system

Abstract

The system according to the embodiment aims to enable users to make delivery and store reservations without hassle. [Solution] A system according to an embodiment includes a reception unit, an acquisition unit, a proposal unit, and an agent unit. The reception unit accepts voice input. The acquisition unit acquires location information based on the information accepted by the reception unit. The proposal unit makes proposals based on the location information acquired by the acquisition unit. The agent unit makes arrangements, reservations, and payments on behalf of the user based on the content proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, customers had to search for delivery options and store reservations themselves, which was a time-consuming process.

[0005] The system according to the embodiment aims to enable users to make delivery and store reservations without hassle. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a proposal unit, and an agent unit. The reception unit accepts voice input. The acquisition unit acquires location information based on the information accepted by the reception unit. The proposal unit makes proposals based on the location information acquired by the acquisition unit. The agent unit makes arrangements, reservations, and payments based on the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to make delivery and store reservations without any hassle. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system designed to reduce the hassle of delivery and restaurant reservations. In this system, a user requests a dish they want to eat by voice input, and the system uses the user's location information to make suggestions for nearby delivery arrangements and restaurant reservations. These suggestions are customized based on factors such as time and price. If the user accepts the suggestion, the system handles the arrangements, reservations, and payment on their behalf. This eliminates the hassle of delivery and restaurant reservations, allowing the user to easily arrange meals. For example, a user requests a dish they want to eat by voice input. The system searches for nearby delivery services and restaurants based on the user's location information and makes optimal suggestions. The suggestions are customized based on factors such as time and price. If the user accepts the suggestion, the system handles the arrangements, reservations, and payment on their behalf. This allows the user to enjoy a meal without any hassle.

[0029] A delivery and restaurant reservation system according to an embodiment includes a reception unit, an acquisition unit, a proposal unit, and an agent unit. The reception unit accepts a user's desired food via voice input. For example, the user may voice input a request such as "I want pizza." The acquisition unit acquires the user's location information based on the information accepted by the reception unit. For example, the location information may be acquired using GPS, Wi-Fi location information, cell tower location information, etc. The proposal unit makes proposals for nearby delivery services and restaurant reservations based on the location information acquired by the acquisition unit. For example, if a user requests "I want pizza," the system proposes nearby pizza delivery services and reservations at pizza restaurants. The proposal unit customizes the proposal content based on factors such as time and price. For example, if the user specifies conditions such as "delivery within 30 minutes" or "budget within 2,000 yen," the system makes proposals accordingly. The agent unit handles arrangements, reservations, and payments based on the proposal content. For example, if a user accepts a proposed pizza delivery, the system automatically arranges the delivery and handles the payment. This allows the delivery and restaurant reservation system according to the embodiment to save the user time and effort and make meal arrangements easily.

[0030] The reception unit can receive the user's desired food by voice input. For example, the reception unit receives a user's voice input request such as "I want pizza." Voice input can be performed using voice recognition technology or a type of microphone. For example, the voice recognition technology can be used to accurately recognize the user's request. The reception unit can also use a microphone with a noise canceling function. This allows the user to intuitively request the food they want to eat.

[0031] The acquisition unit can acquire user location information based on the information accepted by the acceptance unit. The acquisition unit acquires user location information using, for example, GPS. For example, the acquisition unit acquires location information using the GPS function of the user's smartphone. The acquisition unit can also acquire location information using Wi-Fi location information. For example, the user's location is identified based on Wi-Fi access point location information. The acquisition unit can also acquire location information using cell tower location information. For example, the user's location is identified based on cell tower signal strength. This makes it possible to accurately acquire user location information and make optimal suggestions.

[0032] The suggestion unit can make suggestions for nearby delivery arrangements and restaurant reservations based on the location information acquired by the acquisition unit. For example, if a user requests "I want pizza," the suggestion unit can suggest a nearby pizza delivery service or a reservation at a pizza restaurant. The suggestion unit references a database to make optimal suggestions based on the user's location information. For example, the suggestion unit searches for information on delivery services and stores registered in the database and makes optimal suggestions based on the user's location information. The suggestion unit can also make more accurate suggestions by referencing the user's past request history. For example, optimal suggestions are made based on information on dishes and stores requested by the user in the past. This allows optimal suggestions to be made based on the user's current location.

[0033] The suggestion unit can customize the content of suggestions in terms of time or price. For example, when the user specifies conditions such as "delivery within 30 minutes" or "budget within 2,000 yen," the system makes suggestions accordingly. The suggestion unit searches the database based on the conditions specified by the user and makes the optimal suggestion. For example, the suggestion unit searches information about delivery services and stores registered in the database and makes the optimal suggestion based on the time and price conditions specified by the user. The suggestion unit can also make more accurate suggestions by referring to the user's past request history. For example, the suggestion unit makes the optimal suggestion based on information about dishes and stores requested by the user in the past. This allows the system to provide the optimal suggestion tailored to the user's needs.

[0034] The agent unit can handle arrangements, reservations, and payments based on the content proposed by the proposal unit. For example, when a user accepts a proposed pizza delivery, the system automatically arranges the delivery and also handles the payment. The agent unit makes the payment using an online payment system. For example, the agent unit makes the payment using credit card information. The agent unit can also make reservations over the phone. For example, the agent unit makes a reservation by calling a store on behalf of the user. The agent unit can also make arrangements within the app. For example, the agent unit makes arrangements using a delivery service app. This allows the user to enjoy a meal without any hassle.

[0035] The reception unit can improve the accuracy of voice input by referring to the user's past request history. For example, the reception unit can automatically display dishes that the user has previously requested as candidates. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has previously used. The reception unit can also predict and suggest dishes that the user will request during a specific time period based on the user's past request history. In this way, the accuracy of input can be improved by referring to the past request history.

[0036] The reception unit can adjust the speech recognition algorithm according to the user's speaking speed and accent when inputting speech. For example, if the user speaks quickly, the reception unit speeds up the speech recognition algorithm to accurately recognize the input. Also, if the user speaks slowly, the reception unit can adjust the speech recognition algorithm to accurately recognize the input. Also, the reception unit can customize the speech recognition algorithm according to the user's accent to improve the accuracy of the input. This enables speech recognition according to the user's speaking speed and accent.

[0037] The reception unit can filter the user's current environmental sounds to remove noise when inputting voice. For example, when the user is in a noisy place, the reception unit can filter the environmental sounds to improve the accuracy of the voice input. Furthermore, when the user is in a quiet place, the reception unit can minimize the environmental sounds to improve the accuracy of the voice input. Furthermore, when the user is inputting voice while moving, the reception unit can filter wind noise and vehicle noise to improve the accuracy of the voice input. In this way, filtering the environmental sounds improves the accuracy of the voice input.

[0038] The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information when inputting voice. For example, when the user is in a specific area, the reception unit can prioritize receiving requests related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving requests related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving requests around the user's home. In this way, highly relevant requests can be prioritized by taking into account the geographical location information.

[0039] The reception unit can analyze the user's social media activity and accept related requests when voice input is performed. For example, the reception unit accepts requests related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and accept related requests. The reception unit can also accept related requests by referring to the activities of the user's friends on social media. In this way, related requests can be accepted by analyzing social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when inputting voice. The reception unit customizes the voice input interface, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal voice input method. In this way, the reception method is customized by reflecting the past feedback.

[0041] When acquiring location information, the acquisition unit can select the optimal acquisition method by referring to the user's past movement history. The acquisition unit acquires optimal location information, for example, based on places the user has visited in the past. The acquisition unit can also acquire location information for avoiding congestion from the user's past movement history. The acquisition unit can also analyze the user's past movement history and acquire the most efficient location information. This makes it possible to acquire optimal location information by referring to the past movement history.

[0042] The acquisition unit can perform filtering based on the user's current activity status when acquiring location information. For example, when the user is on the move, the acquisition unit prioritizes acquiring current location information. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring location information around the user's home. Furthermore, when the user is participating in a specific event, the acquisition unit can also prioritize acquiring location information related to the event. This makes it possible to filter location information based on the user's current activity status.

[0043] When acquiring location information, the acquisition unit can select the optimal acquisition method depending on the user's device information. For example, if the user is using a smartphone, the acquisition unit acquires location information using GPS. If the user is using a tablet, the acquisition unit can also acquire location information using Wi-Fi. If the user is using a smartwatch, the acquisition unit can also acquire location information using Bluetooth (registered trademark). This makes it possible to acquire optimal location information depending on the device information.

[0044] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring location information related to that area. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring location information related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring location information around the user's home. In this way, highly relevant information is prioritized by taking into account the geographical location information.

[0045] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit acquires location information related to a location where the user checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related location information. The acquisition unit can also acquire related location information by referring to the activities of the user's friends on social media. In this way, related information is acquired by analyzing social media activity.

[0046] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. The acquisition unit customizes the location information acquisition method based on, for example, feedback provided by the user in the past. The acquisition unit can also preferentially suggest a specific acquisition method based on the user's past feedback. The acquisition unit can also analyze the user's past feedback and suggest the optimal location information acquisition method. In this way, the acquisition method is customized by reflecting the past feedback.

[0047] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past selection history. The suggestion unit makes optimal suggestions based on, for example, dishes selected by the user in the past. The suggestion unit can also make suggestions to avoid crowds based on the user's past selection history. The suggestion unit can also analyze the user's past selection history and make the most efficient suggestion. In this way, by referring to the past selection history, the accuracy of the suggestion is improved.

[0048] When making a suggestion, the suggestion unit can customize the suggestion content based on the user's current activity status. For example, when the user is on the move, the suggestion unit makes a suggestion based on current location information. Furthermore, when the user is at home, the suggestion unit can also make suggestions around the user's home. Furthermore, when the user is participating in a specific event, the suggestion unit can also make suggestions related to the event. In this way, the suggestion content is customized based on the user's current activity status.

[0049] When making a proposal, the proposal unit can adjust the level of detail of the proposal according to the user's budget and time constraints. For example, when the user specifies a budget, the proposal unit makes an optimal proposal within that budget. Furthermore, when the user specifies a time constraint, the proposal unit can also make an optimal proposal within that time. Furthermore, when the user specifies a budget and a time constraint, the proposal unit can also make an optimal proposal based on those conditions. In this way, an optimal proposal according to the budget and time constraints is provided.

[0050] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when the user is in a specific area, the suggestion unit can prioritize suggestions related to that area. Furthermore, when the user is traveling, the suggestion unit can also prioritize suggestions related to the travel destination. Furthermore, when the user is at home, the suggestion unit can also prioritize suggestions around the user's home. In this way, highly relevant suggestions are prioritized by taking into account the geographical location information.

[0051] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a related suggestion. For example, the suggestion unit can make a suggestion related to a place where the user has checked in on social media. The suggestion unit can also analyze the content of the user's posts on social media and make a related suggestion. The suggestion unit can also make a related suggestion by referring to the activities of the user's friends on social media. In this way, related suggestions are made by analyzing social media activity.

[0052] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion interface based on feedback provided by the user in the past. The suggestion unit can also preferentially suggest a specific suggestion method based on the user's past feedback. The suggestion unit can also analyze the user's past feedback and suggest an optimal suggestion method. In this way, the suggestion method is customized by reflecting the past feedback.

[0053] When acting as an agent, the agent unit can select the optimal agent method by referring to the user's past ordering history. The agent unit selects the optimal agent method, for example, based on the ordering methods used by the user in the past. The agent unit can also select an agent method that avoids congestion from the user's past ordering history. The agent unit can also analyze the user's past ordering history and select the most efficient agent method. In this way, the optimal agent method is selected by referring to the past ordering history.

[0054] The proxy unit can customize the proxy content based on the user's current activity status when proxying. For example, when the user is on the move, the proxy unit performs proxying based on current location information. Furthermore, when the user is at home, the proxy unit can also perform proxying around the user's home. Furthermore, when the user is participating in a specific event, the proxy unit can also perform proxying related to the event. In this way, the proxy content is customized based on the user's current activity status.

[0055] The proxy unit can improve the proxy method by reflecting user feedback during proxy operation. For example, the proxy unit can improve the proxy interface based on feedback previously provided by the user. The proxy unit can also preferentially suggest a specific proxy method based on the user's past feedback. The proxy unit can also analyze the user's past feedback and suggest the optimal proxy method. In this way, the proxy method is improved by reflecting the feedback.

[0056] When performing a proxy operation, the proxy unit can select the optimal proxy method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the proxy unit selects a proxy method related to that area. Furthermore, if the user is traveling, the proxy unit can also select a proxy method related to the user's travel destination. Furthermore, if the user is at home, the proxy unit can also select a proxy method near the user's home. In this way, the optimal proxy method is selected by taking into consideration the geographical location information.

[0057] The proxy unit can analyze the user's social media activity and suggest proxy means when proxying. For example, the proxy unit can suggest proxy methods related to places where the user has checked in on social media. The proxy unit can also analyze the content of the user's social media posts and suggest related proxy methods. The proxy unit can also suggest related proxy methods with reference to the activities of the user's friends on social media. In this way, the optimal proxy means is suggested by analyzing social media activity.

[0058] The proxy unit can customize the proxy method by reflecting the user's past feedback when proxying. The proxy unit customizes the proxy interface, for example, based on feedback provided by the user in the past. The proxy unit can also preferentially suggest a specific proxy method based on the user's past feedback. The proxy unit can also analyze the user's past feedback and suggest the optimal proxy method. In this way, the proxy method is customized by reflecting the past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When accepting a user's voice input, the reception unit can improve the accuracy of the request by referring to the user's past meal history. For example, dishes that the user has frequently ordered in the past can be displayed as candidates with priority. The reception unit can also predict and suggest dishes that the user will request during a specific time period based on the user's past request history. Furthermore, the reception unit can analyze the user's preference for dishes based on the user's past request history and improve the accuracy of the request. This allows the user to make requests more smoothly by utilizing their past history.

[0061] When acquiring the user's location information, the acquisition unit can select the optimal acquisition method taking into consideration the user's current activity status. For example, if the user is on the move, the acquisition unit can prioritize acquiring current location information. Also, if the user is at home, the acquisition unit can prioritize acquiring location information around the user's home. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring location information related to the event. This makes it possible to acquire location information based on the user's current activity status.

[0062] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past selection history. For example, the suggestion unit makes the optimal suggestion based on dishes selected by the user in the past. The suggestion unit can also make suggestions to avoid crowds based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and make the most efficient suggestion. In this way, by referring to the past selection history, the accuracy of the suggestion is improved.

[0063] When acting as an agent, the agent unit can select the optimal agent method by referring to the user's past ordering history. For example, the agent unit selects the optimal agent method based on the ordering methods used by the user in the past. The agent unit can also select an agent method that avoids congestion based on the user's past ordering history. Furthermore, the agent unit can analyze the user's past ordering history and select the most efficient agent method. In this way, the optimal agent method is selected by referring to the past ordering history.

[0064] When acquiring location information, the acquisition unit can select the optimal acquisition method by referring to the user's past movement history. For example, the optimal location information is acquired based on places the user has visited in the past. The acquisition unit can also acquire location information for avoiding congestion from the user's past movement history. Furthermore, the acquisition unit can analyze the user's past movement history and acquire the most efficient location information. This makes it possible to acquire optimal location information by referring to the past movement history.

[0065] When performing a proxy operation, the proxy unit can select the optimal proxy method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the proxy unit selects a proxy method related to that area. Also, if the user is traveling, the proxy unit can select a proxy method related to the user's travel destination. Furthermore, if the user is at home, the proxy unit can select a proxy method near the user's home. In this way, the optimal proxy method is selected by taking into consideration the geographical location information.

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

[0067] Step 1: The reception unit receives a voice request from the user regarding the food they want to eat. For example, the user may input a request such as "I want pizza." Step 2: The acquisition unit acquires the user's location information based on the information received by the reception unit. For example, the location information is acquired using GPS, Wi-Fi location information, cell tower location information, etc. Step 3: The suggestion unit makes suggestions for nearby delivery arrangements and restaurant reservations based on the location information acquired by the acquisition unit. For example, if a user requests "I want pizza," the system will suggest nearby pizza delivery services and reservations at pizza restaurants. The suggestion unit also customizes the suggestions based on factors such as time and price. For example, if a user specifies conditions such as "I want delivery within 30 minutes" or "My budget is within 2,000 yen," the system will make suggestions accordingly. Step 4: The agent handles arrangements, reservations, and payments based on the proposals made by the proposal unit. For example, if the user accepts the proposed pizza delivery, the system automatically arranges delivery and handles payment.

[0068] (Example 2) A system according to an embodiment of the present invention is a system designed to reduce the hassle of delivery and restaurant reservations. In this system, a user requests a dish they want to eat by voice input, and the system uses the user's location information to make suggestions for nearby delivery arrangements and restaurant reservations. These suggestions are customized based on factors such as time and price. If the user accepts the suggestion, the system handles the arrangements, reservations, and payment on their behalf. This eliminates the hassle of delivery and restaurant reservations, allowing the user to easily arrange meals. For example, a user requests a dish they want to eat by voice input. The system searches for nearby delivery services and restaurants based on the user's location information and makes optimal suggestions. The suggestions are customized based on factors such as time and price. If the user accepts the suggestion, the system handles the arrangements, reservations, and payment on their behalf. This allows the user to enjoy a meal without any hassle.

[0069] A delivery and restaurant reservation system according to an embodiment includes a reception unit, an acquisition unit, a proposal unit, and an agent unit. The reception unit accepts a user's desired food via voice input. For example, the user may voice input a request such as "I want pizza." The acquisition unit acquires the user's location information based on the information accepted by the reception unit. For example, the location information may be acquired using GPS, Wi-Fi location information, cell tower location information, etc. The proposal unit makes proposals for nearby delivery services and restaurant reservations based on the location information acquired by the acquisition unit. For example, if a user requests "I want pizza," the system proposes nearby pizza delivery services and reservations at pizza restaurants. The proposal unit customizes the proposal content based on factors such as time and price. For example, if the user specifies conditions such as "delivery within 30 minutes" or "budget within 2,000 yen," the system makes proposals accordingly. The agent unit handles arrangements, reservations, and payments based on the proposal content. For example, if a user accepts a proposed pizza delivery, the system automatically arranges the delivery and handles the payment. This allows the delivery and restaurant reservation system according to the embodiment to save the user time and effort and make meal arrangements easily.

[0070] The reception unit can receive the user's desired food by voice input. For example, the reception unit receives a user's voice input request such as "I want pizza." Voice input can be performed using voice recognition technology or a type of microphone. For example, the voice recognition technology can be used to accurately recognize the user's request. The reception unit can also use a microphone with a noise canceling function. This allows the user to intuitively request the food they want to eat.

[0071] The acquisition unit can acquire user location information based on the information accepted by the acceptance unit. The acquisition unit acquires user location information using, for example, GPS. For example, the acquisition unit acquires location information using the GPS function of the user's smartphone. The acquisition unit can also acquire location information using Wi-Fi location information. For example, the user's location is identified based on Wi-Fi access point location information. The acquisition unit can also acquire location information using cell tower location information. For example, the user's location is identified based on cell tower signal strength. This makes it possible to accurately acquire user location information and make optimal suggestions.

[0072] The suggestion unit can make suggestions for nearby delivery arrangements and restaurant reservations based on the location information acquired by the acquisition unit. For example, if a user requests "I want pizza," the suggestion unit can suggest a nearby pizza delivery service or a reservation at a pizza restaurant. The suggestion unit references a database to make optimal suggestions based on the user's location information. For example, the suggestion unit searches for information on delivery services and stores registered in the database and makes optimal suggestions based on the user's location information. The suggestion unit can also make more accurate suggestions by referencing the user's past request history. For example, optimal suggestions are made based on information on dishes and stores requested by the user in the past. This allows optimal suggestions to be made based on the user's current location.

[0073] The suggestion unit can customize the content of suggestions in terms of time or price. For example, when the user specifies conditions such as "delivery within 30 minutes" or "budget within 2,000 yen," the system makes suggestions accordingly. The suggestion unit searches the database based on the conditions specified by the user and makes the optimal suggestion. For example, the suggestion unit searches information about delivery services and stores registered in the database and makes the optimal suggestion based on the time and price conditions specified by the user. The suggestion unit can also make more accurate suggestions by referring to the user's past request history. For example, the suggestion unit makes the optimal suggestion based on information about dishes and stores requested by the user in the past. This allows the system to provide the optimal suggestion tailored to the user's needs.

[0074] The agent unit can handle arrangements, reservations, and payments based on the content proposed by the proposal unit. For example, when a user accepts a proposed pizza delivery, the system automatically arranges the delivery and also handles the payment. The agent unit makes the payment using an online payment system. For example, the agent unit makes the payment using credit card information. The agent unit can also make reservations over the phone. For example, the agent unit makes a reservation by calling a store on behalf of the user. The agent unit can also make arrangements within the app. For example, the agent unit makes arrangements using a delivery service app. This allows the user to enjoy a meal without any hassle.

[0075] The reception unit can estimate the user's emotions and adjust the voice input reception method based on the emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input the food they want to eat. This provides an optimal voice input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can improve the accuracy of voice input by referring to the user's past request history. For example, the reception unit can automatically display dishes that the user has previously requested as candidates. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has previously used. The reception unit can also predict and suggest dishes that the user will request during a specific time period based on the user's past request history. In this way, the accuracy of input can be improved by referring to the past request history.

[0077] The reception unit can adjust the speech recognition algorithm according to the user's speaking speed and accent when inputting speech. For example, if the user speaks quickly, the reception unit speeds up the speech recognition algorithm to accurately recognize the input. Also, if the user speaks slowly, the reception unit can adjust the speech recognition algorithm to accurately recognize the input. Also, the reception unit can customize the speech recognition algorithm according to the user's accent to improve the accuracy of the input. This enables speech recognition according to the user's speaking speed and accent.

[0078] The reception unit can filter the user's current environmental sounds to remove noise when inputting voice. For example, when the user is in a noisy place, the reception unit can filter the environmental sounds to improve the accuracy of the voice input. Furthermore, when the user is in a quiet place, the reception unit can minimize the environmental sounds to improve the accuracy of the voice input. Furthermore, when the user is inputting voice while moving, the reception unit can filter wind noise and vehicle noise to improve the accuracy of the voice input. In this way, filtering the environmental sounds improves the accuracy of the voice input.

[0079] The reception unit can estimate the user's emotions and determine the priority of voice inputs based on the emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving important requests. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed requests. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving requests that require a quick response. In this way, the priority of voice inputs is determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information when inputting voice. For example, when the user is in a specific area, the reception unit can prioritize receiving requests related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving requests related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving requests around the user's home. In this way, highly relevant requests can be prioritized by taking into account the geographical location information.

[0081] The reception unit can analyze the user's social media activity and accept related requests when voice input is performed. For example, the reception unit accepts requests related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's posts on social media and accept related requests. The reception unit can also accept related requests by referring to the activities of the user's friends on social media. In this way, related requests can be accepted by analyzing social media activity.

[0082] The reception unit can customize the reception method by reflecting the user's past feedback when inputting voice. The reception unit customizes the voice input interface, for example, based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the optimal voice input method. In this way, the reception method is customized by reflecting the past feedback.

[0083] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the emotions. For example, when the user is feeling stressed, the acquisition unit can quickly acquire location information and make suggestions. Furthermore, when the user is relaxed, the acquisition unit can also acquire detailed location information and make suggestions. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire location information and make suggestions. In this way, the timing of acquiring location information is adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] When acquiring location information, the acquisition unit can select the optimal acquisition method by referring to the user's past movement history. The acquisition unit acquires optimal location information, for example, based on places the user has visited in the past. The acquisition unit can also acquire location information for avoiding congestion from the user's past movement history. The acquisition unit can also analyze the user's past movement history and acquire the most efficient location information. This makes it possible to acquire optimal location information by referring to the past movement history.

[0085] The acquisition unit can perform filtering based on the user's current activity status when acquiring location information. For example, when the user is on the move, the acquisition unit prioritizes acquiring current location information. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring location information around the user's home. Furthermore, when the user is participating in a specific event, the acquisition unit can also prioritize acquiring location information related to the event. This makes it possible to filter location information based on the user's current activity status.

[0086] When acquiring location information, the acquisition unit can select the optimal acquisition method depending on the user's device information. For example, if the user is using a smartphone, the acquisition unit acquires location information using GPS. If the user is using a tablet, the acquisition unit can also acquire location information using Wi-Fi. If the user is using a smartwatch, the acquisition unit can also acquire location information using Bluetooth. This makes it possible to acquire optimal location information depending on the device information.

[0087] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring important location information. Furthermore, when the user is relaxed, the acquisition unit can also prioritize acquiring detailed location information. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring location information that needs to be acquired quickly. In this way, the priority of location information is determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring location information related to that area. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring location information related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring location information around the user's home. In this way, highly relevant information is prioritized by taking into account the geographical location information.

[0089] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit acquires location information related to a location where the user checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related location information. The acquisition unit can also acquire related location information by referring to the activities of the user's friends on social media. In this way, related information is acquired by analyzing social media activity.

[0090] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. The acquisition unit customizes the location information acquisition method based on, for example, feedback provided by the user in the past. The acquisition unit can also preferentially suggest a specific acquisition method based on the user's past feedback. The acquisition unit can also analyze the user's past feedback and suggest the optimal location information acquisition method. In this way, the acquisition method is customized by reflecting the past feedback.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the emotions. For example, if the user is feeling stressed, the suggestion unit makes simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also make suggestions that focus on the main points. This provides an optimal way to express suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past selection history. The suggestion unit makes optimal suggestions based on, for example, dishes selected by the user in the past. The suggestion unit can also make suggestions to avoid crowds based on the user's past selection history. The suggestion unit can also analyze the user's past selection history and make the most efficient suggestion. In this way, by referring to the past selection history, the accuracy of the suggestion is improved.

[0093] When making a suggestion, the suggestion unit can customize the suggestion content based on the user's current activity status. For example, when the user is on the move, the suggestion unit makes a suggestion based on current location information. Furthermore, when the user is at home, the suggestion unit can also make suggestions around the user's home. Furthermore, when the user is participating in a specific event, the suggestion unit can also make suggestions related to the event. In this way, the suggestion content is customized based on the user's current activity status.

[0094] When making a proposal, the proposal unit can adjust the level of detail of the proposal according to the user's budget and time constraints. For example, when the user specifies a budget, the proposal unit makes an optimal proposal within that budget. Furthermore, when the user specifies a time constraint, the proposal unit can also make an optimal proposal within that time. Furthermore, when the user specifies a budget and a time constraint, the proposal unit can also make an optimal proposal based on those conditions. In this way, an optimal proposal according to the budget and time constraints is provided.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize important suggestions. Furthermore, if the user is relaxed, the suggestion unit can prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions that require a quick response. In this way, the priority of suggestions is determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when the user is in a specific area, the suggestion unit can prioritize suggestions related to that area. Furthermore, when the user is traveling, the suggestion unit can also prioritize suggestions related to the travel destination. Furthermore, when the user is at home, the suggestion unit can also prioritize suggestions around the user's home. In this way, highly relevant suggestions are prioritized by taking into account the geographical location information.

[0097] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a related suggestion. For example, the suggestion unit can make a suggestion related to a place where the user has checked in on social media. The suggestion unit can also analyze the content of the user's posts on social media and make a related suggestion. The suggestion unit can also make a related suggestion by referring to the activities of the user's friends on social media. In this way, related suggestions are made by analyzing social media activity.

[0098] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion interface based on feedback provided by the user in the past. The suggestion unit can also preferentially suggest a specific suggestion method based on the user's past feedback. The suggestion unit can also analyze the user's past feedback and suggest an optimal suggestion method. In this way, the suggestion method is customized by reflecting the past feedback.

[0099] The proxy unit can estimate the user's emotions and adjust the proxy method based on the emotions. For example, if the user is feeling stressed, the proxy unit can quickly perform proxy actions. Furthermore, if the user is relaxed, the proxy unit can also perform detailed proxy actions. Furthermore, if the user is in a hurry, the proxy unit can also perform proxy actions that require a quick response. This provides an optimal proxy method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] When acting as an agent, the agent unit can select the optimal agent method by referring to the user's past ordering history. The agent unit selects the optimal agent method, for example, based on the ordering methods used by the user in the past. The agent unit can also select an agent method that avoids congestion from the user's past ordering history. The agent unit can also analyze the user's past ordering history and select the most efficient agent method. In this way, the optimal agent method is selected by referring to the past ordering history.

[0101] The proxy unit can customize the proxy content based on the user's current activity status when proxying. For example, when the user is on the move, the proxy unit performs proxying based on current location information. Furthermore, when the user is at home, the proxy unit can also perform proxying around the user's home. Furthermore, when the user is participating in a specific event, the proxy unit can also perform proxying related to the event. In this way, the proxy content is customized based on the user's current activity status.

[0102] The proxy unit can improve the proxy method by reflecting user feedback during proxy operation. For example, the proxy unit can improve the proxy interface based on feedback previously provided by the user. The proxy unit can also preferentially suggest a specific proxy method based on the user's past feedback. The proxy unit can also analyze the user's past feedback and suggest the optimal proxy method. In this way, the proxy method is improved by reflecting the feedback.

[0103] The proxy unit can estimate the user's emotions and determine the priority of proxy tasks based on the emotions. For example, when the user is feeling stressed, the proxy unit can prioritize important proxy tasks. Furthermore, when the user is relaxed, the proxy unit can also prioritize detailed proxy tasks. Furthermore, when the user is in a hurry, the proxy unit can also prioritize proxy tasks that require a quick response. In this way, the priority of proxy tasks is determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When performing a proxy operation, the proxy unit can select the optimal proxy method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the proxy unit selects a proxy method related to that area. Furthermore, if the user is traveling, the proxy unit can also select a proxy method related to the user's travel destination. Furthermore, if the user is at home, the proxy unit can also select a proxy method near the user's home. In this way, the optimal proxy method is selected by taking into consideration the geographical location information.

[0105] The proxy unit can analyze the user's social media activity and suggest proxy means when proxying. For example, the proxy unit can suggest proxy methods related to places where the user has checked in on social media. The proxy unit can also analyze the content of the user's social media posts and suggest related proxy methods. The proxy unit can also suggest related proxy methods with reference to the activities of the user's friends on social media. In this way, the optimal proxy means is suggested by analyzing social media activity.

[0106] The proxy unit can customize the proxy method by reflecting the user's past feedback when proxying. The proxy unit customizes the proxy interface, for example, based on feedback provided by the user in the past. The proxy unit can also preferentially suggest a specific proxy method based on the user's past feedback. The proxy unit can also analyze the user's past feedback and suggest the optimal proxy method. In this way, the proxy method is customized by reflecting the past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and agent unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts voice input from the user using the microphone 38B of the smart device 14 and processes the voice data using the control unit 46A. The acquisition unit acquires user location information using the specific processing unit 290 of the data processing device 12. The proposal unit proposes nearby delivery arrangements and store reservations using the specific processing unit 290 of the data processing device 12 and customizes the proposal content. The agent unit handles arrangements, reservations, and payments on behalf of the user using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and agent unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts a user's voice input using the microphone 238 of the smart glasses 214 and processes the voice data using the control unit 46A. The acquisition unit acquires the user's location information using the specific processing unit 290 of the data processing device 12. The proposal unit proposes nearby delivery arrangements and store reservations using the specific processing unit 290 of the data processing device 12 and customizes the proposal content. The agent unit handles arrangements, reservations, and payments on behalf of the user using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and agent unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit accepts voice input from the user using the microphone 238 of the headset-type terminal 314 and processes the voice data by the control unit 46A. The acquisition unit acquires user location information by the specific processing unit 290 of the data processing device 12. The proposal unit makes proposals for nearby delivery arrangements and store reservations by the specific processing unit 290 of the data processing device 12 and customizes the proposal content. The agent unit handles arrangements, reservations, and payments by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and agent unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit accepts voice input from the user using the microphone 238 of the robot 414 and processes the voice data by the control unit 46A. The acquisition unit acquires location information of the user by the specific processing unit 290 of the data processing device 12. The proposal unit makes proposals for nearby delivery arrangements and store reservations by the specific processing unit 290 of the data processing device 12 and customizes the proposal content. The agent unit handles arrangements, reservations, and payments on behalf of the user by the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When accepting a user's voice input, the reception unit can improve the accuracy of the request by referring to the user's past meal history. For example, dishes that the user has frequently ordered in the past can be displayed as candidates with priority. The reception unit can also predict and suggest dishes that the user will request during a specific time period based on the user's past request history. Furthermore, the reception unit can analyze the user's preference for dishes based on the user's past request history and improve the accuracy of the request. This allows the user to make requests more smoothly by utilizing their past history.

[0109] When acquiring the user's location information, the acquisition unit can select the optimal acquisition method taking into consideration the user's current activity status. For example, if the user is on the move, the acquisition unit can prioritize acquiring current location information. Also, if the user is at home, the acquisition unit can prioritize acquiring location information around the user's home. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring location information related to the event. This makes it possible to acquire location information based on the user's current activity status.

[0110] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also make suggestions that focus on the main points. This provides an optimal way to express suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past selection history. For example, the suggestion unit makes the optimal suggestion based on dishes selected by the user in the past. The suggestion unit can also make suggestions to avoid crowds based on the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and make the most efficient suggestion. In this way, by referring to the past selection history, the accuracy of the suggestion is improved.

[0112] The proxy unit can estimate the user's emotions and adjust the proxy method based on the emotions. For example, if the user is feeling stressed, the proxy unit can quickly perform proxy actions. Also, if the user is relaxed, the proxy unit can perform detailed proxy actions. Furthermore, if the user is in a hurry, the proxy unit can perform proxy actions that require a quick response. This provides an optimal proxy method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] When acting as an agent, the agent unit can select the optimal agent method by referring to the user's past ordering history. For example, the agent unit selects the optimal agent method based on the ordering methods used by the user in the past. The agent unit can also select an agent method that avoids congestion based on the user's past ordering history. Furthermore, the agent unit can analyze the user's past ordering history and select the most efficient agent method. In this way, the optimal agent method is selected by referring to the past ordering history.

[0114] The reception unit can estimate the user's emotions and determine the priority of voice inputs based on the emotions. For example, if the user is feeling stressed, important requests can be given priority. The reception unit can also give priority to detailed requests when the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also give priority to requests that require a quick response. This allows the priority of voice inputs to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] When acquiring location information, the acquisition unit can select the optimal acquisition method by referring to the user's past movement history. For example, the optimal location information is acquired based on places the user has visited in the past. The acquisition unit can also acquire location information for avoiding congestion from the user's past movement history. Furthermore, the acquisition unit can analyze the user's past movement history and acquire the most efficient location information. This makes it possible to acquire optimal location information by referring to the past movement history.

[0116] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the emotions. For example, if the user is feeling stressed, important suggestions are given priority. The suggestion unit can also give priority to detailed suggestions if the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can also give priority to suggestions that require a quick response. In this way, the priority of suggestions is determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] When performing a proxy operation, the proxy unit can select the optimal proxy method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the proxy unit selects a proxy method related to that area. Also, if the user is traveling, the proxy unit can select a proxy method related to the user's travel destination. Furthermore, if the user is at home, the proxy unit can select a proxy method near the user's home. In this way, the optimal proxy method is selected by taking into consideration the geographical location information.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives a voice request from the user regarding the food they want to eat. For example, the user may input a request such as "I want pizza." Step 2: The acquisition unit acquires the user's location information based on the information received by the reception unit. For example, the location information is acquired using GPS, Wi-Fi location information, cell tower location information, etc. Step 3: The suggestion unit makes suggestions for nearby delivery arrangements and restaurant reservations based on the location information acquired by the acquisition unit. For example, if a user requests "I want pizza," the system will suggest nearby pizza delivery services and reservations at pizza restaurants. The suggestion unit also customizes the suggestions based on factors such as time and price. For example, if a user specifies conditions such as "I want delivery within 30 minutes" or "My budget is within 2,000 yen," the system will make suggestions accordingly. Step 4: The agent handles arrangements, reservations, and payments based on the proposals made by the proposal unit. For example, if the user accepts the proposed pizza delivery, the system automatically arranges delivery and handles payment.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice input; an acquisition unit that acquires location information based on the information accepted by the acceptance unit; a suggestion unit that makes a suggestion based on the location information acquired by the acquisition unit; An agent unit that handles arrangements, reservations, and payments based on the content proposed by the proposal unit is provided. A system characterized by:

2. The reception unit Accepts voice input of the food the user wants to eat 2. The system of claim 1.

3. The acquisition unit Acquires user location information based on the information received by the receiving unit.

2. The system of claim 1.

4. The proposal unit Based on the location information acquired by the acquisition unit, a proposal is made for nearby delivery arrangements and store reservations.

2. The system of claim 1.

5. The proposal unit Customize your proposal in terms of time or money 2. The system of claim 1.

6. The agent unit: Based on the content proposed by the proposal department, the agency will handle arrangements, reservations, and payments.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the method of accepting voice input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When inputting by voice, the accuracy of input is improved by referring to the user's past request history.

2. The system of claim 1.

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