System

Abstract

A system according to an embodiment aims to provide real-time information and allow the user to make optimal decisions.SOLUTION: A system includes an information providing unit, a chat reception unit, and a generation unit. The information providing unit provides real-time information. The chat acceptance unit accepts a question. The generation unit generates an optimal answer based on the question received by the chat reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to quickly obtain real-time information and make optimal decisions.

[0005] The system according to the embodiment aims to provide real-time information to help users make optimal decisions. [Means for solving the problem]

[0006] A system according to an embodiment includes an information providing unit, a chat accepting unit, and a generating unit. The information providing unit provides real-time information. The chat accepting unit accepts questions. The generating unit generates an optimal answer based on the questions accepted by the chat accepting unit. [Effects of the Invention]

[0007] The system according to the embodiment provides real-time information to enable users to make optimal decisions. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The speed concierge system according to an embodiment of the present invention provides real-time information and uses a generation AI to generate optimal answers, allowing users to quickly and accurately obtain the information they need.

[0029] A speed concierge system according to an embodiment includes an information providing unit, a chat reception unit, and a generation unit. The information providing unit provides real-time information. For example, the information providing unit provides the inventory status of sale items. The information providing unit can also provide the reservation status of restaurants. The information providing unit can also provide the availability of parking spaces. For example, the information providing unit updates the inventory status of sale items in real time and provides it to a user. The latest information on restaurant reservation status is provided in cooperation with a reservation system. The latest information on parking space availability is provided in cooperation with a management system. The chat reception unit accepts questions via LINE. For example, the chat reception unit accepts questions sent by a user via LINE. The chat reception unit can also accept questions sent by a user via voice input. The chat reception unit can also accept questions sent by a user in text format. For example, when a user asks, "Do you have sale items in stock at the nearby supermarket?", the chat reception unit accepts the question. The generation unit generates an optimal answer based on the question accepted by the chat reception unit. For example, the generation unit uses the generation AI to analyze a user's question and generate an answer by obtaining the latest information from a related database. The generation unit can also use the generation AI to connect with a restaurant reservation system, obtain the latest reservation status, and generate an answer. The generation unit can also use the generation AI to connect with a parking lot management system, obtain the latest availability status, and generate an answer. For example, the generation unit uses the generation AI to analyze a question such as "Are there any sale items in stock at the nearby supermarket?" and generate an answer by obtaining the latest inventory information from a related database. This allows the speed concierge system according to the embodiment to quickly and accurately obtain the information a user needs. For example, the user can check the availability of sale items in real time, thereby avoiding unnecessary shopping. Checking the reservation status of a restaurant allows for a smooth dining out experience. Checking the availability of parking spaces can also reduce the stress of searching for a parking space.

[0030] The generation unit learns the user's past question history and can provide predictive information for the next question. For example, the generation unit uses a generation AI to analyze the user's past question history and provide predictive information for the next question. For example, if a user has frequently asked about the stock status of sale items in the past, the latest sale item information will be automatically provided the next time the user asks. The generation unit also develops an algorithm that allows the generation AI to learn the user's past question history and provide predictive information for the next question. For example, the generation AI predicts the information the user is likely to ask next based on the past question history and provides that information. This allows the generation AI to learn the user's past question history and provide predictive information for the next question.

[0031] The generation unit can acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation unit uses a generation AI to acquire the user's location information in real time and provide sale item inventory information at the nearest supermarket. For example, when a user asks, "Are sale items in stock at the nearby supermarket?", the generation AI provides inventory information at the nearest supermarket based on the user's location information. The generation unit also develops an algorithm that allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation AI collects data from the nearest information source based on the user's location information and provides that information. This allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source.

[0032] The generation unit can analyze the user's schedule and provide information at the optimal timing. For example, the generation AI analyzes the user's schedule and provides sale item inventory information at the optimal timing. For example, the generation unit notifies the user of the sale item inventory status the day before the user plans to go shopping. The generation unit also develops an algorithm that allows the generation AI to analyze the user's schedule and provide information at the optimal timing. For example, the generation AI provides information at the optimal timing based on the user's schedule. This makes it possible to analyze the user's schedule and provide information at the optimal timing.

[0033] The generation unit can integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI in the generation unit integrates inventory information from multiple supermarkets and provides it to the user in one lump sum. For example, when a user asks, "Are there any sale items in stock at the supermarket near me?", the generation AI integrates inventory information from multiple supermarkets and provides it. The generation unit also develops an algorithm that enables the generation AI to integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI integrates information from different data sources and provides that information to the user. This allows data from different information sources to be integrated and provided to the user in one lump sum.

[0034] The generation unit can analyze the user's chat history and predict and provide the optimal answer. For example, the generation AI analyzes the user's chat history and predicts and provides the optimal answer. For example, if a user has previously asked about the stock status of special sale items, the latest sale item information will be automatically provided the next time the user asks. The generation unit also develops an algorithm that allows the generation AI to analyze the user's chat history and predict and provide the optimal answer. For example, the generation AI predicts the information that is likely to be asked next based on the user's chat history and provides that information. This makes it possible to analyze the user's chat history and predict and provide the optimal answer.

[0035] The generation unit learns the user's language style and can realize more natural conversations. For example, if the user prefers casual language, the generation AI will provide answers that match that style. The generation unit also develops an algorithm that allows the generation AI to learn the user's language style and realize more natural conversations. For example, the generation AI will learn the user's past speech patterns and provide answers that match that style. In this way, the generation AI can learn the user's language style and realize more natural conversations.

[0036] The generation unit works with multiple chat platforms and can accept questions from any platform. For example, the generation AI works not only with LINE but also with other chat platforms (e.g., WhatsApp and Facebook Messenger) and can accept questions from any platform. The generation unit also develops an algorithm that allows the generation AI to work with multiple chat platforms and accept questions from any platform. For example, the generation AI integrates questions from different chat platforms and provides answers to those questions. This allows the system to work with multiple chat platforms and accept questions from any platform.

[0037] The generation unit updates the inventory data of sale items in real time and can provide the user with the latest information. For example, when a user asks, "Are there any sale items in stock at the nearby supermarket?", the generation AI provides the latest inventory information. The generation unit also develops an algorithm that enables the generation AI to update the inventory data of sale items in real time and provide the user with the latest information. For example, the generation AI works in conjunction with an inventory management system to obtain and provide the latest inventory information. This allows the inventory data of sale items to be updated in real time and the user to be provided with the latest information.

[0038] The generation unit can analyze the popularity of sale items and notify users before stocks run low. For example, the generation unit uses a generation AI to analyze the popularity of sale items and notify users before stocks run low. For example, the generation unit predicts popularity based on the purchase history of sale items and notifies users before stocks run low. The generation unit also develops an algorithm that uses the generation AI to analyze the popularity of sale items and notify users before stocks run low. For example, the generation AI analyzes sales data, predicts the popularity of sale items, and notifies users of that information. This makes it possible to analyze the popularity of sale items and notify users before stocks run low.

[0039] The generation unit can share sale item inventory information with other users and promote joint purchases. For example, the generation AI can share sale item inventory information with other users and promote joint purchases. For example, a user can share sale item inventory information with a friend and purchase the item together. The generation unit can also develop an algorithm that allows the generation AI to share sale item inventory information with other users and promote joint purchases. For example, the generation AI can build a platform for sharing inventory information, allowing users to make joint purchases. This allows sale item inventory information to be shared with other users and promote joint purchases.

[0040] The generation unit can propose stock information of sale items in combination with other related items. For example, the generation AI of the generation unit proposes stock information of sale items in combination with other related items. For example, when a user asks about the stock status of a sale item, the generation AI also provides stock information of related items. The generation unit also develops an algorithm that allows the generation AI to propose stock information of sale items in combination with other related items. For example, the generation AI proposes highly related items based on the user's past purchasing history. This makes it possible to propose stock information of sale items in combination with other related items.

[0041] The generation unit can link with a restaurant's reservation system in real time and provide the latest reservation status. For example, when a user asks, "Are there any restaurants that accept reservations at 7pm tonight?", the generation AI provides the latest reservation status. The generation unit also develops an algorithm that allows the generation AI to link with a restaurant's reservation system in real time and provide the latest reservation status. For example, the generation AI links with the reservation system to obtain and provide the latest reservation information. This allows the generation AI to link with a restaurant's reservation system in real time and provide the latest reservation status.

[0042] The generation unit can learn the user's past reservation history and prioritize suggesting favorite restaurants. For example, the generation unit uses a generation AI to learn the user's past reservation history and prioritize suggesting favorite restaurants. For example, the generation unit prioritizes providing reservation status for restaurants that the user has visited in the past. The generation unit also develops an algorithm that uses the generation AI to learn the user's past reservation history and prioritize suggesting favorite restaurants. For example, the generation AI identifies favorite restaurants based on the user's past reservation history and provides information about them. This allows the generation AI to learn the user's past reservation history and prioritize suggesting favorite restaurants.

[0043] The generation unit can share the reservation status of a restaurant with other users and promote group reservations. For example, the generation AI of the generation unit shares the reservation status of a restaurant with other users and promotes group reservations. For example, when a user makes a reservation at a restaurant with friends, the reservation status is shared. The generation unit also develops an algorithm that allows the generation AI to share the reservation status of a restaurant with other users and promote group reservations. For example, the generation AI builds a platform for sharing reservation status, allowing users to make reservations as a group. This allows the reservation status of a restaurant to be shared with other users and promotes group reservations.

[0044] The generation unit can propose restaurant reservation status in combination with other event information. For example, the generation AI of the generation unit proposes restaurant reservation status in combination with other event information. For example, when a user asks, "Are there any restaurants where I can make a reservation at 7pm tonight?", the generation AI will also provide information about nearby events. The generation unit also develops an algorithm that allows the generation AI to propose restaurant reservation status in combination with other event information. For example, the generation AI provides event information related to restaurant reservation status based on the user's interests. This makes it possible to propose restaurant reservation status in combination with other event information.

[0045] The generation unit can learn the user's parking history and suggest the most suitable parking lot. For example, the generation AI of the generation unit learns the user's parking history and suggests the most suitable parking lot. For example, it prioritizes providing information on the availability of parking lots that the user has used in the past. The generation unit also develops an algorithm that allows the generation AI to learn the user's parking history and suggest the most suitable parking lot. For example, the generation AI identifies the most suitable parking lot based on the user's past parking history and provides that information. This allows the generation AI to learn the user's parking history and suggest the most suitable parking lot.

[0046] The generation unit can share the availability of parking spaces with other users and promote joint use. For example, the generation AI of the generation unit shares the availability of parking spaces with other users and promotes joint use. For example, a user shares the availability of parking spaces with friends and uses them together. The generation unit also develops an algorithm that allows the generation AI to share the availability of parking spaces with other users and promotes joint use. For example, the generation AI builds a platform for sharing availability, allowing users to use it jointly. This allows the availability of parking spaces to be shared with other users and promotes joint use.

[0047] The generation unit can propose parking availability by combining it with other traffic information. For example, the generation AI of the generation unit proposes parking availability by combining it with other traffic information. For example, when a user asks, "Are there any parking spaces available nearby?", the generation AI also provides the nearby traffic information. The generation unit also develops an algorithm that allows the generation AI to propose parking availability by combining it with other traffic information. For example, the generation AI proposes the optimal parking lot based on the user's location information and traffic information. This makes it possible to propose parking availability by combining it with other traffic information.

[0048] The generation unit learns the user's past question history and can provide predictive information for the next question. For example, the generation unit uses a generation AI to analyze the user's past question history and provide predictive information for the next question. For example, if a user has frequently asked about the stock status of sale items in the past, the latest sale item information will be automatically provided the next time the user asks. The generation unit also develops an algorithm that allows the generation AI to learn the user's past question history and provide predictive information for the next question. For example, the generation AI predicts the information the user is likely to ask next based on the past question history and provides that information. This allows the generation AI to learn the user's past question history and provide predictive information for the next question.

[0049] The generation unit can acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation unit uses a generation AI to acquire the user's location information in real time and provide sale item inventory information at the nearest supermarket. For example, when a user asks, "Are sale items in stock at the nearby supermarket?", the generation AI provides inventory information at the nearest supermarket based on the user's location information. The generation unit also develops an algorithm that allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation AI collects data from the nearest information source based on the user's location information and provides that information. This allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source.

[0050] The generation unit can analyze the user's schedule and provide information at the optimal timing. For example, the generation AI analyzes the user's schedule and provides sale item inventory information at the optimal timing. For example, the generation unit notifies the user of the sale item inventory status the day before the user plans to go shopping. The generation unit also develops an algorithm that allows the generation AI to analyze the user's schedule and provide information at the optimal timing. For example, the generation AI provides information at the optimal timing based on the user's schedule. This makes it possible to analyze the user's schedule and provide information at the optimal timing.

[0051] The generation unit can integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI in the generation unit integrates inventory information from multiple supermarkets and provides it to the user in one lump sum. For example, when a user asks, "Are there any sale items in stock at the supermarket near me?", the generation AI integrates inventory information from multiple supermarkets and provides it. The generation unit also develops an algorithm that enables the generation AI to integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI integrates information from different data sources and provides that information to the user. This allows data from different information sources to be integrated and provided to the user in one lump sum.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The speed concierge system may further include a voice recognition unit. When a user inputs a question by voice, the voice recognition unit converts the voice into text. For example, when a user inputs a question by voice, such as "Are there any sale items in stock at the nearby supermarket?", the voice recognition unit converts the voice into text and sends it to the generation unit. The voice recognition unit may also have a function to remove background noise when a user inputs a question by voice. For example, even if a user inputs a question in a noisy place, the voice recognition unit removes the noise and converts the question into accurate text. This allows the user to obtain information more smoothly when inputting a question by voice.

[0054] The speed concierge system may further include a translation unit. When a user inputs a question in a different language, the translation unit translates the question into a language that the system can understand. For example, when a user asks in English, "Is there any stock of sale items at the nearby supermarket?", the translation unit translates the question into Japanese and sends it to the generation unit. The translation unit can also translate the answer generated by the generation unit into the user's language and provide it. For example, when the generation unit generates an answer in Japanese, the translation unit translates the answer into English and provides it to the user. This allows users who speak different languages ​​to smoothly obtain information.

[0055] The speed concierge system may further include an image recognition unit. When a user sends an image, the image recognition unit analyzes the image and provides related information. For example, when a user sends a photo of a sale item, the image recognition unit analyzes the photo and provides the stock status of the sale item. The image recognition unit may also recognize a specific object from the image sent by the user and provide information related to the object. For example, when a user sends a photo of a parking lot, the image recognition unit recognizes vacant parking spaces from the photo and provides information about them. This allows the user to obtain information through images.

[0056] The speed concierge system may further include a notification unit. The notification unit automatically notifies the user of specific information based on conditions set by the user. For example, if the user wants to periodically check the stock status of special sale items, the notification unit periodically notifies the user of that information. The notification unit may also notify the user when a specific event occurs based on conditions set by the user. For example, if the user wants to check the reservation status of a restaurant, the notification unit notifies the user as soon as a reservation becomes available. This allows the user to obtain the information they need in a timely manner.

[0057] The speed concierge system may further include a feedback unit. The feedback unit provides a function that allows users to provide feedback on the information provided. For example, a user may provide feedback such as "That was accurate" or "That was inaccurate" regarding the inventory information of a special sale item provided. The feedback unit may also collect user feedback and use it to improve the accuracy of the system. For example, the generation AI algorithm may be improved based on user feedback to provide more accurate information. This allows users to improve the quality of the information provided.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The information provider provides real-time information, such as the stock status of special sale items, restaurant reservations, and parking availability. This information is linked to various management systems to provide the latest information to users. Step 2: The chat reception unit accepts questions from LINE. For example, it accepts questions sent by users via LINE, voice input, or text. Step 3: The generator generates the optimal answer based on the question received by the chat reception unit. For example, the generator AI analyzes the user's question and generates an answer by retrieving the latest information from related databases and systems.

[0060] (Example 2) The speed concierge system according to an embodiment of the present invention provides real-time information and uses a generation AI to generate optimal answers, allowing users to quickly and accurately obtain the information they need.

[0061] A speed concierge system according to an embodiment includes an information providing unit, a chat reception unit, and a generation unit. The information providing unit provides real-time information. For example, the information providing unit provides the inventory status of sale items. The information providing unit can also provide the reservation status of restaurants. The information providing unit can also provide the availability of parking spaces. For example, the information providing unit updates the inventory status of sale items in real time and provides it to a user. The latest information on restaurant reservation status is provided in cooperation with a reservation system. The latest information on parking space availability is provided in cooperation with a management system. The chat reception unit accepts questions via LINE. For example, the chat reception unit accepts questions sent by a user via LINE. The chat reception unit can also accept questions sent by a user via voice input. The chat reception unit can also accept questions sent by a user in text format. For example, when a user asks, "Do you have sale items in stock at the nearby supermarket?", the chat reception unit accepts the question. The generation unit generates an optimal answer based on the question accepted by the chat reception unit. For example, the generation unit uses the generation AI to analyze a user's question and generate an answer by obtaining the latest information from a related database. The generation unit can also use the generation AI to connect with a restaurant reservation system, obtain the latest reservation status, and generate an answer. The generation unit can also use the generation AI to connect with a parking lot management system, obtain the latest availability status, and generate an answer. For example, the generation unit uses the generation AI to analyze a question such as "Are there any sale items in stock at the nearby supermarket?" and generate an answer by obtaining the latest inventory information from a related database. This allows the speed concierge system according to the embodiment to quickly and accurately obtain the information a user needs. For example, the user can check the availability of sale items in real time, thereby avoiding unnecessary shopping. Checking the reservation status of a restaurant allows for a smooth dining out experience. Checking the availability of parking spaces can also reduce the stress of searching for a parking space.

[0062] The generation unit learns the user's past question history and can provide predictive information for the next question. For example, the generation unit uses a generation AI to analyze the user's past question history and provide predictive information for the next question. For example, if a user has frequently asked about the stock status of sale items in the past, the latest sale item information will be automatically provided the next time the user asks. The generation unit also develops an algorithm that allows the generation AI to learn the user's past question history and provide predictive information for the next question. For example, the generation AI predicts the information the user is likely to ask next based on the past question history and provides that information. This allows the generation AI to learn the user's past question history and provide predictive information for the next question.

[0063] The generation unit can acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation unit uses a generation AI to acquire the user's location information in real time and provide sale item inventory information at the nearest supermarket. For example, when a user asks, "Are sale items in stock at the nearby supermarket?", the generation AI provides inventory information at the nearest supermarket based on the user's location information. The generation unit also develops an algorithm that allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation AI collects data from the nearest information source based on the user's location information and provides that information. This allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source.

[0064] The generation unit uses the emotion estimation function to provide information according to the user's emotional state, thereby reducing stress. For example, the generation AI analyzes the user's emotional state in real time and provides information to reduce stress. For example, if the user is feeling stressed, the generation AI provides information about restaurant reservations where the user can relax. The generation unit also develops an algorithm that uses the emotion estimation function to provide information according to the user's emotional state. For example, the generation AI provides information to reduce stress based on the user's emotional state. This provides information according to the user's emotional state, thereby reducing stress.

[0065] The generation unit can analyze the user's schedule and provide information at the optimal timing. For example, the generation AI analyzes the user's schedule and provides sale item inventory information at the optimal timing. For example, the generation unit notifies the user of the sale item inventory status the day before the user plans to go shopping. The generation unit also develops an algorithm that allows the generation AI to analyze the user's schedule and provide information at the optimal timing. For example, the generation AI provides information at the optimal timing based on the user's schedule. This makes it possible to analyze the user's schedule and provide information at the optimal timing.

[0066] The generation unit can integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI in the generation unit integrates inventory information from multiple supermarkets and provides it to the user in one lump sum. For example, when a user asks, "Are there any sale items in stock at the supermarket near me?", the generation AI integrates inventory information from multiple supermarkets and provides it. The generation unit also develops an algorithm that enables the generation AI to integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI integrates information from different data sources and provides that information to the user. This allows data from different information sources to be integrated and provided to the user in one lump sum.

[0067] The generation unit can use the emotion estimation function to prioritize providing information that the user is most interested in. For example, the generation unit can use the emotion estimation function to prioritize providing information about sale items that the user is most interested in. For example, if the user has positive emotions about sale items, the generation AI will prioritize providing information about the stock status of sale items. The generation unit also develops an algorithm that uses the emotion estimation function to prioritize providing information that the user is most interested in. For example, the generation AI will prioritize providing information that the user is most interested in based on the user's emotional state. This allows the generation unit to prioritize providing information that the user is most interested in.

[0068] The generation unit can analyze the user's chat history and predict and provide the optimal answer. For example, the generation AI analyzes the user's chat history and predicts and provides the optimal answer. For example, if a user has previously asked about the stock status of special sale items, the latest sale item information will be automatically provided the next time the user asks. The generation unit also develops an algorithm that allows the generation AI to analyze the user's chat history and predict and provide the optimal answer. For example, the generation AI predicts the information that is likely to be asked next based on the user's chat history and provides that information. This makes it possible to analyze the user's chat history and predict and provide the optimal answer.

[0069] The generation unit learns the user's language style and can realize more natural conversations. For example, if the user prefers casual language, the generation AI will provide answers that match that style. The generation unit also develops an algorithm that allows the generation AI to learn the user's language style and realize more natural conversations. For example, the generation AI will learn the user's past speech patterns and provide answers that match that style. In this way, the generation AI can learn the user's language style and realize more natural conversations.

[0070] The generation unit uses the emotion estimation function to conduct a dialogue that corresponds to the user's emotions, thereby improving the user experience. The generation unit, for example, uses the emotion estimation function to conduct a dialogue that corresponds to the user's emotions. For example, if the user is feeling stressed, the generation AI provides information that helps the user relax. The generation unit also develops an algorithm that uses the emotion estimation function to conduct a dialogue that corresponds to the user's emotions. For example, the generation AI conducts an appropriate dialogue based on the user's emotional state. This allows a dialogue that corresponds to the user's emotions to improve the user experience.

[0071] The generation unit works with multiple chat platforms and can accept questions from any platform. For example, the generation AI works not only with LINE but also with other chat platforms (e.g., WhatsApp and Facebook Messenger) and can accept questions from any platform. The generation unit also develops an algorithm that allows the generation AI to work with multiple chat platforms and accept questions from any platform. For example, the generation AI integrates questions from different chat platforms and provides answers to those questions. This allows the system to work with multiple chat platforms and accept questions from any platform.

[0072] The generation unit uses the emotion estimation function to provide feedback according to the user's emotions, thereby improving the quality of the dialogue. The generation unit, for example, uses the emotion estimation function to provide feedback according to the user's emotions. For example, if the user is feeling stressed, the generation AI provides information that helps the user relax. The generation unit also develops an algorithm that uses the emotion estimation function to provide feedback according to the user's emotions. For example, the generation AI provides appropriate feedback based on the user's emotional state. This allows the generation unit to provide feedback according to the user's emotions and improve the quality of the dialogue.

[0073] The generation unit updates the inventory data of sale items in real time and can provide the user with the latest information. For example, when a user asks, "Are there any sale items in stock at the nearby supermarket?", the generation AI provides the latest inventory information. The generation unit also develops an algorithm that enables the generation AI to update the inventory data of sale items in real time and provide the user with the latest information. For example, the generation AI works in conjunction with an inventory management system to obtain and provide the latest inventory information. This allows the inventory data of sale items to be updated in real time and the user to be provided with the latest information.

[0074] The generation unit can analyze the popularity of sale items and notify users before stocks run low. For example, the generation unit uses a generation AI to analyze the popularity of sale items and notify users before stocks run low. For example, the generation unit predicts popularity based on the purchase history of sale items and notifies users before stocks run low. The generation unit also develops an algorithm that uses the generation AI to analyze the popularity of sale items and notify users before stocks run low. For example, the generation AI analyzes sales data, predicts the popularity of sale items, and notifies users of that information. This makes it possible to analyze the popularity of sale items and notify users before stocks run low.

[0075] The generation unit uses the emotion estimation function to analyze the emotions the user has toward the sale items and make suggestions that will increase their desire to purchase. The generation unit, for example, uses the emotion estimation function to analyze the emotions the user has toward the sale items and make suggestions that will increase their desire to purchase. For example, if the user has positive emotions toward the sale items, the generation unit makes suggestions that will further increase those emotions. The generation unit also develops an algorithm in which the generation AI uses the emotion estimation function to analyze the emotions the user has toward the sale items and make suggestions that will increase their desire to purchase. For example, the generation AI makes suggestions that will increase their desire to purchase based on the user's emotional state. This makes it possible to analyze the emotions the user has toward the sale items and make suggestions that will increase their desire to purchase.

[0076] The generation unit can share sale item inventory information with other users and promote joint purchases. For example, the generation AI can share sale item inventory information with other users and promote joint purchases. For example, a user can share sale item inventory information with a friend and purchase the item together. The generation unit can also develop an algorithm that allows the generation AI to share sale item inventory information with other users and promote joint purchases. For example, the generation AI can build a platform for sharing inventory information, allowing users to make joint purchases. This allows sale item inventory information to be shared with other users and promote joint purchases.

[0077] The generation unit can propose stock information of sale items in combination with other related items. For example, the generation AI of the generation unit proposes stock information of sale items in combination with other related items. For example, when a user asks about the stock status of a sale item, the generation AI also provides stock information of related items. The generation unit also develops an algorithm that allows the generation AI to propose stock information of sale items in combination with other related items. For example, the generation AI proposes highly related items based on the user's past purchasing history. This makes it possible to propose stock information of sale items in combination with other related items.

[0078] The generation unit can use the emotion estimation function to suggest related sale items based on the emotion the user feels toward the sale item. The generation unit, for example, uses the emotion estimation function to suggest related sale items based on the emotion the user feels toward the sale item. For example, if the user has positive emotions toward the sale item, the generation unit suggests related sale items based on that emotion. The generation unit also develops an algorithm in which the generation AI uses the emotion estimation function to suggest related sale items based on the emotion the user feels toward the sale item. For example, the generation AI suggests highly relevant sale items based on the user's emotional state. This makes it possible to suggest related sale items based on the emotion the user feels toward the sale item.

[0079] The generation unit can link with a restaurant's reservation system in real time and provide the latest reservation status. For example, when a user asks, "Are there any restaurants that accept reservations at 7pm tonight?", the generation AI provides the latest reservation status. The generation unit also develops an algorithm that allows the generation AI to link with a restaurant's reservation system in real time and provide the latest reservation status. For example, the generation AI links with the reservation system to obtain and provide the latest reservation information. This allows the generation AI to link with a restaurant's reservation system in real time and provide the latest reservation status.

[0080] The generation unit can learn the user's past reservation history and prioritize suggesting favorite restaurants. For example, the generation unit uses a generation AI to learn the user's past reservation history and prioritize suggesting favorite restaurants. For example, the generation unit prioritizes providing reservation status for restaurants that the user has visited in the past. The generation unit also develops an algorithm that uses the generation AI to learn the user's past reservation history and prioritize suggesting favorite restaurants. For example, the generation AI identifies favorite restaurants based on the user's past reservation history and provides information about them. This allows the generation AI to learn the user's past reservation history and prioritize suggesting favorite restaurants.

[0081] The generation unit uses the emotion estimation function to suggest restaurants that match the user's emotions, thereby improving satisfaction. The generation unit, for example, uses the emotion estimation function to suggest restaurants that match the user's emotions. For example, if the user is feeling stressed, the generation unit suggests restaurants where the user can relax. The generation unit also develops an algorithm that uses the emotion estimation function to suggest restaurants that match the user's emotions. For example, the generation AI suggests appropriate restaurants based on the user's emotional state. This makes it possible to suggest restaurants that match the user's emotions and improve satisfaction.

[0082] The generation unit can share the reservation status of a restaurant with other users and promote group reservations. For example, the generation AI of the generation unit shares the reservation status of a restaurant with other users and promotes group reservations. For example, when a user makes a reservation at a restaurant with friends, the reservation status is shared. The generation unit also develops an algorithm that allows the generation AI to share the reservation status of a restaurant with other users and promote group reservations. For example, the generation AI builds a platform for sharing reservation status, allowing users to make reservations as a group. This allows the reservation status of a restaurant to be shared with other users and promotes group reservations.

[0083] The generation unit can propose restaurant reservation status in combination with other event information. For example, the generation AI of the generation unit proposes restaurant reservation status in combination with other event information. For example, when a user asks, "Are there any restaurants where I can make a reservation at 7pm tonight?", the generation AI will also provide information about nearby events. The generation unit also develops an algorithm that allows the generation AI to propose restaurant reservation status in combination with other event information. For example, the generation AI provides event information related to restaurant reservation status based on the user's interests. This makes it possible to propose restaurant reservation status in combination with other event information.

[0084] The generation unit can use the emotion estimation function to suggest special restaurant menus that correspond to the user's emotions. For example, the generation unit uses the emotion estimation function to suggest special restaurant menus that correspond to the user's emotions. For example, if the user is feeling stressed, the generation unit suggests a special menu that will help them relax. The generation unit also develops an algorithm that uses the emotion estimation function to suggest special restaurant menus that correspond to the user's emotions. For example, the generation AI suggests an appropriate special menu based on the user's emotional state. This makes it possible to suggest special restaurant menus that correspond to the user's emotions.

[0085] The generation unit can learn the user's parking history and suggest the most suitable parking lot. For example, the generation AI of the generation unit learns the user's parking history and suggests the most suitable parking lot. For example, it prioritizes providing information on the availability of parking lots that the user has used in the past. The generation unit also develops an algorithm that allows the generation AI to learn the user's parking history and suggest the most suitable parking lot. For example, the generation AI identifies the most suitable parking lot based on the user's past parking history and provides that information. This allows the generation AI to learn the user's parking history and suggest the most suitable parking lot.

[0086] The generation unit uses the emotion estimation function to suggest parking lots that correspond to the user's emotions, thereby reducing stress. The generation unit, for example, uses the emotion estimation function to suggest parking lots that correspond to the user's emotions. For example, if the user is feeling stressed, it will prioritize suggesting vacant parking lots. The generation unit also develops an algorithm in which the generation AI uses the emotion estimation function to suggest parking lots that correspond to the user's emotions. For example, the generation AI suggests parking lots that will reduce stress based on the user's emotional state. This allows the generation unit to suggest parking lots that correspond to the user's emotions, thereby reducing stress.

[0087] The generation unit can share the availability of parking spaces with other users and promote joint use. For example, the generation AI of the generation unit shares the availability of parking spaces with other users and promotes joint use. For example, a user shares the availability of parking spaces with friends and uses them together. The generation unit also develops an algorithm that allows the generation AI to share the availability of parking spaces with other users and promotes joint use. For example, the generation AI builds a platform for sharing availability, allowing users to use it jointly. This allows the availability of parking spaces to be shared with other users and promotes joint use.

[0088] The generation unit can propose parking availability by combining it with other traffic information. For example, the generation AI of the generation unit proposes parking availability by combining it with other traffic information. For example, when a user asks, "Are there any parking spaces available nearby?", the generation AI also provides the nearby traffic information. The generation unit also develops an algorithm that allows the generation AI to propose parking availability by combining it with other traffic information. For example, the generation AI proposes the optimal parking lot based on the user's location information and traffic information. This makes it possible to propose parking availability by combining it with other traffic information.

[0089] The generation unit can use the emotion estimation function to suggest special parking services that correspond to the user's emotions. For example, the generation unit uses the emotion estimation function to suggest special parking services that correspond to the user's emotions. For example, if the user is feeling stressed, the generation unit suggests special services that will help them relax. The generation unit also develops an algorithm that uses the emotion estimation function to suggest special parking services that correspond to the user's emotions. For example, the generation AI suggests appropriate special services based on the user's emotional state. This makes it possible to suggest special parking services that correspond to the user's emotions.

[0090] The generation unit learns the user's past question history and can provide predictive information for the next question. For example, the generation unit uses a generation AI to analyze the user's past question history and provide predictive information for the next question. For example, if a user has frequently asked about the stock status of sale items in the past, the latest sale item information will be automatically provided the next time the user asks. The generation unit also develops an algorithm that allows the generation AI to learn the user's past question history and provide predictive information for the next question. For example, the generation AI predicts the information the user is likely to ask next based on the past question history and provides that information. This allows the generation AI to learn the user's past question history and provide predictive information for the next question.

[0091] The generation unit can acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation unit uses a generation AI to acquire the user's location information in real time and provide sale item inventory information at the nearest supermarket. For example, when a user asks, "Are sale items in stock at the nearby supermarket?", the generation AI provides inventory information at the nearest supermarket based on the user's location information. The generation unit also develops an algorithm that allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source. For example, the generation AI collects data from the nearest information source based on the user's location information and provides that information. This allows the generation AI to acquire the user's location information in real time and collect and provide data from the nearest information source.

[0092] The generation unit uses the emotion estimation function to provide information according to the user's emotional state, thereby reducing stress. For example, the generation AI analyzes the user's emotional state in real time and provides information to reduce stress. For example, if the user is feeling stressed, the generation AI provides information about restaurant reservations where the user can relax. The generation unit also develops an algorithm that uses the emotion estimation function to provide information according to the user's emotional state. For example, the generation AI provides information to reduce stress based on the user's emotional state. This provides information according to the user's emotional state, thereby reducing stress.

[0093] The generation unit can analyze the user's schedule and provide information at the optimal timing. For example, the generation AI analyzes the user's schedule and provides sale item inventory information at the optimal timing. For example, the generation unit notifies the user of the sale item inventory status the day before the user plans to go shopping. The generation unit also develops an algorithm that allows the generation AI to analyze the user's schedule and provide information at the optimal timing. For example, the generation AI provides information at the optimal timing based on the user's schedule. This makes it possible to analyze the user's schedule and provide information at the optimal timing.

[0094] The generation unit can integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI in the generation unit integrates inventory information from multiple supermarkets and provides it to the user in one lump sum. For example, when a user asks, "Are there any sale items in stock at the supermarket near me?", the generation AI integrates inventory information from multiple supermarkets and provides it. The generation unit also develops an algorithm that enables the generation AI to integrate data from different information sources and provide it to the user in one lump sum. For example, the generation AI integrates information from different data sources and provides that information to the user. This allows data from different information sources to be integrated and provided to the user in one lump sum.

[0095] The generation unit can use the emotion estimation function to prioritize providing information that the user is most interested in. For example, the generation unit can use the emotion estimation function to prioritize providing information about sale items that the user is most interested in. For example, if the user has positive emotions about sale items, the generation AI will prioritize providing information about the stock status of sale items. The generation unit also develops an algorithm that uses the emotion estimation function to prioritize providing information that the user is most interested in. For example, the generation AI will prioritize providing information that the user is most interested in based on the user's emotional state. This allows the generation unit to prioritize providing information that the user is most interested in.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The speed concierge system may further include a voice recognition unit. When a user inputs a question by voice, the voice recognition unit converts the voice into text. For example, when a user inputs a question by voice, such as "Are there any sale items in stock at the nearby supermarket?", the voice recognition unit converts the voice into text and sends it to the generation unit. The voice recognition unit may also have a function to remove background noise when a user inputs a question by voice. For example, even if a user inputs a question in a noisy place, the voice recognition unit removes the noise and converts the question into accurate text. This allows the user to obtain information more smoothly when inputting a question by voice.

[0098] The speed concierge system may further include a translation unit. When a user inputs a question in a different language, the translation unit translates the question into a language that the system can understand. For example, when a user asks in English, "Is there any stock of sale items at the nearby supermarket?", the translation unit translates the question into Japanese and sends it to the generation unit. The translation unit can also translate the answer generated by the generation unit into the user's language and provide it. For example, when the generation unit generates an answer in Japanese, the translation unit translates the answer into English and provides it to the user. This allows users who speak different languages ​​to smoothly obtain information.

[0099] The speed concierge system may further include an image recognition unit. When a user sends an image, the image recognition unit analyzes the image and provides related information. For example, when a user sends a photo of a sale item, the image recognition unit analyzes the photo and provides the stock status of the sale item. The image recognition unit may also recognize a specific object from the image sent by the user and provide information related to the object. For example, when a user sends a photo of a parking lot, the image recognition unit recognizes vacant parking spaces from the photo and provides information about them. This allows the user to obtain information through images.

[0100] The speed concierge system may further include a notification unit. The notification unit automatically notifies the user of specific information based on conditions set by the user. For example, if the user wants to periodically check the stock status of special sale items, the notification unit periodically notifies the user of that information. The notification unit may also notify the user when a specific event occurs based on conditions set by the user. For example, if the user wants to check the reservation status of a restaurant, the notification unit notifies the user as soon as a reservation becomes available. This allows the user to obtain the information they need in a timely manner.

[0101] The speed concierge system may further include a feedback unit. The feedback unit provides a function that allows users to provide feedback on the information provided. For example, a user may provide feedback such as "That was accurate" or "That was inaccurate" regarding the inventory information of a special sale item provided. The feedback unit may also collect user feedback and use it to improve the accuracy of the system. For example, the generation AI algorithm may be improved based on user feedback to provide more accurate information. This allows users to improve the quality of the information provided.

[0102] The Speed ​​Concierge System can also use its emotion estimation function to provide entertainment information that corresponds to the user's emotions. For example, if the user is feeling stressed, the generation AI can provide information about relaxing movies and music. Also, if the user is feeling positive, the emotion estimation function can be used to provide event information to further boost the user's mood. For example, if the user is having fun, the generation AI can provide information about nearby concerts and festivals. This provides entertainment information that corresponds to the user's emotions, improving the user experience.

[0103] The Speed ​​Concierge System can also use its emotion estimation function to provide health information that corresponds to the user's emotions. For example, if the user is feeling stressed, the generation AI can provide information on relaxing yoga or meditation. Also, using the emotion estimation function, if the user is tired, the generation AI can provide information on refreshing spas and massages. For example, if the user is feeling fatigued, the generation AI can provide information on nearby spas and massage parlors. This allows the system to provide health information that corresponds to the user's emotions and support the user's health.

[0104] The Speed ​​Concierge System can also use its emotion estimation function to provide travel information that corresponds to the user's emotions. For example, if the user is feeling stressed, the generation AI can provide information on relaxing travel destinations. Also, if the user is feeling adventurous, the generation AI can use the emotion estimation function to provide information on exciting activities. For example, if the user is seeking adventure, the generation AI can provide information on skydiving and rafting. This allows the system to provide travel information that corresponds to the user's emotions, improving the user's travel experience.

[0105] The Speed ​​Concierge System can also use its emotion estimation function to provide shopping information that corresponds to the user's emotions. For example, if the user is feeling stressed, the generation AI can provide information on shopping spots where the user can relax. Also, if the user is having fun, the emotion estimation function can be used to provide information on shopping events to further boost the user's mood. For example, if the user is having fun, the generation AI can provide information on sales and fairs being held nearby. This allows the system to provide shopping information that corresponds to the user's emotions, improving the user's shopping experience.

[0106] The Speed ​​Concierge System can also use its emotion estimation function to provide learning information that corresponds to the user's emotions. For example, if the user is feeling stressed, the generation AI can provide information on learning methods and learning materials that will help them relax. Furthermore, using the emotion estimation function, if the user is concentrating, the generation AI can provide information to improve the user's learning efficiency. For example, if the user is concentrating, the generation AI can provide information on efficient learning schedules and tools. This allows the system to provide learning information that corresponds to the user's emotions, improving the user's learning experience.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The information provider provides real-time information, such as the stock status of special sale items, restaurant reservations, and parking availability. This information is linked to various management systems to provide the latest information to users. Step 2: The chat reception unit accepts questions from LINE. For example, it accepts questions sent by users via LINE, voice input, or text. Step 3: The generator generates the optimal answer based on the question received by the chat reception unit. For example, the generator AI analyzes the user's question and generates an answer by retrieving the latest information from related databases and systems.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0157] 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.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information providing unit that provides real-time information; A chat reception section for accepting questions, a generation unit that generates an optimal answer based on the question accepted by the chat acceptance unit. A system characterized by:

2. The generation unit Get your location in real time and provide data from the nearest source 2. The system of claim 1.

3. The generation unit Integrate data from different sources and provide it to users in one place 2. The system of claim 1.

4. The generation unit Real-time updates of special sale inventory data provide users with the latest information 2. The system of claim 1.

5. The generation unit Links with restaurant reservation systems in real time to provide users with the latest reservation status 2. The system of claim 1.

6. The generation unit Proposing parking spaces based on the user's emotions to reduce stress 2. The system of claim 1.

Images