system

Abstract

The system according to the embodiment aims to propose an optimal lunch menu based on the user's preferences and health condition, and to provide related information. [Solution] A system according to an embodiment includes a collection unit, a generation unit, an information provision unit, and a linking unit. The collection unit collects information input by a user. The generation unit analyzes the information collected by the collection unit and generates a lunch menu. The information provision unit provides business hours, congestion levels, and map information based on the lunch menu generated by the generation unit. The linking unit makes payments and reservations based on the information provided by the information provision unit.

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 suggest optimal lunch menus based on a user's preferences and health status, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal lunch menu based on the user's preferences and health condition, and to provide related information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, an information provision unit, and a linking unit. The collection unit collects information input by a user. The generation unit analyzes the information collected by the collection unit and generates a lunch menu. The information provision unit provides business hours, congestion levels, and map information based on the lunch menu generated by the generation unit. The linking unit performs payment and reservations based on the information provided by the information provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal lunch menu based on the user's preferences and health condition, and provide related information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A lunch menu generation system according to an embodiment of the present invention learns a user's preferences, health status, and mood of the day, and generates an optimal lunch menu. This lunch menu generation system collects user input information, and a generation AI analyzes that information to propose an optimal lunch menu. It also provides information such as the restaurant's business hours, congestion status, and map information. This allows users to enjoy the optimal lunch without any hassle. Furthermore, by linking the system to a payment and reservation system, users can easily make lunch reservations and payments. This system is extremely useful for busy businesspeople, people struggling to find a lunch spot, and those looking to discover new restaurants. First, the system collects user input information, including the user's preferences, health status, and mood of the day. Next, the generation AI analyzes the collected information and proposes an optimal lunch menu. For example, if a user inputs "I want to eat Japanese food," the generation AI proposes an optimal Japanese menu based on that information. It also provides information such as the restaurant's business hours, congestion status, and map information. This allows users to enjoy the optimal lunch without any hassle. Furthermore, by linking the system to a payment and reservation system, users can easily make lunch reservations and payments. For example, if a user inputs "I'd like to make a reservation for 12 o'clock," the system will make the reservation based on that information and automatically process the payment. In this way, the user can enjoy lunch without any hassle. The lunch menu generation system then provides the optimal lunch menu based on the user's preferences, health condition, and mood of the day, and then processes the payment and reservation, enabling a hassle-free lunch experience.

[0029] A lunch menu generation system according to an embodiment includes a collection unit, a generation unit, an information provision unit, and a linking unit. The collection unit collects user input information. Examples of user input information include, but are not limited to, text input, voice input, and image input. The collection unit can, for example, acquire information from a database or collect information from a sensor. The collection unit can also collect information such as the user's preferences, health status, and mood of the day. For example, the collection unit collects information based on the user's previous menu selections and survey results. The generation unit analyzes the information collected by the collection unit and generates an optimal lunch menu. The generation unit analyzes the information using, for example, data mining or a machine learning algorithm. The generation unit uses a generation AI to generate an optimal lunch menu based on the user's preferences, health status, and mood of the day. For example, if a user inputs "I want to eat Japanese food," the generation unit generates an optimal Japanese food menu based on the information. The information provision unit provides business hours, congestion status, and map information based on the lunch menu generated by the generation unit. The information provision unit can, for example, link with GPS data and a map app. The information providing unit provides the user with information necessary for a hassle-free lunch enjoyment. For example, the information providing unit provides the user with the opening hours and congestion levels of restaurants the user plans to visit. The linking unit performs payment and reservations based on the information provided by the information providing unit. The linking unit provides payment methods such as credit cards, electronic money, and cash. The linking unit enables the user to easily make lunch reservations and payments. For example, if the user inputs "I would like to make a reservation for 12 o'clock," the linking unit can make a reservation based on that information and automatically perform payment. As a result, the lunch menu generation system according to the embodiment provides an optimal lunch menu based on the user's preferences, health condition, and mood of the day, and further enables a hassle-free lunch experience by performing payment and reservations.

[0030] The collection unit can collect information on the user's preferences, health status, and mood of the day. The collection unit collects information based on, for example, menus selected by the user in the past and survey results. The collection unit can use past selection history and survey results to collect the user's preferences. For example, the collection unit collects information based on menus the user has previously preferred to eat. The collection unit can also use medical data and self-reported data to collect the user's health status. For example, the collection unit collects information based on test results the user has received at a medical institution and self-reported data. Furthermore, the collection unit can use sentiment analysis and self-reporting to collect the user's mood of the day. For example, the collection unit provides an interface for the user to self-report their mood of the day and collects information based on that information. In this way, the collection unit can suggest a more appropriate lunch menu by collecting detailed information about the user.

[0031] The generation unit can analyze the collected information and generate a lunch menu. The generation unit can analyze the information using, for example, data mining or machine learning algorithms. The generation unit can use data mining technology to analyze the collected information. For example, the generation unit can extract patterns and trends from the collected data and generate a lunch menu based on the patterns and trends. The generation unit can also analyze the information using machine learning algorithms. For example, the generation unit can train a machine learning model to generate an optimal lunch menu based on the user's preferences, health status, and mood of the day. Furthermore, the generation unit can use a generation AI to generate an optimal lunch menu based on the user's preferences, health status, and mood of the day. For example, the generation unit can use a generation AI to analyze information input by the user and generate an optimal lunch menu based on the information. In this way, the generation unit can provide an optimal lunch menu to the user by analyzing the collected information.

[0032] The information providing unit can provide business hours, congestion levels, and map information based on the generated lunch menu. The information providing unit can, for example, cooperate with GPS data and a map app. The information providing unit can use GPS data to provide the business hours of restaurants the user plans to visit. For example, the information providing unit can provide the business hours of nearby restaurants based on the user's current location. The information providing unit can also use real-time people counting or predictions based on past data to provide the congestion levels. For example, the information providing unit can provide the congestion levels of restaurants in real time and provide the user with information to help them avoid congestion. Furthermore, the information providing unit can cooperate with a map app to provide map information. For example, the information providing unit can provide map information of restaurants the user plans to visit, allowing the user to reach their destination without getting lost. In this way, the information providing unit can provide information related to the lunch menu, allowing the user to enjoy lunch hassle-free.

[0033] The linking unit can perform payment and reservations based on the provided information. The linking unit provides payment methods such as credit cards, electronic money, and cash. The linking unit can provide credit card payment so that the user can easily make lunch reservations and payments. For example, the linking unit can quickly perform payment by having the user input credit card information. The linking unit can also provide a payment method using electronic money. For example, the linking unit provides an interface through which the user can make payments using electronic money. The linking unit can also provide a cash payment method. For example, the linking unit supports a procedure for the user to make a cash payment. This allows the linking unit to easily make lunch reservations and payments. Furthermore, if the user inputs "I would like to make a reservation for 12 o'clock," the linking unit can make a reservation based on that information and automatically perform payment. For example, the linking unit can make a reservation at the user's desired time and complete payment at the same time. This allows the linking unit to automatically perform payment and reservations, allowing the user to enjoy lunch without any hassle.

[0034] The collection unit can analyze the user's past meal history and select the optimal information collection method. For example, the collection unit prioritizes collecting related information based on the user's favorite menu items in the past. The collection unit can use a meal record app to collect the user's past meal history. For example, the collection unit collects the past meal history based on data entered by the user into the meal record app. The collection unit can also collect the past meal history using manually entered data. For example, the collection unit collects the past meal history based on meal data manually entered by the user. Furthermore, the collection unit can use data mining technology to analyze the past meal history. For example, the collection unit extracts patterns and trends from the past meal history and selects the optimal information collection method based on the extracted patterns and trends. This allows the collection unit to collect more appropriate information by analyzing the user's past meal history.

[0035] When collecting information, the collection unit can perform filtering based on the user's current health condition and dietary restrictions. For example, if the user has an allergy, the collection unit excludes information containing allergens. The collection unit can use medical data and self-reported data to collect the user's health condition. For example, the collection unit collects the user's health condition based on test results received at a medical institution and self-reported data. The collection unit can also use allergy information and doctor's instructions to collect the user's dietary restrictions. For example, if the user has an allergy, the collection unit excludes information containing allergens based on that information. Furthermore, if the user is on a diet, the collection unit can prioritize collecting low-calorie information. For example, if the user self-reports that they are on a diet, the collection unit prioritizes collecting low-calorie information based on that information. This allows the collection unit to provide information according to the user's health condition and dietary restrictions.

[0036] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting restaurant information near the user's current location. The collection unit can use GPS data or Wi-Fi location information to collect the user's geographical location information. For example, the collection unit collects nearby restaurant information based on the user's current location. The collection unit can also prioritize collecting information on areas frequently visited by the user. For example, the collection unit prioritizes collecting restaurant information in areas frequently visited by the user. Furthermore, if the user is traveling, the collection unit can prioritize collecting restaurant information at the user's travel destination. For example, the collection unit collects restaurant information that the user plans to visit at the user's travel destination. This allows the collection unit to provide highly relevant information based on the user's geographical location information.

[0037] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, if a user posts "lunch" on social media, the collection unit collects information related to the post. The collection unit can use the content of the post and the number of likes to collect the user's social media activity. For example, if a user posts "lunch" on social media, the collection unit collects restaurant information related to the post. The collection unit can also collect new menu information for restaurants followed by the user. For example, the collection unit prioritizes collecting new menu information for restaurants followed by the user. Furthermore, the collection unit can collect restaurant information shared by the user's friends. For example, the collection unit collects related information based on restaurant information shared by the user's friends. This allows the collection unit to provide related information based on the user's social media activity.

[0038] When generating a lunch menu, the generation unit can adjust the level of menu detail taking into account the user's nutritional balance. For example, if the user places importance on nutritional balance, the generation unit generates a menu including detailed nutritional information. The generation unit can use nutrient ratios and calorie calculations to evaluate the user's nutritional balance. For example, the generation unit generates a menu based on the ratio of nutrients needed by the user. Furthermore, if the user needs a specific nutrient, the generation unit can generate a menu that emphasizes that nutrient. For example, if the user wants to consume a lot of vitamin C, the generation unit generates a menu that is high in vitamin C based on that information. Furthermore, if the user is on a diet, the generation unit can generate a menu with detailed calorie information. For example, if the user self-reports that they are on a diet, the generation unit generates a low-calorie menu based on that information. This allows the generation unit to provide a menu that suits the user's nutritional balance.

[0039] When generating a lunch menu, the generation unit can apply different generation algorithms depending on the user's dietary restrictions. For example, if the user has allergies, the generation unit generates a menu that does not include allergens. The generation unit can use allergy information or doctor's instructions to collect the user's dietary restrictions. For example, if the user has allergies, the generation unit generates a menu that does not include allergens based on that information. The generation unit can also generate a menu that does not include meat if the user is a vegetarian. For example, if the user self-reports that they are vegetarian, the generation unit generates a menu that does not include meat based on that information. Furthermore, the generation unit can also generate a low-carbohydrate menu if the user has diabetes. For example, if the user self-reports that they are diabetic, the generation unit generates a low-carbohydrate menu based on that information. This allows the generation unit to provide a menu that meets the user's dietary restrictions.

[0040] When generating a lunch menu, the generation unit can prioritize menu items based on the user's past meal history. For example, the generation unit preferentially suggests menu items that the user has previously preferred. The generation unit can use a meal record app to collect the user's past meal history. For example, the generation unit collects the past meal history based on data entered by the user into the meal record app. The generation unit can also collect the past meal history using manually entered data. For example, the generation unit collects the past meal history based on meal data manually entered by the user. Furthermore, the generation unit can use data mining technology to analyze the past meal history. For example, the generation unit extracts patterns and trends from the past meal history and prioritizes the menu items based on the patterns and trends. This allows the generation unit to provide an appropriate menu based on the user's past meal history.

[0041] When generating a lunch menu, the generation unit can adjust the order of the menu items based on the user's health data. For example, the generation unit can prioritize and suggest appropriate menu items based on items identified in a health checkup. The generation unit can use medical data and fitness data to collect the user's health data. For example, the generation unit can collect health data based on test results the user has received at a medical institution or data entered into a fitness app. The generation unit can also prioritize and suggest easy-to-digest menu items based on the user's health condition. For example, if the user desires an easy-to-digest meal, the generation unit can generate an easy-to-digest menu item based on that information. Furthermore, the generation unit can prioritize and suggest menu items containing specific nutrients based on the user's health data. For example, if the user desires to consume a large amount of a specific nutrient, the generation unit can generate a menu item containing that nutrient based on that information. This allows the generation unit to provide an appropriate menu item based on the user's health data.

[0042] When providing information, the information providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the information providing unit preferentially suggests information providing methods that the user has used in the past. The information providing unit can use log data and survey results to collect the user's past usage history. For example, the information providing unit selects the optimal information providing method based on information providing methods that the user has used in the past. The information providing unit can also select the optimal information providing method from the user's past usage history. For example, the information providing unit preferentially suggests information providing methods that the user has preferred in the past. Furthermore, the information providing unit can use data mining technology to analyze the user's past usage history. For example, the information providing unit extracts patterns and trends from the past usage history and selects the optimal information providing method based on the extracted patterns and trends. This allows the information providing unit to select an appropriate information providing method based on the user's past usage history.

[0043] The information providing unit can adjust the timing of displaying information based on the user's current schedule when providing information. For example, the information providing unit provides information at an optimal timing in accordance with the user's schedule. The information providing unit can use a calendar app or manually input data to collect the user's current schedule. For example, the information providing unit adjusts the timing of displaying information based on schedule data input by the user into a calendar app. The information providing unit can also adjust the timing of displaying information based on schedule data manually input by the user. Furthermore, the information providing unit can analyze the user's activity pattern to adjust the timing of displaying information based on the user's schedule. For example, the information providing unit provides information by avoiding busy hours based on the user's activity pattern. This allows the information providing unit to provide information at an appropriate timing based on the user's schedule.

[0044] When providing information, the information providing unit can provide optimal information taking into consideration the user's geographical location information. For example, the information providing unit can prioritize providing restaurant information near the user's current location. The information providing unit can use GPS data or Wi-Fi location information to collect the user's geographical location information. For example, the information providing unit can provide nearby restaurant information based on the user's current location. The information providing unit can also prioritize providing information about areas the user frequently visits. For example, the information providing unit prioritizes providing restaurant information about areas the user frequently visits. Furthermore, when the user is traveling, the information providing unit can prioritize providing restaurant information about the user's travel destination. For example, the information providing unit provides information about restaurants the user plans to visit at the travel destination. This allows the information providing unit to provide highly relevant information based on the user's geographical location information.

[0045] When providing information, the information providing unit can analyze the user's social media activity and provide related information. For example, if a user posts "lunch" on social media, the information providing unit provides information related to the post. The information providing unit can use the content of the post and the number of likes to collect the user's social media activity. For example, if a user posts "lunch" on social media, the information providing unit provides restaurant information related to the post. The information providing unit can also provide new menu information for restaurants followed by the user. For example, the information providing unit preferentially provides new menu information for restaurants followed by the user. Furthermore, the information providing unit can also provide restaurant information shared by the user's friends. For example, the information providing unit provides related information based on restaurant information shared by the user's friends. This allows the information providing unit to provide related information based on the user's social media activity.

[0046] The collaboration unit can select the optimal payment and reservation method by referring to the user's past usage history. For example, the collaboration unit prioritizes suggesting payment methods that the user has used in the past. The collaboration unit can use log data and survey results to collect the user's past usage history. For example, the collaboration unit selects the optimal method based on payment methods that the user has used in the past. The collaboration unit can also select the optimal reservation method from the user's past usage history. For example, the collaboration unit prioritizes suggesting reservation methods that the user has preferred in the past. Furthermore, the collaboration unit can use data mining technology to analyze the user's past usage history. For example, the collaboration unit extracts patterns and trends from the past usage history and selects the optimal payment and reservation method based on the extracted patterns and trends. This allows the collaboration unit to provide an appropriate payment and reservation method based on the user's past usage history.

[0047] The collaboration unit can adjust the optimal timing for payment and reservation based on the user's current schedule. For example, the collaboration unit makes reservations and payments at the optimal timing according to the user's schedule. The collaboration unit can use a calendar app or manually input data to collect the user's current schedule. For example, the collaboration unit adjusts the timing for reservations and payments based on schedule data input by the user into a calendar app. The collaboration unit can also adjust the timing for reservations and payments based on schedule data manually input by the user. Furthermore, the collaboration unit can analyze the user's activity patterns to adjust the timing for reservations and payments based on the user's schedule. For example, the collaboration unit makes reservations and payments to avoid busy hours based on the user's activity patterns. This allows the collaboration unit to make payments and reservations at appropriate times based on the user's schedule.

[0048] The linking unit can select the optimal method for payment and reservation by taking into consideration the user's geographical location information. For example, the linking unit prioritizes providing reservation and payment methods for restaurants near the user's current location. The linking unit can use GPS data and Wi-Fi location information to collect the user's geographical location information. For example, the linking unit can provide reservation and payment methods for nearby restaurants based on the user's current location. The linking unit can also prioritize providing reservation and payment methods for restaurants in areas the user frequently visits. For example, the linking unit prioritizes providing reservation and payment methods for restaurants in areas the user frequently visits. Furthermore, if the user is traveling, the linking unit can prioritize providing reservation and payment methods for restaurants in the user's travel destination. For example, the linking unit provides reservation and payment methods for restaurants the user plans to visit at the user's travel destination. This allows the linking unit to provide an appropriate payment and reservation method based on the user's geographical location information.

[0049] The linking unit can analyze a user's social media activity and provide related information at the time of payment and reservation. For example, if a user posts "lunch" on social media, the linking unit provides restaurant reservation and payment methods related to the post. The linking unit can use the content of the post and the number of likes to collect the user's social media activity. For example, if a user posts "lunch" on social media, the linking unit provides restaurant reservation and payment methods related to the post. The linking unit can also provide reservation and payment methods for restaurants that the user follows. For example, the linking unit prioritizes providing reservation and payment methods for restaurants that the user follows. Furthermore, the linking unit can also provide reservation and payment methods for restaurants shared by the user's friends. For example, the linking unit provides related information based on restaurant information shared by the user's friends. This allows the linking unit to provide related payment and reservation methods based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can identify the user's preferred ingredients and cooking methods based on the user's past meal history, and improve the accuracy of information collection based on this. For example, the collection unit can prioritize collecting ingredients and cooking methods that the user has frequently selected in the past, and suggest menus that suit the user's preferences. The collection unit can also analyze the user's intake trends of specific nutrients from the user's meal history and collect information that takes nutritional balance into consideration. Furthermore, the collection unit can collect information for suggesting menus according to seasons or events based on the user's meal history. This allows the collection unit to provide more personalized information by utilizing the user's past meal history.

[0052] The information providing unit can select the optimal information providing method by taking into consideration the user's current activity status. For example, if the user is exercising, priority can be given to providing information by voice. Also, if the user is in a meeting, adjustments can be made, such as reducing notifications. Furthermore, detailed information can be provided during times when the user is relaxing. In this way, the information providing unit can provide more appropriate information by selecting an information providing method according to the user's activity status.

[0053] The collection unit can analyze the user's social media activity and collect information related to topics that interest the user. For example, if a user posts "healthy lunch" on social media, the collection unit can collect healthy menu information related to that post. It can also collect trending menu information based on the posts of influencers the user follows. It can also collect related information based on restaurant information shared by the user's friends. This allows the collection unit to provide more relevant information based on the user's social media activity.

[0054] The information providing unit can select the optimal information providing method by referring to the user's past usage history. For example, if the user has preferred voice information provision in the past, voice information provision can be prioritized. Also, if the user has preferred text information provision in the past, text information provision can be prioritized. Furthermore, data mining technology can be used to select the optimal information providing method from the user's past usage history. This allows the information providing unit to select a more appropriate information providing method based on the user's past usage history.

[0055] The collaboration unit can make reservations and payments at optimal times based on the user's current schedule. For example, if the user is in a meeting, the collaboration unit can make reservations and payments after the meeting ends. Alternatively, if the user is exercising, the collaboration unit can make reservations and payments after the exercise ends. Furthermore, the collaboration unit can analyze the user's activity patterns to adjust the timing of reservations and payments based on the user's schedule. This allows the collaboration unit to make reservations and payments at more appropriate times based on the user's schedule.

[0056] The generation unit can adjust the nutritional balance of the menu based on the user's health data. For example, if the user needs to consume a lot of a specific nutrient, the generation unit can suggest a menu that is rich in that nutrient. Also, if the user is on a diet, the generation unit can suggest a low-calorie menu. Furthermore, if the user has an allergy, the generation unit can suggest a menu that does not contain allergens. This allows the generation unit to provide healthier menus based on the user's health data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects user input information. User input information includes text input, voice input, and image input. The collection unit can retrieve information from a database or collect information from sensors, and collect information such as the user's preferences, health status, and mood for the day. For example, information can be collected based on the user's past menu selections or survey results. Step 2: The generation unit analyzes the information collected by the collection unit and generates the optimal lunch menu. The generation unit analyzes the information using data mining and machine learning algorithms, and uses generation AI to generate the optimal lunch menu based on the user's preferences, health condition, and mood of the day. For example, if a user inputs "I want to eat Japanese food," the optimal Japanese food menu will be generated based on that information. Step 3: The information providing unit provides business hours, congestion levels, and map information based on the lunch menu generated by the generation unit. The information providing unit works in conjunction with GPS data and map apps to provide the information the user needs to enjoy lunch hassle-free. For example, it provides the business hours and congestion levels of restaurants the user plans to visit. Step 4: The linking unit makes the payment and reservation based on the information provided by the information providing unit. The linking unit provides payment methods such as credit card, electronic money, and cash, allowing the user to easily make lunch reservations and payments. For example, if the user inputs "I would like to make a reservation for 12 o'clock," the reservation can be made based on that information, and payment can also be made automatically.

[0059] (Example 2) A lunch menu generation system according to an embodiment of the present invention learns a user's preferences, health status, and mood of the day, and generates an optimal lunch menu. This lunch menu generation system collects user input information, and a generation AI analyzes that information to propose an optimal lunch menu. It also provides information such as the restaurant's business hours, congestion status, and map information. This allows users to enjoy the optimal lunch without any hassle. Furthermore, by linking the system to a payment and reservation system, users can easily make lunch reservations and payments. This system is extremely useful for busy businesspeople, people struggling to find a lunch spot, and those looking to discover new restaurants. First, the system collects user input information, including the user's preferences, health status, and mood of the day. Next, the generation AI analyzes the collected information and proposes an optimal lunch menu. For example, if a user inputs "I want to eat Japanese food," the generation AI proposes an optimal Japanese menu based on that information. It also provides information such as the restaurant's business hours, congestion status, and map information. This allows users to enjoy the optimal lunch without any hassle. Furthermore, by linking the system to a payment and reservation system, users can easily make lunch reservations and payments. For example, if a user inputs "I'd like to make a reservation for 12 o'clock," the system will make the reservation based on that information and automatically process the payment. In this way, the user can enjoy lunch without any hassle. The lunch menu generation system then provides the optimal lunch menu based on the user's preferences, health condition, and mood of the day, and then processes the payment and reservation, enabling a hassle-free lunch experience.

[0060] A lunch menu generation system according to an embodiment includes a collection unit, a generation unit, an information provision unit, and a linking unit. The collection unit collects user input information. Examples of user input information include, but are not limited to, text input, voice input, and image input. The collection unit can, for example, acquire information from a database or collect information from a sensor. The collection unit can also collect information such as the user's preferences, health status, and mood of the day. For example, the collection unit collects information based on the user's previous menu selections and survey results. The generation unit analyzes the information collected by the collection unit and generates an optimal lunch menu. The generation unit analyzes the information using, for example, data mining or a machine learning algorithm. The generation unit uses a generation AI to generate an optimal lunch menu based on the user's preferences, health status, and mood of the day. For example, if a user inputs "I want to eat Japanese food," the generation unit generates an optimal Japanese food menu based on the information. The information provision unit provides business hours, congestion status, and map information based on the lunch menu generated by the generation unit. The information provision unit can, for example, link with GPS data and a map app. The information providing unit provides the user with information necessary for a hassle-free lunch enjoyment. For example, the information providing unit provides the user with the opening hours and congestion levels of restaurants the user plans to visit. The linking unit performs payment and reservations based on the information provided by the information providing unit. The linking unit provides payment methods such as credit cards, electronic money, and cash. The linking unit enables the user to easily make lunch reservations and payments. For example, if the user inputs "I would like to make a reservation for 12 o'clock," the linking unit can make a reservation based on that information and automatically perform payment. As a result, the lunch menu generation system according to the embodiment provides an optimal lunch menu based on the user's preferences, health condition, and mood of the day, and further enables a hassle-free lunch experience by performing payment and reservations.

[0061] The collection unit can collect information on the user's preferences, health status, and mood of the day. The collection unit collects information based on, for example, menus selected by the user in the past and survey results. The collection unit can use past selection history and survey results to collect the user's preferences. For example, the collection unit collects information based on menus the user has previously preferred to eat. The collection unit can also use medical data and self-reported data to collect the user's health status. For example, the collection unit collects information based on test results the user has received at a medical institution and self-reported data. Furthermore, the collection unit can use sentiment analysis and self-reporting to collect the user's mood of the day. For example, the collection unit provides an interface for the user to self-report their mood of the day and collects information based on that information. In this way, the collection unit can suggest a more appropriate lunch menu by collecting detailed information about the user.

[0062] The generation unit can analyze the collected information and generate a lunch menu. The generation unit can analyze the information using, for example, data mining or machine learning algorithms. The generation unit can use data mining technology to analyze the collected information. For example, the generation unit can extract patterns and trends from the collected data and generate a lunch menu based on the patterns and trends. The generation unit can also analyze the information using machine learning algorithms. For example, the generation unit can train a machine learning model to generate an optimal lunch menu based on the user's preferences, health status, and mood of the day. Furthermore, the generation unit can use a generation AI to generate an optimal lunch menu based on the user's preferences, health status, and mood of the day. For example, the generation unit can use a generation AI to analyze information input by the user and generate an optimal lunch menu based on the information. In this way, the generation unit can provide an optimal lunch menu to the user by analyzing the collected information.

[0063] The information providing unit can provide business hours, congestion levels, and map information based on the generated lunch menu. The information providing unit can, for example, cooperate with GPS data and a map app. The information providing unit can use GPS data to provide the business hours of restaurants the user plans to visit. For example, the information providing unit can provide the business hours of nearby restaurants based on the user's current location. The information providing unit can also use real-time people counting or predictions based on past data to provide the congestion levels. For example, the information providing unit can provide the congestion levels of restaurants in real time and provide the user with information to help them avoid congestion. Furthermore, the information providing unit can cooperate with a map app to provide map information. For example, the information providing unit can provide map information of restaurants the user plans to visit, allowing the user to reach their destination without getting lost. In this way, the information providing unit can provide information related to the lunch menu, allowing the user to enjoy lunch hassle-free.

[0064] The linking unit can perform payment and reservations based on the provided information. The linking unit provides payment methods such as credit cards, electronic money, and cash. The linking unit can provide credit card payment so that the user can easily make lunch reservations and payments. For example, the linking unit can quickly perform payment by having the user input credit card information. The linking unit can also provide a payment method using electronic money. For example, the linking unit provides an interface through which the user can make payments using electronic money. The linking unit can also provide a cash payment method. For example, the linking unit supports a procedure for the user to make a cash payment. This allows the linking unit to easily make lunch reservations and payments. Furthermore, if the user inputs "I would like to make a reservation for 12 o'clock," the linking unit can make a reservation based on that information and automatically perform payment. For example, the linking unit can make a reservation at the user's desired time and complete payment at the same time. This allows the linking unit to automatically perform payment and reservations, allowing the user to enjoy lunch without any hassle.

[0065] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during times when the user is able to relax. The collection unit can use facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the collection unit can adjust the timing of information collection based on the user's emotions. For example, the collection unit collects detailed information during times when the user is relaxed and adjusts the information collection to complete quickly during times when the user is busy. This allows the collection unit to collect more appropriate information by adjusting the timing of information collection according to 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.

[0066] The collection unit can analyze the user's past meal history and select the optimal information collection method. For example, the collection unit prioritizes collecting related information based on the user's favorite menu items in the past. The collection unit can use a meal record app to collect the user's past meal history. For example, the collection unit collects the past meal history based on data entered by the user into the meal record app. The collection unit can also collect the past meal history using manually entered data. For example, the collection unit collects the past meal history based on meal data manually entered by the user. Furthermore, the collection unit can use data mining technology to analyze the past meal history. For example, the collection unit extracts patterns and trends from the past meal history and selects the optimal information collection method based on the extracted patterns and trends. This allows the collection unit to collect more appropriate information by analyzing the user's past meal history.

[0067] When collecting information, the collection unit can perform filtering based on the user's current health condition and dietary restrictions. For example, if the user has an allergy, the collection unit excludes information containing allergens. The collection unit can use medical data and self-reported data to collect the user's health condition. For example, the collection unit collects the user's health condition based on test results received at a medical institution and self-reported data. The collection unit can also use allergy information and doctor's instructions to collect the user's dietary restrictions. For example, if the user has an allergy, the collection unit excludes information containing allergens based on that information. Furthermore, if the user is on a diet, the collection unit can prioritize collecting low-calorie information. For example, if the user self-reports that they are on a diet, the collection unit prioritizes collecting low-calorie information based on that information. This allows the collection unit to provide information according to the user's health condition and dietary restrictions.

[0068] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. For example, if the user is tired, the collection unit prioritizes collecting information about menus that will help the user relax. The collection unit can use facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the collection unit can prioritize the information to be collected based on the user's emotions. For example, the collection unit prioritizes collecting detailed information when the user is relaxed, and adjusts the information collection to complete quickly when the user is busy. This allows the collection unit to provide more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting restaurant information near the user's current location. The collection unit can use GPS data or Wi-Fi location information to collect the user's geographical location information. For example, the collection unit collects nearby restaurant information based on the user's current location. The collection unit can also prioritize collecting information on areas frequently visited by the user. For example, the collection unit prioritizes collecting restaurant information in areas frequently visited by the user. Furthermore, if the user is traveling, the collection unit can prioritize collecting restaurant information at the user's travel destination. For example, the collection unit collects restaurant information that the user plans to visit at the user's travel destination. This allows the collection unit to provide highly relevant information based on the user's geographical location information.

[0070] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, if a user posts "lunch" on social media, the collection unit collects information related to the post. The collection unit can use the content of the post and the number of likes to collect the user's social media activity. For example, if a user posts "lunch" on social media, the collection unit collects restaurant information related to the post. The collection unit can also collect new menu information for restaurants followed by the user. For example, the collection unit prioritizes collecting new menu information for restaurants followed by the user. Furthermore, the collection unit can collect restaurant information shared by the user's friends. For example, the collection unit collects related information based on restaurant information shared by the user's friends. This allows the collection unit to provide related information based on the user's social media activity.

[0071] The generation unit can estimate the user's emotions and adjust the presentation of the lunch menu based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a menu that progresses at a leisurely pace. The generation unit can use facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit can adjust the presentation of the lunch menu based on the user's emotions. For example, if the user is relaxed, the generation unit generates a menu that progresses at a leisurely pace, and if the user is in a hurry, the generation unit generates a menu that is concise and to the point. This allows the generation unit to provide a more appropriate menu by adjusting the presentation of the lunch menu according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] When generating a lunch menu, the generation unit can adjust the level of menu detail taking into account the user's nutritional balance. For example, if the user places importance on nutritional balance, the generation unit generates a menu including detailed nutritional information. The generation unit can use nutrient ratios and calorie calculations to evaluate the user's nutritional balance. For example, the generation unit generates a menu based on the ratio of nutrients needed by the user. Furthermore, if the user needs a specific nutrient, the generation unit can generate a menu that emphasizes that nutrient. For example, if the user wants to consume a lot of vitamin C, the generation unit generates a menu that is high in vitamin C based on that information. Furthermore, if the user is on a diet, the generation unit can generate a menu with detailed calorie information. For example, if the user self-reports that they are on a diet, the generation unit generates a low-calorie menu based on that information. This allows the generation unit to provide a menu that suits the user's nutritional balance.

[0073] When generating a lunch menu, the generation unit can apply different generation algorithms depending on the user's dietary restrictions. For example, if the user has allergies, the generation unit generates a menu that does not include allergens. The generation unit can use allergy information or doctor's instructions to collect the user's dietary restrictions. For example, if the user has allergies, the generation unit generates a menu that does not include allergens based on that information. The generation unit can also generate a menu that does not include meat if the user is a vegetarian. For example, if the user self-reports that they are vegetarian, the generation unit generates a menu that does not include meat based on that information. Furthermore, the generation unit can also generate a low-carbohydrate menu if the user has diabetes. For example, if the user self-reports that they are diabetic, the generation unit generates a low-carbohydrate menu based on that information. This allows the generation unit to provide a menu that meets the user's dietary restrictions.

[0074] The generation unit can estimate the user's emotions and adjust the length of the lunch menu based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit generates a short and concise menu. The generation unit can use facial expression recognition technology to estimate the user's emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the generation unit can adjust the length of the lunch menu based on the user's emotions. For example, if the user is in a hurry, the generation unit generates a short and concise menu, and if the user is relaxed, the generation unit generates a longer menu with detailed explanations. This allows the generation unit to provide a more appropriate menu by adjusting the length of the lunch menu according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] When generating a lunch menu, the generation unit can prioritize menu items based on the user's past meal history. For example, the generation unit preferentially suggests menu items that the user has previously preferred. The generation unit can use a meal record app to collect the user's past meal history. For example, the generation unit collects the past meal history based on data entered by the user into the meal record app. The generation unit can also collect the past meal history using manually entered data. For example, the generation unit collects the past meal history based on meal data manually entered by the user. Furthermore, the generation unit can use data mining technology to analyze the past meal history. For example, the generation unit extracts patterns and trends from the past meal history and prioritizes the menu items based on the patterns and trends. This allows the generation unit to provide an appropriate menu based on the user's past meal history.

[0076] When generating a lunch menu, the generation unit can adjust the order of the menu items based on the user's health data. For example, the generation unit can prioritize and suggest appropriate menu items based on items identified in a health checkup. The generation unit can use medical data and fitness data to collect the user's health data. For example, the generation unit can collect health data based on test results the user has received at a medical institution or data entered into a fitness app. The generation unit can also prioritize and suggest easy-to-digest menu items based on the user's health condition. For example, if the user desires an easy-to-digest meal, the generation unit can generate an easy-to-digest menu item based on that information. Furthermore, the generation unit can prioritize and suggest menu items containing specific nutrients based on the user's health data. For example, if the user desires to consume a large amount of a specific nutrient, the generation unit can generate a menu item containing that nutrient based on that information. This allows the generation unit to provide an appropriate menu item based on the user's health data.

[0077] The information providing unit can estimate the user's emotions and adjust the display method of the information provided based on the estimated user's emotions. For example, if the user is nervous, the information providing unit provides a simple, highly visible display method. The information providing unit can use facial expression recognition technology to estimate the user's emotions. For example, the information providing unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The information providing unit can also estimate the user's emotions using voice analysis technology. For example, the information providing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the information providing unit can adjust the display method of the information provided based on the user's emotions. For example, if the user is nervous, the information providing unit provides a simple, highly visible display method, and if the user is relaxed, it provides a display method including detailed information. This allows the information providing unit to provide more appropriate information by adjusting the display method of the information provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] When providing information, the information providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the information providing unit preferentially suggests information providing methods that the user has used in the past. The information providing unit can use log data and survey results to collect the user's past usage history. For example, the information providing unit selects the optimal information providing method based on information providing methods that the user has used in the past. The information providing unit can also select the optimal information providing method from the user's past usage history. For example, the information providing unit preferentially suggests information providing methods that the user has preferred in the past. Furthermore, the information providing unit can use data mining technology to analyze the user's past usage history. For example, the information providing unit extracts patterns and trends from the past usage history and selects the optimal information providing method based on the extracted patterns and trends. This allows the information providing unit to select an appropriate information providing method based on the user's past usage history.

[0079] The information providing unit can adjust the timing of displaying information based on the user's current schedule when providing information. For example, the information providing unit provides information at an optimal timing in accordance with the user's schedule. The information providing unit can use a calendar app or manually input data to collect the user's current schedule. For example, the information providing unit adjusts the timing of displaying information based on schedule data input by the user into a calendar app. The information providing unit can also adjust the timing of displaying information based on schedule data manually input by the user. Furthermore, the information providing unit can analyze the user's activity pattern to adjust the timing of displaying information based on the user's schedule. For example, the information providing unit provides information by avoiding busy hours based on the user's activity pattern. This allows the information providing unit to provide information at an appropriate timing based on the user's schedule.

[0080] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, if the user is tired, the information providing unit can prioritize providing information that helps the user relax. The information providing unit can use facial expression recognition technology to estimate the user's emotions. For example, the information providing unit can capture the user's facial expressions with a camera and estimate the emotion using a facial expression recognition algorithm. The information providing unit can also estimate the user's emotions using voice analysis technology. For example, the information providing unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the information providing unit can determine the priority of information provision based on the user's emotions. For example, if the user is relaxed, the information providing unit can prioritize providing detailed information, and if the user is busy, the information providing unit can adjust the information provision so that it is completed in a short time. This allows the information providing unit to provide more appropriate information by determining the priority of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] When providing information, the information providing unit can provide optimal information taking into consideration the user's geographical location information. For example, the information providing unit can prioritize providing restaurant information near the user's current location. The information providing unit can use GPS data or Wi-Fi location information to collect the user's geographical location information. For example, the information providing unit can provide nearby restaurant information based on the user's current location. The information providing unit can also prioritize providing information about areas the user frequently visits. For example, the information providing unit prioritizes providing restaurant information about areas the user frequently visits. Furthermore, when the user is traveling, the information providing unit can prioritize providing restaurant information about the user's travel destination. For example, the information providing unit provides information about restaurants the user plans to visit at the travel destination. This allows the information providing unit to provide highly relevant information based on the user's geographical location information.

[0082] When providing information, the information providing unit can analyze the user's social media activity and provide related information. For example, if a user posts "lunch" on social media, the information providing unit provides information related to the post. The information providing unit can use the content of the post and the number of likes to collect the user's social media activity. For example, if a user posts "lunch" on social media, the information providing unit provides restaurant information related to the post. The information providing unit can also provide new menu information for restaurants followed by the user. For example, the information providing unit preferentially provides new menu information for restaurants followed by the user. Furthermore, the information providing unit can also provide restaurant information shared by the user's friends. For example, the information providing unit provides related information based on restaurant information shared by the user's friends. This allows the information providing unit to provide related information based on the user's social media activity.

[0083] The collaboration unit can estimate the user's emotions and adjust the payment and reservation methods based on the estimated user emotions. For example, if the user is nervous, the collaboration unit can provide a simple and quick payment method. The collaboration unit can use facial expression recognition technology to estimate the user's emotions. For example, the collaboration unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The collaboration unit can also estimate the user's emotions using voice analysis technology. For example, the collaboration unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the collaboration unit can adjust the payment and reservation methods based on the user's emotions. For example, the collaboration unit can provide a simple and quick payment method if the user is nervous, and a payment method with detailed instructions if the user is relaxed. This allows the collaboration unit to provide a more appropriate method by adjusting the payment and reservation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The collaboration unit can select the optimal payment and reservation method by referring to the user's past usage history. For example, the collaboration unit prioritizes suggesting payment methods that the user has used in the past. The collaboration unit can use log data and survey results to collect the user's past usage history. For example, the collaboration unit selects the optimal method based on payment methods that the user has used in the past. The collaboration unit can also select the optimal reservation method from the user's past usage history. For example, the collaboration unit prioritizes suggesting reservation methods that the user has preferred in the past. Furthermore, the collaboration unit can use data mining technology to analyze the user's past usage history. For example, the collaboration unit extracts patterns and trends from the past usage history and selects the optimal payment and reservation method based on the extracted patterns and trends. This allows the collaboration unit to provide an appropriate payment and reservation method based on the user's past usage history.

[0085] The collaboration unit can adjust the optimal timing for payment and reservation based on the user's current schedule. For example, the collaboration unit makes reservations and payments at the optimal timing according to the user's schedule. The collaboration unit can use a calendar app or manually input data to collect the user's current schedule. For example, the collaboration unit adjusts the timing for reservations and payments based on schedule data input by the user into a calendar app. The collaboration unit can also adjust the timing for reservations and payments based on schedule data manually input by the user. Furthermore, the collaboration unit can analyze the user's activity patterns to adjust the timing for reservations and payments based on the user's schedule. For example, the collaboration unit makes reservations and payments to avoid busy hours based on the user's activity patterns. This allows the collaboration unit to make payments and reservations at appropriate times based on the user's schedule.

[0086] The collaboration unit can estimate a user's emotions and prioritize payments and reservations based on the estimated user emotions. For example, if the user is tired, the collaboration unit provides a method for quickly completing payments and reservations. The collaboration unit can use facial expression recognition technology to estimate the user's emotions. For example, the collaboration unit can capture the user's facial expressions with a camera and estimate the emotion using a facial expression recognition algorithm. The collaboration unit can also estimate the user's emotions using voice analysis technology. For example, the collaboration unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the collaboration unit can prioritize payments and reservations based on the user's emotions. For example, if the user is tired, the collaboration unit can provide a method for quickly completing payments and reservations, and if the user is energetic, the collaboration unit can provide a payment and reservation method that includes detailed explanations. This allows the collaboration unit to provide a more appropriate method by prioritizing payments and reservations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The linking unit can select the optimal method for payment and reservation by taking into consideration the user's geographical location information. For example, the linking unit prioritizes providing reservation and payment methods for restaurants near the user's current location. The linking unit can use GPS data and Wi-Fi location information to collect the user's geographical location information. For example, the linking unit can provide reservation and payment methods for nearby restaurants based on the user's current location. The linking unit can also prioritize providing reservation and payment methods for restaurants in areas the user frequently visits. For example, the linking unit prioritizes providing reservation and payment methods for restaurants in areas the user frequently visits. Furthermore, if the user is traveling, the linking unit can prioritize providing reservation and payment methods for restaurants in the user's travel destination. For example, the linking unit provides reservation and payment methods for restaurants the user plans to visit at the user's travel destination. This allows the linking unit to provide an appropriate payment and reservation method based on the user's geographical location information.

[0088] The linking unit can analyze a user's social media activity and provide related information at the time of payment and reservation. For example, if a user posts "lunch" on social media, the linking unit provides restaurant reservation and payment methods related to the post. The linking unit can use the content of the post and the number of likes to collect the user's social media activity. For example, if a user posts "lunch" on social media, the linking unit provides restaurant reservation and payment methods related to the post. The linking unit can also provide reservation and payment methods for restaurants that the user follows. For example, the linking unit prioritizes providing reservation and payment methods for restaurants that the user follows. Furthermore, the linking unit can also provide reservation and payment methods for restaurants shared by the user's friends. For example, the linking unit provides related information based on restaurant information shared by the user's friends. This allows the linking unit to provide related payment and reservation methods based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, information provision unit, and linking 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 user input information using the camera 42 and microphone 38B of the smart device 14, and collects information such as the user's preferences, health status, and mood of the day using the control unit 46A. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and generates an optimal lunch menu. The information provision unit, for example, uses the output device 40 of the smart device 14 to provide business hours, congestion levels, and map information. The linking unit, realized, for example, by the specific processing unit 290 of the data processing device 12, handles payments and reservations. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, information provision unit, and linking 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 user input information using the camera 42 and microphone 238 of the smart glasses 214, and collects information such as the user's preferences, health condition, and mood of the day using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate an optimal lunch menu. The information provision unit provides business hours, congestion levels, and map information using, for example, the speaker 240 of the smart glasses 214. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and handles payments and reservations. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, information provision unit, and linking unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects user input information using the camera 42 and microphone 238 of the headset terminal 314, and collects information such as the user's preferences, health condition, and mood for the day using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate an optimal lunch menu. The information provision unit provides business hours, congestion levels, and map information using, for example, the display 343 of the headset terminal 314. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and handles payments and reservations. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, information provision unit, and linking 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 user input information using the camera 42 and microphone 238 of the robot 414, and collects information such as the user's preferences, health condition, and mood of the day using the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate an optimal lunch menu. The information provision unit provides business hours, congestion levels, and map information using, for example, the speaker 240 of the robot 414. The linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and handles payments and reservations.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The collection unit can identify the user's preferred ingredients and cooking methods based on the user's past meal history, and improve the accuracy of information collection based on this. For example, the collection unit can prioritize collecting ingredients and cooking methods that the user has frequently selected in the past, and suggest menus that suit the user's preferences. The collection unit can also analyze the user's intake trends of specific nutrients from the user's meal history and collect information that takes nutritional balance into consideration. Furthermore, the collection unit can collect information for suggesting menus according to seasons or events based on the user's meal history. This allows the collection unit to provide more personalized information by utilizing the user's past meal history.

[0091] The generation unit can estimate the user's emotions and adjust the method of suggesting menus based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can suggest a menu using ingredients that have a relaxing effect. If the user is happy, the generation unit can suggest a luxurious menu suitable for a special event. Furthermore, if the user is tired, the generation unit can suggest a menu that is easy to prepare. In this way, the generation unit can provide a more satisfying lunch experience by suggesting a menu that corresponds to the user's emotions.

[0092] The information providing unit can select the optimal information providing method by taking into consideration the user's current activity status. For example, if the user is exercising, priority can be given to providing information by voice. Also, if the user is in a meeting, adjustments can be made, such as reducing notifications. Furthermore, detailed information can be provided during times when the user is relaxing. In this way, the information providing unit can provide more appropriate information by selecting an information providing method according to the user's activity status.

[0093] The collaboration unit can estimate the user's emotions and adjust the payment and reservation methods based on the estimated user emotions. For example, if the user is nervous, a simple and quick payment method can be provided. If the user is relaxed, a payment method with detailed instructions can be provided. Furthermore, if the user is in a hurry, a method for completing the reservation and payment in the shortest time can be provided. In this way, the collaboration unit can provide a more comfortable lunch experience by providing payment and reservation methods according to the user's emotions.

[0094] The collection unit can analyze the user's social media activity and collect information related to topics that interest the user. For example, if a user posts "healthy lunch" on social media, the collection unit can collect healthy menu information related to that post. It can also collect trending menu information based on the posts of influencers the user follows. It can also collect related information based on restaurant information shared by the user's friends. This allows the collection unit to provide more relevant information based on the user's social media activity.

[0095] The generation unit can estimate the user's emotions and adjust the menu variations based on the estimated user emotions. For example, if the user is feeling adventurous, new or exotic dishes can be suggested. Alternatively, if the user is feeling conservative, familiar dishes can be suggested. Furthermore, if the user is health-conscious, low-calorie or nutritionally balanced menus can be suggested. In this way, the generation unit can provide a more satisfying lunch experience by offering menu variations according to the user's emotions.

[0096] The information providing unit can select the optimal information providing method by referring to the user's past usage history. For example, if the user has preferred voice information provision in the past, voice information provision can be prioritized. Also, if the user has preferred text information provision in the past, text information provision can be prioritized. Furthermore, data mining technology can be used to select the optimal information providing method from the user's past usage history. This allows the information providing unit to select a more appropriate information providing method based on the user's past usage history.

[0097] The collaboration unit can make reservations and payments at optimal times based on the user's current schedule. For example, if the user is in a meeting, the collaboration unit can make reservations and payments after the meeting ends. Alternatively, if the user is exercising, the collaboration unit can make reservations and payments after the exercise ends. Furthermore, the collaboration unit can analyze the user's activity patterns to adjust the timing of reservations and payments based on the user's schedule. This allows the collaboration unit to make reservations and payments at more appropriate times based on the user's schedule.

[0098] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is tired, information on relaxing menu items can be collected with priority. If the user is excited, information on new dishes or adventurous menu items can be collected with priority. Furthermore, if the user is health-conscious, information on low-calorie and nutritionally balanced menu items can be collected with priority. In this way, the collection unit can provide more appropriate information by determining the priority of information according to the user's emotions.

[0099] The generation unit can adjust the nutritional balance of the menu based on the user's health data. For example, if the user needs to consume a lot of a specific nutrient, the generation unit can suggest a menu that is rich in that nutrient. Also, if the user is on a diet, the generation unit can suggest a low-calorie menu. Furthermore, if the user has an allergy, the generation unit can suggest a menu that does not contain allergens. This allows the generation unit to provide healthier menus based on the user's health data.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects user input information. User input information includes text input, voice input, and image input. The collection unit can retrieve information from a database or collect information from sensors, and collect information such as the user's preferences, health status, and mood for the day. For example, information can be collected based on the user's past menu selections or survey results. Step 2: The generation unit analyzes the information collected by the collection unit and generates the optimal lunch menu. The generation unit analyzes the information using data mining and machine learning algorithms, and uses generation AI to generate the optimal lunch menu based on the user's preferences, health condition, and mood of the day. For example, if a user inputs "I want to eat Japanese food," the optimal Japanese food menu will be generated based on that information. Step 3: The information providing unit provides business hours, congestion levels, and map information based on the lunch menu generated by the generation unit. The information providing unit works in conjunction with GPS data and map apps to provide the information the user needs to enjoy lunch hassle-free. For example, it provides the business hours and congestion levels of restaurants the user plans to visit. Step 4: The linking unit makes the payment and reservation based on the information provided by the information providing unit. The linking unit provides payment methods such as credit card, electronic money, and cash, allowing the user to easily make lunch reservations and payments. For example, if the user inputs "I would like to make a reservation for 12 o'clock," the reservation can be made based on that information, and payment can also be made automatically.

[0102] 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.

[0103] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0149] In the 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.

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

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 user input information; a generation unit that analyzes the information collected by the collection unit and generates a lunch menu; an information providing unit that provides business hours, congestion levels, and map information based on the lunch menu generated by the generating unit; a linking unit that performs payment and reservations based on the information provided by the information providing unit; A system characterized by:

2. The collecting unit Collect information about the user's preferences, health status, and mood of the day 2. The system of claim 1.

3. The generation unit Analyze the collected information and generate a lunch menu 2. The system of claim 1.

4. The information providing unit Providing business hours, congestion status, and map information based on the generated lunch menu 2. The system of claim 1.

5. The linking unit is Make payments and reservations based on the information you provide 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

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

8. The collecting unit When collecting information, it filters it based on the user's current health status and dietary restrictions.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

10. The collecting unit When collecting information, prioritize collecting highly relevant information by taking into account the user's geographical location information.

2. The system of claim 1.

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