can use them to improve marketing.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects personal attributes of users; a collection unit that collects past feedback; A collection department that collects restaurant menu information; an analysis unit that analyzes the information collected by the collection unit and recommends orders; a providing unit that provides the recommendation results obtained by the analysis unit. A system characterized by:

2. The analysis unit Analysis based on calories and nutritional balance 2. The system of claim 1.

3. The providing unit Providing recommendation results to users 2. The system of claim 1.

4. The providing unit Providing recommendation results to stores to help improve marketing 2. The system of claim 1.

5. The collecting unit Collecting user's dietary preferences and allergies 2. The system of claim 1.

6. The collecting unit Collect past user feedback 2. The system of claim 1.

7. The collecting unit The system estimates user emotions and adjusts the timing of collecting personal attributes based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past dietary history and select the optimal collection method 2. The system of claim 1.

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