System

Abstract

An object of a system according to an embodiment is to quickly and accurately respond to a wide variety of demands of users.SOLUTION: A system includes a reception part, an analysis part, an arrangement part, and a provision part. The reception unit receives a request of a user in a natural language. The analysis unit analyzes the request received by the reception unit and specifies specific details of the arrangement or the guidance. The arrangement unit performs arrangement or guidance based on the content specified by the analysis unit. The providing unit provides the user with a result of the arrangement or guidance performed by the arranging unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to respond quickly and accurately to a wide range of user needs.

[0005] The system according to the embodiment aims to quickly and accurately respond to a wide range of user requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a dispatch unit, and a provision unit. The reception unit receives a user's request in natural language. The analysis unit analyzes the request received by the reception unit and identifies specific details of arrangements and guidance. The dispatch unit makes arrangements and guidance based on the details identified by the analysis unit. The provision unit provides the user with the results of the arrangements and guidance made by the dispatch unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately respond to a wide range of user requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI concierge system according to an embodiment of the present invention is a system that accepts a user's request in natural language, analyzes it, makes arrangements and guidance, and provides the results. The AI ​​concierge system accepts a user's request in natural language, analyzes it using AI, and identifies specific arrangements and guidance content. Next, an arrangement unit makes arrangements and guidance based on the identified content, and a provision unit provides the results to the user. For example, in the AI ​​concierge system, a user inputs, "I would like to make a restaurant reservation for tomorrow night." This request is accepted by a reception unit. Next, an analysis unit is provided that analyzes the request accepted by the reception unit. The analysis unit analyzes the user's request using AI and identifies specific arrangements and guidance content. For example, for a request such as, "I would like to make a restaurant reservation for tomorrow night," the analysis unit identifies the specific content, "restaurant reservation." Next, the arrangement unit makes arrangements and guidance based on the identified content. The arrangement unit arranges accommodations and tourist spots, makes airline and restaurant reservations, provides transportation information, provides medical consultations, and so on. For example, based on the content, "restaurant reservation," identified by the analysis unit, the arrangement unit reserves a restaurant that meets the user's preferences. Finally, a provision unit is provided that provides the results of the arrangements and guidance made by the arrangement unit to the user. The provision unit provides the results of the arrangements and guidance made by the arrangement unit to the user. For example, it provides information about restaurants reserved by the arrangement unit to the user. This allows the AI ​​concierge system to efficiently accept and analyze user requests, make arrangements and guidance, and provide the results. This allows the AI ​​concierge system to efficiently accept and analyze user requests, make arrangements and guidance, and provide the results. For example, users can receive service that is just like having a personal butler, and support can be provided to help them spend their daily lives comfortably.

[0029] The AI ​​concierge system according to the embodiment includes a reception unit, an analysis unit, an arrangement unit, and a provision unit. The reception unit receives a user's request in natural language. The user's request may include, but is not limited to, a service request or an information inquiry. For example, the user may input "I would like to make a restaurant reservation for tomorrow night" into the reception unit. This request is received by the reception unit. The analysis unit uses AI to analyze the request received by the reception unit and identify specific arrangements and guidance content. The analysis may be performed using, but is not limited to, methods such as text analysis and sentiment analysis. For example, the analysis unit identifies the specific content, "restaurant reservation," for the request, "I would like to make a restaurant reservation for tomorrow night." The arrangement unit provides arrangements and guidance based on the content identified by the analysis unit. The arrangements and guidance may include, but are not limited to, arrangements for accommodations and tourist spots, reservations for airline tickets and restaurants, transportation information, medical consultations, and the like. For example, the arrangement unit reserves a restaurant that meets the user's needs based on the content, "restaurant reservation," identified by the analysis unit. The providing unit provides the user with the results of arrangements and guidance made by the arrangement unit. The provision may be, for example, by email notification or display within the app, but is not limited to these examples. For example, the providing unit may provide the user with information about the restaurant reserved by the arrangement unit. This allows the AI ​​concierge system according to the embodiment to efficiently accept and analyze user requests, make arrangements and guidance, and provide the results.

[0030] The reception unit can analyze the user's past request history and select an appropriate reception method. For example, the reception unit can automatically display requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest requests to be used in a specific time period based on the user's past request history. This makes it possible to provide the optimal reception method based on the user's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past request data into a generation AI and have the generation AI select the optimal reception method.

[0031] The reception unit can filter requests based on the user's current situation and areas of interest when receiving the request. The reception unit, for example, preferentially receives related requests based on the user's current location. The reception unit can also filter and receive related requests based on the user's areas of interest. The reception unit can also suggest optimal requests based on the user's current situation (time of day, weather, etc.). This makes it possible to receive optimal requests based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current situation data to the generation AI and cause the generation AI to filter optimal requests.

[0032] When receiving a request, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs a request by voice, the reception unit receives the request using voice recognition technology. Furthermore, when the user inputs a request by text, the reception unit can also receive the request using text analysis technology. Furthermore, when the user inputs a request by image, the reception unit can also receive the request using image recognition technology. This makes it possible to provide an optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0033] When receiving a request, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. For example, the reception unit can prioritize receiving requests for nearby restaurants and tourist attractions based on the user's current location. Furthermore, when the user is traveling, the reception unit can prioritize receiving information about the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving requests related to nearby services. This makes it possible to prioritize receiving optimal requests based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to prioritize highly relevant requests.

[0034] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can prioritize receiving 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 social media posts and receive related requests. The reception unit can also receive related requests by referring to the activities of the user's friends on social media. This makes it possible to receive optimal requests based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related requests.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis for requests with a high level of importance. The analysis unit can also perform a simplified analysis for requests with a low level of importance. The analysis unit can also adjust the priority of the analysis according to the importance. This makes it possible to provide optimal analysis results according to the importance of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input request importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request. For example, the analysis unit can apply an algorithm that analyzes restaurant ratings and seat availability information to a request regarding a restaurant reservation. The analysis unit can also apply an algorithm that analyzes traffic information to a request regarding traffic directions. The analysis unit can also apply an algorithm that analyzes medical information to a request regarding a medical consultation. This makes it possible to provide optimal analysis results depending on the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs highly accurate analysis for similar requests based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of request submission. For example, the analysis unit prioritizes analysis for urgent requests. The analysis unit can also analyze normal requests with normal priority. The analysis unit can also adjust the priority of analysis depending on the time of submission. This makes it possible to provide optimal analysis results depending on the time of request submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the time of request submission into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also postpone analysis of less relevant requests. The analysis unit can also adjust the order of analysis based on the relevance. This makes it possible to provide optimal analysis results based on the relevance of the requests. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of requests to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in easy-to-understand language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0042] When making a request, the dispatch unit can analyze the user's past dispatch history and select an appropriate dispatch method. For example, the dispatch unit can suggest the optimal dispatch method based on the dispatch methods used by the user in the past. The dispatch unit can also extract specific patterns from the user's past dispatch history and select the optimal dispatch method. The dispatch unit can also analyze the user's past dispatch history and improve the dispatch method. This makes it possible to provide the optimal dispatch method based on the user's past dispatch history. Some or all of the above-described processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input the user's past dispatch data into a generation AI and have the generation AI select the optimal dispatch method.

[0043] The dispatch unit can customize the means of dispatch based on the user's current situation when dispatching. The dispatch unit, for example, suggests the optimal dispatch method based on the user's current location. The dispatch unit can also customize the means of dispatch based on the user's current situation (time of day, weather, etc.). The dispatch unit can also adjust the priority of dispatches taking into account the user's current situation. This makes it possible to provide the optimal means of dispatch according to the user's current situation. Some or all of the above-described processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can input the user's current situation data into a generation AI and have the generation AI customize the means of dispatch.

[0044] The dispatch unit can improve the dispatch method by reflecting user feedback when dispatching. The dispatch unit, for example, proposes the optimal dispatch method based on the user's past feedback. The dispatch unit can also preferentially propose a specific dispatch method based on the user's feedback. The dispatch unit can also analyze the user's feedback and improve the dispatch method. This makes it possible to provide the optimal dispatch method based on the user's feedback. Some or all of the above-mentioned processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input user feedback data into a generation AI and have the generation AI improve the dispatch method.

[0045] When making arrangements, the arrangement unit can select the optimal arrangement method by taking into account the user's geographical location information. For example, the arrangement unit prioritizes arrangements for nearby accommodations and restaurants based on the user's current location. Furthermore, if the user is traveling, the arrangement unit can also make arrangements based on information about the user's travel destination. Furthermore, if the user is at home, the arrangement unit can also prioritize arrangements for nearby services. This makes it possible to provide the optimal arrangement method based on the user's geographical location information. Some or all of the above-mentioned processing in the arrangement unit may be performed using, or without, AI, for example. For example, the arrangement unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal arrangement method.

[0046] When making arrangements, the dispatch unit can analyze the user's social media activity and suggest arrangement means. For example, the dispatch unit prioritizes arrangements related to places where the user has checked in on social media. The dispatch unit can also analyze the content of the user's social media posts and suggest related arrangements. The dispatch unit can also suggest related arrangements based on the activity of the user's friends on social media. This makes it possible to provide the optimal arrangement means based on the user's social media activity. Some or all of the above-mentioned processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input the user's social media data into a generation AI and have the generation AI suggest arrangement means.

[0047] The dispatch unit can customize the dispatch method by reflecting the user's past feedback when dispatching. The dispatch unit, for example, proposes the optimal dispatch method based on the user's past feedback. The dispatch unit can also preferentially propose a specific dispatch method based on the user's feedback. The dispatch unit can also analyze the user's feedback and improve the dispatch method. This makes it possible to provide the optimal dispatch method based on the user's past feedback. Some or all of the above-described processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input the user's feedback data into a generation AI and have the generation AI customize the dispatch method.

[0048] At the time of provision, the providing unit can select an appropriate provision method by analyzing the user's past provision history. For example, the providing unit can suggest an optimal provision method based on the provision methods used by the user in the past. The providing unit can also extract specific patterns from the user's past provision history and select an optimal provision method. The providing unit can also analyze the user's past provision history and improve the provision method. This makes it possible to provide an optimal provision method based on the user's past provision history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision data into a generation AI and cause the generation AI to select an optimal provision method.

[0049] The providing unit can customize the means of provision based on the user's current situation at the time of provision. The providing unit can suggest the optimal provision method based on, for example, the user's current location. The providing unit can also customize the means of provision based on the user's current situation (time of day, weather, etc.). The providing unit can also adjust the priority of provision taking into account the user's current situation. This makes it possible to provide the optimal means of provision according to the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into the generation AI and cause the generation AI to customize the means of provision.

[0050] The providing unit can improve the method of provision by reflecting user feedback at the time of provision. The providing unit can, for example, suggest an optimal method of provision based on the user's past feedback. The providing unit can also preferentially suggest a specific method of provision based on the user's feedback. The providing unit can also analyze the user's feedback and improve the method of provision. This makes it possible to provide an optimal method of provision based on the user's feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the method of provision.

[0051] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, the providing unit can prioritize providing nearby services and information based on the user's current location. Furthermore, if the user is traveling, the providing unit can also provide information based on travel destination information. Furthermore, if the user is at home, the providing unit can prioritize providing information about nearby services. This makes it possible to provide the optimal providing method based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to the generating AI and cause the generating AI to select the optimal providing method.

[0052] At the time of providing, the providing unit can analyze the user's social media activity and suggest a means of provision. For example, the providing unit can prioritize providing information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the user's friends on social media. This makes it possible to provide the optimal means of provision based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest a means of provision.

[0053] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The providing unit, for example, suggests an optimal delivery method based on the user's past feedback. The providing unit can also preferentially suggest a specific delivery method based on the user's feedback. The providing unit can also analyze the user's feedback and improve the delivery method. This makes it possible to provide an optimal delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the delivery method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When accepting a user's request, the reception unit can analyze the user's past behavioral patterns and suggest the optimal reception method. For example, it can preferentially display services that the user has frequently used in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests that will be made during a specific time period based on the user's past behavioral patterns. This makes it possible to provide the optimal reception method based on the user's past behavioral patterns.

[0056] The dispatch unit can customize the dispatch method based on the user's current situation. For example, it can propose the optimal dispatch method based on the user's current location. The dispatch unit can also customize the dispatch method based on the user's current situation (time of day, weather, etc.). Furthermore, the dispatch unit can adjust the dispatch priority taking into account the user's current situation. This makes it possible to provide the optimal dispatch method according to the user's current situation.

[0057] The reception unit can analyze the user's social media activity and receive related requests. For example, requests related to places where the user has checked in on social media are preferentially received. The reception unit can also analyze the content of the user's posts on social media and receive related requests. Furthermore, the reception unit can also receive related requests by referring to the activities of the user's friends on social media. This makes it possible to receive optimal requests based on the user's social media activity.

[0058] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can perform highly accurate analysis for similar requests based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and improve the analysis algorithm. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0059] The providing unit can improve the method of provision by reflecting the user's past feedback. For example, the providing unit can suggest an optimal method of provision based on the user's past feedback. The providing unit can also preferentially suggest a specific method of provision based on the user's feedback. Furthermore, the providing unit can analyze the user's feedback and improve the method of provision. This makes it possible to provide an optimal method of provision based on the user's past feedback.

[0060] The analysis unit can apply different analysis algorithms depending on the category of the request. For example, for a request regarding a restaurant reservation, an algorithm that analyzes restaurant ratings and seat availability information can be applied. For a request regarding transportation directions, an algorithm that analyzes transportation information can be applied. Furthermore, for a request regarding medical consultation, an algorithm that analyzes medical information can be applied. This makes it possible to provide optimal analysis results according to the category of the request.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts the user's request in natural language. User requests include service requests and information inquiries. For example, the user might enter, "I'd like to make a restaurant reservation for tomorrow night." This request is accepted by the reception unit. Step 2: The analysis unit uses AI to analyze the request received by the reception unit and identify the specific arrangements and information to be provided. Analysis is performed using methods such as text analysis and sentiment analysis. For example, in response to a request such as "I would like to make a restaurant reservation for tomorrow night," the specific content of "restaurant reservation" is identified. Step 3: The arrangement unit makes arrangements and guidance based on the content identified by the analysis unit. Arrangements and guidance include arranging accommodations and tourist spots, booking airline tickets and restaurants, providing transportation information, and providing medical consultations. For example, the arrangement unit makes reservations at a restaurant that meets the user's preferences based on the content "reservation of a restaurant" identified by the analysis unit. Step 4: The providing unit provides the user with the results of the arrangements and guidance made by the arrangement unit. The results are provided by means of email notification, display within the app, etc. For example, the providing unit provides the user with information about the restaurant reserved by the arrangement unit.

[0063] (Example 2) An AI concierge system according to an embodiment of the present invention is a system that accepts a user's request in natural language, analyzes it, makes arrangements and guidance, and provides the results. The AI ​​concierge system accepts a user's request in natural language, analyzes it using AI, and identifies specific arrangements and guidance content. Next, an arrangement unit makes arrangements and guidance based on the identified content, and a provision unit provides the results to the user. For example, in the AI ​​concierge system, a user inputs, "I would like to make a restaurant reservation for tomorrow night." This request is accepted by a reception unit. Next, an analysis unit is provided that analyzes the request accepted by the reception unit. The analysis unit analyzes the user's request using AI and identifies specific arrangements and guidance content. For example, for a request such as, "I would like to make a restaurant reservation for tomorrow night," the analysis unit identifies the specific content, "restaurant reservation." Next, the arrangement unit makes arrangements and guidance based on the identified content. The arrangement unit arranges accommodations and tourist spots, makes airline and restaurant reservations, provides transportation information, provides medical consultations, and so on. For example, based on the content, "restaurant reservation," identified by the analysis unit, the arrangement unit reserves a restaurant that meets the user's preferences. Finally, a provision unit is provided that provides the results of the arrangements and guidance made by the arrangement unit to the user. The provision unit provides the results of the arrangements and guidance made by the arrangement unit to the user. For example, it provides information about restaurants reserved by the arrangement unit to the user. This allows the AI ​​concierge system to efficiently accept and analyze user requests, make arrangements and guidance, and provide the results. This allows the AI ​​concierge system to efficiently accept and analyze user requests, make arrangements and guidance, and provide the results. For example, users can receive service that is just like having a personal butler, and support can be provided to help them spend their daily lives comfortably.

[0064] The AI ​​concierge system according to the embodiment includes a reception unit, an analysis unit, an arrangement unit, and a provision unit. The reception unit receives a user's request in natural language. The user's request may include, but is not limited to, a service request or an information inquiry. For example, the user may input "I would like to make a restaurant reservation for tomorrow night" into the reception unit. This request is received by the reception unit. The analysis unit uses AI to analyze the request received by the reception unit and identify specific arrangements and guidance content. The analysis may be performed using, but is not limited to, methods such as text analysis and sentiment analysis. For example, the analysis unit identifies the specific content, "restaurant reservation," for the request, "I would like to make a restaurant reservation for tomorrow night." The arrangement unit provides arrangements and guidance based on the content identified by the analysis unit. The arrangements and guidance may include, but are not limited to, arrangements for accommodations and tourist spots, reservations for airline tickets and restaurants, transportation information, medical consultations, and the like. For example, the arrangement unit reserves a restaurant that meets the user's needs based on the content, "restaurant reservation," identified by the analysis unit. The providing unit provides the user with the results of arrangements and guidance made by the arrangement unit. The provision may be, for example, by email notification or display within the app, but is not limited to these examples. For example, the providing unit may provide the user with information about the restaurant reserved by the arrangement unit. This allows the AI ​​concierge system according to the embodiment to efficiently accept and analyze user requests, make arrangements and guidance, and provide the results.

[0065] The reception unit can estimate the user's emotions and adjust the request reception method based on the estimated user 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 quickly receive requests. This makes it possible to provide an optimal reception method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0066] The reception unit can analyze the user's past request history and select an appropriate reception method. For example, the reception unit can automatically display requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest requests to be used in a specific time period based on the user's past request history. This makes it possible to provide the optimal reception method based on the user's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past request data into a generation AI and have the generation AI select the optimal reception method.

[0067] The reception unit can filter requests based on the user's current situation and areas of interest when receiving the request. The reception unit, for example, preferentially receives related requests based on the user's current location. The reception unit can also filter and receive related requests based on the user's areas of interest. The reception unit can also suggest optimal requests based on the user's current situation (time of day, weather, etc.). This makes it possible to receive optimal requests based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current situation data to the generation AI and cause the generation AI to filter optimal requests.

[0068] When receiving a request, the reception unit can select an appropriate reception means depending on the user's input method. For example, when the user inputs a request by voice, the reception unit receives the request using voice recognition technology. Furthermore, when the user inputs a request by text, the reception unit can also receive the request using text analysis technology. Furthermore, when the user inputs a request by image, the reception unit can also receive the request using image recognition technology. This makes it possible to provide an optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0069] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user emotions. For example, the reception unit prioritizes requests when the user has an urgent request. The reception unit can also receive requests with normal priority when the user is relaxed. The reception unit can also raise the priority to respond quickly when the user is stressed. This allows requests to be received with the optimal priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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 such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0070] When receiving a request, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. For example, the reception unit can prioritize receiving requests for nearby restaurants and tourist attractions based on the user's current location. Furthermore, when the user is traveling, the reception unit can prioritize receiving information about the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving requests related to nearby services. This makes it possible to prioritize receiving optimal requests based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to prioritize highly relevant requests.

[0071] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can prioritize receiving 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 social media posts and receive related requests. The reception unit can also receive related requests by referring to the activities of the user's friends on social media. This makes it possible to receive optimal requests based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related requests.

[0072] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and improve the reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for optimal analysis results to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis for requests with a high level of importance. The analysis unit can also perform a simplified analysis for requests with a low level of importance. The analysis unit can also adjust the priority of the analysis according to the importance. This makes it possible to provide optimal analysis results according to the importance of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input request importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request. For example, the analysis unit can apply an algorithm that analyzes restaurant ratings and seat availability information to a request regarding a restaurant reservation. The analysis unit can also apply an algorithm that analyzes traffic information to a request regarding traffic directions. The analysis unit can also apply an algorithm that analyzes medical information to a request regarding a medical consultation. This makes it possible to provide optimal analysis results depending on the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs highly accurate analysis for similar requests based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. This allows the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for optimal analysis results to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the length of the analysis.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the time of request submission. For example, the analysis unit prioritizes analysis for urgent requests. The analysis unit can also analyze normal requests with normal priority. The analysis unit can also adjust the priority of analysis depending on the time of submission. This makes it possible to provide optimal analysis results depending on the time of request submission. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the time of request submission into the generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the requests. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also postpone analysis of less relevant requests. The analysis unit can also adjust the order of analysis based on the relevance. This makes it possible to provide optimal analysis results based on the relevance of the requests. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of requests to the generation AI and have the generation AI adjust the order of analysis.

[0080] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in easy-to-understand language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0081] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user emotions. For example, if the user is nervous, the dispatch unit can provide a simple and quick dispatch method. Furthermore, if the user is relaxed, the dispatch unit can also provide detailed dispatch options. Furthermore, if the user is in a hurry, the dispatch unit can also provide a dispatch method for making a quick dispatch. This makes it possible to provide an optimal dispatch method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with 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. Some or all of the above-described processing in the dispatch unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the dispatch unit can input the user's facial expression data into the generation AI and have the generation AI adjust the dispatch method.

[0082] When making a request, the dispatch unit can analyze the user's past dispatch history and select an appropriate dispatch method. For example, the dispatch unit can suggest the optimal dispatch method based on the dispatch methods used by the user in the past. The dispatch unit can also extract specific patterns from the user's past dispatch history and select the optimal dispatch method. The dispatch unit can also analyze the user's past dispatch history and improve the dispatch method. This makes it possible to provide the optimal dispatch method based on the user's past dispatch history. Some or all of the above-described processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input the user's past dispatch data into a generation AI and have the generation AI select the optimal dispatch method.

[0083] The dispatch unit can customize the means of dispatch based on the user's current situation when dispatching. The dispatch unit, for example, suggests the optimal dispatch method based on the user's current location. The dispatch unit can also customize the means of dispatch based on the user's current situation (time of day, weather, etc.). The dispatch unit can also adjust the priority of dispatches taking into account the user's current situation. This makes it possible to provide the optimal means of dispatch according to the user's current situation. Some or all of the above-described processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can input the user's current situation data into a generation AI and have the generation AI customize the means of dispatch.

[0084] The dispatch unit can improve the dispatch method by reflecting user feedback when dispatching. The dispatch unit, for example, proposes the optimal dispatch method based on the user's past feedback. The dispatch unit can also preferentially propose a specific dispatch method based on the user's feedback. The dispatch unit can also analyze the user's feedback and improve the dispatch method. This makes it possible to provide the optimal dispatch method based on the user's feedback. Some or all of the above-mentioned processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input user feedback data into a generation AI and have the generation AI improve the dispatch method.

[0085] The dispatch unit can estimate the user's emotions and determine the priority of dispatch based on the estimated user emotions. For example, if the user needs urgent dispatch, the dispatch unit prioritizes dispatch. Furthermore, if the user is relaxed, the dispatch unit can also make dispatches with normal priority. Furthermore, if the user is stressed, the dispatch unit can also raise the priority of dispatch to respond quickly. This allows dispatches to be made with optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dispatch unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dispatch unit can input the user's facial expression data into the generation AI and have the generation AI determine the dispatch priority.

[0086] When making arrangements, the arrangement unit can select the optimal arrangement method by taking into account the user's geographical location information. For example, the arrangement unit prioritizes arrangements for nearby accommodations and restaurants based on the user's current location. Furthermore, if the user is traveling, the arrangement unit can also make arrangements based on information about the user's travel destination. Furthermore, if the user is at home, the arrangement unit can also prioritize arrangements for nearby services. This makes it possible to provide the optimal arrangement method based on the user's geographical location information. Some or all of the above-mentioned processing in the arrangement unit may be performed using, or without, AI, for example. For example, the arrangement unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal arrangement method.

[0087] When making arrangements, the dispatch unit can analyze the user's social media activity and suggest arrangement means. For example, the dispatch unit prioritizes arrangements related to places where the user has checked in on social media. The dispatch unit can also analyze the content of the user's social media posts and suggest related arrangements. The dispatch unit can also suggest related arrangements based on the activity of the user's friends on social media. This makes it possible to provide the optimal arrangement means based on the user's social media activity. Some or all of the above-mentioned processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input the user's social media data into a generation AI and have the generation AI suggest arrangement means.

[0088] The dispatch unit can customize the dispatch method by reflecting the user's past feedback when dispatching. The dispatch unit, for example, proposes the optimal dispatch method based on the user's past feedback. The dispatch unit can also preferentially propose a specific dispatch method based on the user's feedback. The dispatch unit can also analyze the user's feedback and improve the dispatch method. This makes it possible to provide the optimal dispatch method based on the user's past feedback. Some or all of the above-described processing in the dispatch unit may be performed using, for example, AI, or may be performed without using AI. For example, the dispatch unit can input the user's feedback data into a generation AI and have the generation AI customize the dispatch method.

[0089] The providing unit can estimate the user's emotions and adjust the presentation method based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible presentation method. Furthermore, if the user is relaxed, the providing unit can provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a presentation method that focuses on the main points. This makes it possible to provide the optimal presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the presentation method.

[0090] At the time of provision, the providing unit can select an appropriate provision method by analyzing the user's past provision history. For example, the providing unit can suggest an optimal provision method based on the provision methods used by the user in the past. The providing unit can also extract specific patterns from the user's past provision history and select an optimal provision method. The providing unit can also analyze the user's past provision history and improve the provision method. This makes it possible to provide an optimal provision method based on the user's past provision history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision data into a generation AI and cause the generation AI to select an optimal provision method.

[0091] The providing unit can customize the means of provision based on the user's current situation at the time of provision. The providing unit can suggest the optimal provision method based on, for example, the user's current location. The providing unit can also customize the means of provision based on the user's current situation (time of day, weather, etc.). The providing unit can also adjust the priority of provision taking into account the user's current situation. This makes it possible to provide the optimal means of provision according to the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into the generation AI and cause the generation AI to customize the means of provision.

[0092] The providing unit can improve the method of provision by reflecting user feedback at the time of provision. The providing unit can, for example, suggest an optimal method of provision based on the user's past feedback. The providing unit can also preferentially suggest a specific method of provision based on the user's feedback. The providing unit can also analyze the user's feedback and improve the method of provision. This makes it possible to provide an optimal method of provision based on the user's feedback. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the method of provision.

[0093] The providing unit can estimate the user's emotions and determine the priority of provision based on the estimated user emotions. For example, if the user needs urgent provision, the providing unit can provide information with priority. Furthermore, if the user is relaxed, the providing unit can provide information with normal priority. Furthermore, if the user is stressed, the providing unit can increase the priority of provision to respond quickly. This allows information to be provided with the optimal priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and have the generation AI determine the priority of provision.

[0094] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, the providing unit can prioritize providing nearby services and information based on the user's current location. Furthermore, if the user is traveling, the providing unit can also provide information based on travel destination information. Furthermore, if the user is at home, the providing unit can prioritize providing information about nearby services. This makes it possible to provide the optimal providing method based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to the generating AI and cause the generating AI to select the optimal providing method.

[0095] At the time of providing, the providing unit can analyze the user's social media activity and suggest a means of provision. For example, the providing unit can prioritize providing information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the user's friends on social media. This makes it possible to provide the optimal means of provision based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest a means of provision.

[0096] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The providing unit, for example, suggests an optimal delivery method based on the user's past feedback. The providing unit can also preferentially suggest a specific delivery method based on the user's feedback. The providing unit can also analyze the user's feedback and improve the delivery method. This makes it possible to provide an optimal delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, arrangement unit, and provision 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 is realized by the reception device 38 of the smart device 14 and receives a user's request in natural language. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using AI and identifies specific arrangements and guidance content. The arrangement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes arrangements and guidance based on the identified content. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the results of the arrangements and guidance to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, dispatch unit, and provision 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 is realized by the microphone 238 of the smart glasses 214 and receives a user's request in natural language. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using AI and identifies specific arrangements and guidance content. The dispatch unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes arrangements and guidance based on the identified content. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the results of the arrangements and guidance to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, dispatch unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives a user's request in natural language. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using AI and identifies specific arrangements and guidance content. The dispatch unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes arrangements and guidance based on the identified content. The provision unit is realized, for example, by the speaker 240 of the headset terminal 314 and provides the results of arrangements and guidance to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, dispatch unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's request in natural language. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using AI and identifies specific arrangements and guidance content. The dispatch unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes arrangements and guidance based on the identified content. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the results of the arrangements and guidance to the user.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] When accepting a user's request, the reception unit can analyze the user's past behavioral patterns and suggest the optimal reception method. For example, it can preferentially display services that the user has frequently used in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests that will be made during a specific time period based on the user's past behavioral patterns. This makes it possible to provide the optimal reception method based on the user's past behavioral patterns.

[0099] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user has an urgent request, analysis can be performed with priority. If the user is relaxed, analysis can be performed with normal priority. Furthermore, if the user is feeling stressed, the priority can be raised to respond quickly. This allows analysis to be performed with optimal priority according to the user's emotions.

[0100] The dispatch unit can customize the dispatch method based on the user's current situation. For example, it can propose the optimal dispatch method based on the user's current location. The dispatch unit can also customize the dispatch method based on the user's current situation (time of day, weather, etc.). Furthermore, the dispatch unit can adjust the dispatch priority taking into account the user's current situation. This makes it possible to provide the optimal dispatch method according to the user's current situation.

[0101] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible method of providing information can be provided. If the user is relaxed, a method of providing information that includes detailed information can be provided. Furthermore, if the user is in a hurry, a method of providing information that focuses on the main points can be provided. This makes it possible to provide the optimal method of providing information according to the user's emotions.

[0102] The reception unit can analyze the user's social media activity and receive related requests. For example, requests related to places where the user has checked in on social media are preferentially received. The reception unit can also analyze the content of the user's posts on social media and receive related requests. Furthermore, the reception unit can also receive related requests by referring to the activities of the user's friends on social media. This makes it possible to receive optimal requests based on the user's social media activity.

[0103] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can perform highly accurate analysis for similar requests based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and improve the analysis algorithm. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0104] The dispatch unit can estimate the user's emotions and determine the priority of dispatches based on the estimated user's emotions. For example, if the user needs urgent dispatches, dispatches can be made with priority. If the user is relaxed, dispatches can be made with normal priority. Furthermore, if the user is feeling stressed, dispatches can be made with higher priority to respond quickly. This allows dispatches to be made with optimal priority according to the user's emotions.

[0105] The providing unit can improve the method of provision by reflecting the user's past feedback. For example, the providing unit can suggest an optimal method of provision based on the user's past feedback. The providing unit can also preferentially suggest a specific method of provision based on the user's feedback. Furthermore, the providing unit can analyze the user's feedback and improve the method of provision. This makes it possible to provide an optimal method of provision based on the user's past feedback.

[0106] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user's emotions. For example, if the user has an urgent request, the request can be received with priority. If the user is relaxed, the request can be received with normal priority. Furthermore, if the user is feeling stressed, the priority can be raised to respond quickly. In this way, requests can be received with the optimal priority according to the user's emotions.

[0107] The analysis unit can apply different analysis algorithms depending on the category of the request. For example, for a request regarding a restaurant reservation, an algorithm that analyzes restaurant ratings and seat availability information can be applied. For a request regarding transportation directions, an algorithm that analyzes transportation information can be applied. Furthermore, for a request regarding medical consultation, an algorithm that analyzes medical information can be applied. This makes it possible to provide optimal analysis results according to the category of the request.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit accepts the user's request in natural language. User requests include service requests and information inquiries. For example, the user might enter, "I'd like to make a restaurant reservation for tomorrow night." This request is accepted by the reception unit. Step 2: The analysis unit uses AI to analyze the request received by the reception unit and identify the specific arrangements and information to be provided. Analysis is performed using methods such as text analysis and sentiment analysis. For example, in response to a request such as "I would like to make a restaurant reservation for tomorrow night," the specific content of "restaurant reservation" is identified. Step 3: The arrangement unit makes arrangements and guidance based on the content identified by the analysis unit. Arrangements and guidance include arranging accommodations and tourist spots, booking airline tickets and restaurants, providing transportation information, and providing medical consultations. For example, the arrangement unit makes reservations at a restaurant that meets the user's preferences based on the content "reservation of a restaurant" identified by the analysis unit. Step 4: The providing unit provides the user with the results of the arrangements and guidance made by the arrangement unit. The results are provided by means of email notification, display within the app, etc. For example, the providing unit provides the user with information about the restaurant reserved by the arrangement unit.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 a user's request in natural language; an analysis unit that analyzes the request received by the reception unit and identifies the details of specific arrangements and guidance; a dispatch unit that makes dispatches and guidance based on the content identified by the analysis unit; a provision unit that provides the user with the results of arrangements and guidance made by the arrangement unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the method of accepting requests based on the estimated user emotions 2. The system of claim 1.

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

4. The reception unit When receiving requests, filter them based on the user's current situation and interests.

2. The system of claim 1.

5. The reception unit When accepting a request, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize requests based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting requests, the system takes into account the user's geographic location information to prioritize requests that are highly relevant.

2. The system of claim 1.

8. The reception unit When receiving a request, analyze your social media activity and receive related requests.

2. The system of claim 1.

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